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Schroeder, Pascal, e Imed Kacem. "Competitive difference analysis of the cash management problem with uncertain demands". European Journal of Operational Research 283, n. 3 (giugno 2020): 1183–92. http://dx.doi.org/10.1016/j.ejor.2019.11.065.
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Song, Na, Wai-Ki Ching, Tak-Kuen Siu e Cedric Ka-Fai Yiu. "On Optimal Cash Management under a Stochastic Volatility Model". East Asian Journal on Applied Mathematics 3, n. 2 (maggio 2013): 81–92. http://dx.doi.org/10.4208/eajam.070313.220413a.
Abstract (sommario):
AbstractWe discuss a mathematical model for optimal cash management. A firm wishes to manage cash to meet demands for daily operations, and to maximize terminal wealth via bank deposits and stock investments that pay dividends and have uncertain capital gains. A Stochastic Volatility (SV) model is adopted for the capital gains rate of a stock, providing a more realistic way to describe its price dynamics. The cash management problem is formulated as a stochastic optimal control problem, and solved numerically using dynamic programming. We analyze the implications of the heteroscedasticity described by the SV model for evaluating risk, by comparing the terminal wealth arising from the SV model to that obtained from a Constant Volatility (CV) model.
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Pandey, Peeyush, Patel Jinil Ashvinbhai, Yushmita Singh, Tania Mittal, Ishank Goel, Bharat Kumar Mehta e Sayali Tapas. "Modi Mangoes: managing an uncertain future". Emerald Emerging Markets Case Studies 12, n. 4 (16 dicembre 2022): 1–18. http://dx.doi.org/10.1108/eemcs-06-2022-0208.
Abstract (sommario):
Learning outcomes The case primarily focused on a real-life problem and shows that existing operations management tools can be used to solve a complex problem. Through this case, the participants will learn the application of the factor loading method and aggregate planning. Case overview/synopsis This case revolves around the Modi Agro Pvt. Ltd, a mango procurement and distribution business established in 1994 by Mr Dhanush Modi in Mumbai, India. Mr Mahendra Modi, son of the company owner, observed that the different seasons of cultivation and varied customer demands lead to changing workforce requirements during the procurement process. In addition, the production quality, variety, available resources, procurement location and cost play a significant role in establishing a long-term relationship with the customers. This case highlights the problem faced by Mahendra in determining an appropriate location among all available options for mango procurement and the optimal workforce for each month to meet the varying customers’ demands. Complexity academic level The case can be used as teaching material for participants of the course Service Operations Management, Operations Management, Decision Analysis and Quantitative Techniques Supplementary materials Teaching notes are available for educators only. Subject code CSS 9: Operations and logistics.
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Lima, Relvas, Barbosa-Póvoa e Morales. "Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks". Processes 7, n. 8 (2 agosto 2019): 507. http://dx.doi.org/10.3390/pr7080507.
Abstract (sommario):
The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.
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Rouzpeykar, Yaser, Roya Soltani e Mohammad Ali Afashr Kazemi. "EFP-GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty". Complexity 2022 (6 luglio 2022): 1–12. http://dx.doi.org/10.1155/2022/7801188.
Abstract (sommario):
The aviation industry is one of the most widely used applications in transportation. Due to the limited capacity of aircraft, revenue management in this industry is of high significance. On the other hand, the hub location problem has been considered to facilitate the demands assignment to hubs. This paper presents an integrated p-hub location and revenue management problem under uncertain demand to maximize net revenue and minimize total cost, including hub establishment and transportation costs. A fuzzy programming model and a genetic algorithm are developed to solve the proposed model with different sizes. The mining and petroleum industry is used for case studies. Results show that the proposed algorithm can obtain a suitable solution in a reasonable amount of time.
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Martínez-Reyes, Andrés, Carlos L. Quintero-Araújo e Elyn L. Solano-Charris. "Supplying Personal Protective Equipment to Intensive Care Units during the COVID-19 Outbreak in Colombia. A Simheuristic Approach Based on the Location-Routing Problem". Sustainability 13, n. 14 (13 luglio 2021): 7822. http://dx.doi.org/10.3390/su13147822.
Abstract (sommario):
The coronavirus disease 2019, known as COVID-19, has generated an imminent necessity for personal protective equipment (PPE) that became essential for all populations and much more for health centers, clinics, hospitals, and intensive care units (ICUs). Considering this fact, one of the main issues for cities’ governments is the distribution of PPE to ICUs to ensure the protection of medical personnel and, therefore, the sustainability of the health system. Aware of this challenge, in this paper, we propose a simheuristic approach for supplying personal protective equipment to intensive care units which is based on the location-routing problem (LRP). The objective is to provide decision makers with a decision support tool that considers uncertain demands, distribution cost, and reliability in the solutions. To validate our approach, a case study in Bogotá, Colombia was analyzed. Computational results show the efficiency of the usage of alternative safety stock policies to face demand uncertainty in terms of both expected stochastic costs and reliabilities.
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Rahdar, Mohammad, Lizhi Wang, Jing Dong e Guiping Hu. "Resilient Transportation Network Design under Uncertain Link Capacity Using a Trilevel Optimization Model". Journal of Advanced Transportation 2022 (20 gennaio 2022): 1–16. http://dx.doi.org/10.1155/2022/5023518.
Abstract (sommario):
This study addresses uncertainty in a transportation network by proposing a trilevel optimization model, which improves resiliency against uncertain disruptions. The goal is to minimize the total travel time by designing a resilient transportation network under uncertain disruptions and deterministic origin-destination demands. The trilevel optimization model has three levels. The lower level determines the network flow, and the middle level assesses the network’s resiliency by identifying the worst-case scenario disruptions that could lead to maximal travel time. The upper-level takes the system perspective to expand the existing transportation network to enhance resiliency. We also propose a formulation for the network flow problem to significantly reduce the number of variables and constraints. Two algorithms have been developed to solve the trilevel model. The results of solving the highway network in Iowa show that the trilevel optimization model improves the total travel time by an average of 41%.
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Chen, Xin, e David Simchi-Levi. "A NEW APPROACH FOR THE STOCHASTIC CASH BALANCE PROBLEM WITH FIXED COSTS". Probability in the Engineering and Informational Sciences 23, n. 4 (28 agosto 2009): 545–62. http://dx.doi.org/10.1017/s0269964809000242.
Abstract (sommario):
The stochastic cash balance problem is a periodic review inventory problem faced by a firm in which the customer demands might be positive or negative. At the beginning of each time period, the firm may decide to replenish the inventory or return excess stock. Both the ordering cost and the return cost include a fixed component and a variable component. A holding or penalty cost is charged depending on whether the inventory level is positive or negative. The objective of the firm is to find an ordering and return policy so as to minimize the total expected cost over the entire planning horizon. We show how the concept of symmetric K-convexity introduced by Chen and Simchi-Levi [2,3] and the concept of (K, Q)-convexity introduced by Ye and Duenyas [13] can be used to characterize the optimal policy for this problem.
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Silmi Juman, Zeinul Abdeen M., Mahmoud Masoud, Mohammed Elhenawy, Hanif Bhuiyan, Md Mostafizur Rahman Komol e Olga Battaïa. "A new algorithm for solving uncapacitated transportation problem with interval-defined demands and suppliers capacities". Journal of Intelligent & Fuzzy Systems 41, n. 1 (11 agosto 2021): 625–37. http://dx.doi.org/10.3233/jifs-202436.
Abstract (sommario):
The uncapacitated transportation problem (UTP) deals with minimizing the transportation costs related to the delivery of a homogeneous product from multi-suppliers to multi-consumers. The application of the UTP can be extended to other areas of operations research, including inventory control, personnel assignment, signature matching, product distribution with uncertainty, multi-period production and inventory planning, employment scheduling, and cash management. Such a UTP with interval-defined demands and suppliers capacities (UTPIDS) is investigated in this paper. In UTPIDS, the demands and suppliers capacities may not be known exactly but vary within an interval due to variation in the economic conditions of the global economy. Following the variation, the minimal total cost of the transportation can also be varied within an interval and thus, the cost bounds can be obtained. Here, although the lower bound solution can be attained methodologically, the correct estimation of the worst case realization (the exact upper bound) on the minimal total transportation cost of the UTPIDS is an NP-hard problem. So, the decision-makers seek for minimizing the transportation costs and they are interested in the estimation of the worst case realization on these minimal costs for better decision making especially, for proper investment and return. In literature very few approaches are available to find this estimation of the worst case realization with some shortcomings. First, we demonstrate that the available heuristic methods fail to obtain the correct estimation of the worst case realization always. In this situation, development of a better heuristic method to find the better near optimal estimation of the worst case realization on the minimal total costs of the UTPIDS is desirable. Then this paper provides a new polynomial time algorithm that runs in O (N2) time (N, higher of the numbers of source and destination nodes) for better estimation. A comparative assessment on solutions of available benchmark instances, some randomly generated numerical example problems and a real-world application shows promising performance of the current technique. So, our new finding would definitely be benefited to practitioners, academics and decision makers who deal with such type of decision making instances.
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Mejia, Jose, Liliana Avelar-Sosa, Boris Mederos e Jorge L. García-Alcaraz. "Inventory Model with Stochastic Demand Using Single-Period Inventory Model and Gaussian Process". Processes 10, n. 4 (16 aprile 2022): 783. http://dx.doi.org/10.3390/pr10040783.
Abstract (sommario):
Proper inventory management is vital to achieving sustainability within a supply chain and is also related to a company’s cash flow through the funds represented by the inventory. Therefore, it is necessary to balance excess inventory and insufficient inventory. However, this can be difficult to achieve in the presence of stochastic demand because decisions must be made in an uncertain environment and the inventory policy bears risks associated with each decision. This study reports an extension of the single-period model for the inventory problem under uncertain demand. We proposed incorporating a Gaussian stochastic process into the model using the associated posterior distribution of the Gaussian process as a distribution for the demand. This enables the modeling of data from historical inventory demand using the Gaussian process theory, which adapts well to small datasets and provides measurements of the risks associated with the predictions made. Thus, unlike other works that assume that demand follows an autoregressive or Brownian motion model, among others, our approach enables adaptability to different complex forms of demand trends over time. We offer several numerical examples that explore aspects of the proposed approach and compare our results with those achieved using other state-of-the-art methods.
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Babaee Tirkolaee, Erfan, Iraj Mahdavi, Mir Mehdi Seyyed Esfahani e Gerhard-Wilhelm Weber. "A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management". Waste Management & Research 38, n. 2 (13 agosto 2019): 156–72. http://dx.doi.org/10.1177/0734242x19865782.
Abstract (sommario):
Nowadays, urban solid waste management is one of the most crucial activities in municipalities and their affiliated organizations. It includes the processes of collection, transportation and disposal. These major operations require a large amount of resources and investments, which will always be subject to limitations. In this paper, a chance-constrained programming model based on fuzzy credibility theory is proposed for the multi-trip capacitated arc routing problem to cope with the uncertain nature of waste amount generated in urban areas with the aim of total cost minimization. To deal with the complexity of the problem and solve it efficiently, a hybrid augmented ant colony optimization algorithm is developed based on an improved max–min ant system with an innovative probability function and a simulated annealing algorithm. The performance of hybrid augmented ant colony optimization is enhanced by using the Taguchi parameter design method to adjust the parameters’ values optimally. The overall efficiency of the algorithm is evaluated against other similar algorithms using well-known benchmarks. Finally, the applicability of the suggested methodology is tested on a real case study with a sensitivity analysis to evolve the managerial insights and decision aids.
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Zhou, Yancong, e Junqing Sun. "Inventory Decisions in a Product-Updated System with Component Substitution and Product Substitution". Discrete Dynamics in Nature and Society 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/136074.
Abstract (sommario):
Substitution behaviors happen frequently when demands are uncertain in a production inventory system, and it has attracted enough attention from firms. Related researches can be clearly classified into firm-driven substitution and customer-driven substitution. However, if production inventory is stock-out when a firm updates its product, the firm may use a new generation product to satisfy the customer’s demand of old generation product or use updated component to substitute old component to satisfy production demand. Obviously, two cases of substitution exist simultaneously in the product-updated system when an emergent shortage happens. In this paper, we consider a component order problem with component substitution and product substitution simultaneously in a product-updated system, where the case of firm-driven substitution or customer-driven substitution can be reached by setting different values for two system parameters. Firstly, we formulate the problem into a two-stage dynamic programming. Secondly, we give the optimal decisions about assembled quantities of different types of products. Next, we prove that the expected profit function is jointly concave in order quantities and decrease the feasible domain by determining some bounds for decision variables. Finally, some management insights about component substitution and product substitution are investigated by theoretical analysis method.
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Chovniuk, Yurii, Aleksey Priymachenko, Luydmula Zolotar, Olena Mischenko e Oleksandra Cherednichenko. "MODELING IN INVENTORY MANAGEMENT PROBLEMS UNDER UNCERTAINTY AND TAKING INTO ACCOUNT (INFLATION) RISKS". Spatial development, n. 7 (23 febbraio 2024): 506–31. http://dx.doi.org/10.32347/2786-7269.2024.7.506-531.
Abstract (sommario):
A model that can be used in the problems of inventory management under uncertainty and taking into account various types of risks, in particular, inflation risks, is presented and substantiated. The strategy of stock management under uncertain (stochastic) demand usually requires the creation of a certain reserve of a predetermined volume, and then the next deliveries of stocks are made. If at a certain point in time the total stock is reduced to the size of the reserve, then an urgent order for delivery of a new batch is made. If the fulfillment of the request requires a certain time (this process is not instantaneous!), then the request for its replenishment is submitted when the stock decreases to a level exceeding the predetermined amount of the reserve. The study provides one of the simplest ways to solve the reserve problem, namely, applying the principle of guaranteed result, i.e. electing a large enough reserve that guarantees minimum risk, namely, compensation of any random deviations, which requires large costs for their storage and the like. This also leads to the so-called opportunity risk as large reserves are associated with the diversion of significant funds. In this connection, additional hypotheses are introduced in the paper, and the concept of acceptable risk - the probability that the need for reserves will not exceed the available reserve - is used as the basis for calculating the required reserve. The concept of risk coefficient is introduced, which expresses the probability that the need for reserves will be unsatisfactory due to the insufficiency of the reserve and will exceed its volume. The value of the risk coefficient can be equal to 5% or 1%. The paper uses a modified formula for calculating the nominal rate of interest with inflation interest rate risk, which includes the following components: 1) real safe rate of interest; 2) inflation premium; 3) inflation risk premium; 4) investment project risk premium (reserve creation is a kind of investment); 5) synergistic premium for investment project and inflation risk; 6) synergistic premium for investment project and inflation risk; 7) liquidity premium (in fact, it is an assessment of liquidity risk). In the study the volumes of raw materials (components) reserve are determined, which leads to the reduction of risk degree; within the framework of M. Miller and D. Orr's model the volumes of cash reserves are determined, where inflation risks are taken into account in the value of missed opportunities (which is related to the content of cash reserves balance); in the task of reserves management under uncertainty and the risk caused by it, the total costs of maintaining the reserve per unit of time are minimized and the value of the optimal reserve together with the reserve is determined (within the framework of the modified model of V. Miller and D. Orr). The paper provides a rather simple method of accounting for possible risks arising in the creation of stocks (raw materials, cash, etc.), solved the problem of selecting a specific rational value of the risk coefficient (based on expert procedures and utility theory), which allows to reflect and take into account the attitude of decision-making subjects to risk.
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Binkley, Clark S. "Preserving nature through intensive plantation forestry: The case for forestland allocation with illustrations from British Columbia". Forestry Chronicle 73, n. 5 (1 ottobre 1997): 553–59. http://dx.doi.org/10.5558/tfc73553-5.
Abstract (sommario):
Historically British Columbia's forests were managed under the implicit assumption that virtually the whole forested land base would, one day, be available for timber production. The BC Forest Service and licencees incorporate non-timber values into timber production plans through a process of "integrated resource management" which attempts to consider wildlife, riparian habitat, recreation, water flows, grazing and other forest uses in each decision about each hectare where logging is to occur. Under this extensive form of management, silvicultural investments are low. This policy has clearly failed either to satisfy legitimate demands from the environmental community or to produce the predictably high levels of timber harvest needed to sustain the forest products industry and industry-dependent communities. The core problem is that, despite a vast forest estate in British Columbia, land has become scarce. It is therefore logical to substitute capital, labour and knowledge for land in forest production processes. Such a policy could lead to substantial higher, sustainable timber harvests as well as a system of parks that covers more than half the Province. Implementing such a policy requires a change in forest management approach to zone the landscape and manage each zone intensively for a specific purpose. For the bulk of commercial timber production, intensively managed plantations appear to represent the best technological option. New directions in British Columbia's forest policy — land-use zoning, a new forest practice code, and dedicated capital for silvicultural investments—generally move towards this objective, but implementation remains uncertain. Major impediments include dysfunctional forest tenure arrangements and a comparatively poor information base. Key words: forest policy, British Columbia, intensive plantation forestry
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Heide, Mats, e Charlotte Simonsson. "What was that all about? On internal crisis communication and communicative coworkership during a pandemic". Journal of Communication Management 25, n. 3 (7 giugno 2021): 256–75. http://dx.doi.org/10.1108/jcom-09-2020-0105.
Abstract (sommario):
PurposeThe aim of this paper is to contribute with increased knowledge of the complex role of internal communication during a crisis like the COVID-19 pandemic. More specifically, the authors want to address the following research questions. How can the overall approach to internal crisis communication during the pandemic be interpreted, and what view of internal crisis communication does this approach reflect? What has been characteristic of the leadership communication during the pandemic? What do coworkers think of their communication role and how well does the internal communication support that role?Design/methodology/approachThis article is based on a case study of an authority with 1,000 employees. The empirical material consists of both documents and interviews. The analyzed documents include steering documents, e-mails to managers from the support function and newsletters from the top manager. The 17 interviews comprise managers, coworkers and communication managers. All interviews were recorded and the authors have conducted verbatim transcriptions.FindingsThe pandemic is an example of a wicked problem that involves a lot of ambiguity. Often organizations try to handle wicked problems by trying to control it through traditional management skills and practices. A pandemic demands a leadership, culture and communicative approach that highlights the importance of coworkers. In the studied organization the authors found knowledge and rhetoric about the value of coworkers and communicative coworkership. However, top management does not encourage, support and award practices that are in line with the espoused culture. The key to success is top managers that walk the talk and act as role models.Practical implicationsCrisis managers and crisis communicators need to focus more on improvisation, flexibility, listening and how to approach and make sense of the uncertain. In general, there is a tendency to rely too much on simple tools and to oversimplify complexity. Complex crises such as the pandemic raise new demands on leadership. Effective crisis leadership in a complex crisis seems to be much more democratic and collaborative than often assumed. If coworkers are expected to act as ambassadors or organizational representatives, they also need to be given better support for that role.Originality/valueThis article highlights the importance of closing the gap between espoused and enacted culture in order to change from a managerialistic internal crisis communication to a process internal crisis communication approach.
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Schroeder, Pascal, e Imed Kacem. "Optimal cash management with uncertain, interrelated and bounded demands". Computers & Industrial Engineering 133 (luglio 2019): 195–206. http://dx.doi.org/10.1016/j.cie.2019.04.052.
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Chaerani, Diah, Athaya Zahrani Irmansyah, Tomy Perdana e Nurul Gusriani. "Contribution of robust optimization on handling agricultural processed products supply chain problem during Covid-19 pandemic". Uncertain Supply Chain Management 10, n. 1 (2022): 239–54. http://dx.doi.org/10.5267/j.uscm.2021.9.004.
Abstract (sommario):
This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice.
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Liang, Nai Wen. "Inventory Management with Uncertain Demands Based on Probability Methods". Applied Mechanics and Materials 121-126 (ottobre 2011): 2064–66. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2064.
Abstract (sommario):
The inventory management problem with uncertain demands is considered in this paper. the possibility theory and credibility theory are employed to solve the problem regarding uncertain demands, and different order strategies and storage strategies according to the different customer’s demand are designed for rational inventory management. Finally, a rule is designed for choosing the storage strategy.
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Mou, Deyi, e Xiaoxin Wang. "Uncertain Programming for Network Revenue Management". Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/187275.
Abstract (sommario):
The mathematical model for airline network seat inventory control problem is usually investigated to maximize the total revenue under some constraints such as capacities and demands. This paper presents a chance-constrained programming model based on the uncertainty theory for network revenue management, in which the fares and the demands are both uncertain variables rather than random variables. The uncertain programming model can be transformed into a deterministic form by taking expected value on objective function and confidence level on the constraint functions. Based on the strategy of nested booking limits, a solution method of booking control is developed to solve the problem. Finally, this paper gives a numerical example to show that the method is practical and efficient.
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Guo, Zhaozhuang, Shengnan Tian e Yankui Liu. "A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions". Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/2159281.
Abstract (sommario):
In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities.
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Zhao, Jiao, Tao Wang e Thibaud Monteiro. "A Bi-Objective Home Health Care Routing and Scheduling Problem under Uncertainty". International Journal of Environmental Research and Public Health 21, n. 3 (21 marzo 2024): 377. http://dx.doi.org/10.3390/ijerph21030377.
Abstract (sommario):
Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations.
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Azizi, Vahid, e Guiping Hu. "A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem". Sustainability 13, n. 24 (9 dicembre 2021): 13596. http://dx.doi.org/10.3390/su132413596.
Abstract (sommario):
Reverse logistics planning plays a crucial role in supply chain management. Stochasticity in different parameters along with time horizon can be a challenge in solving reverse logistics problems. This paper proposes a multi-stage, multi-period reverse logistics with lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, and return quality. Moment matching method was adopted to generate a discrete set of scenarios to represent the original continuous distribution of stochastic parameters. Fast forward selection algorithm was employed to select the most representative scenarios and facilitate computational tractability. A case study was conducted and optimal solution of the recursive problem obtained by solving extensive form. Sensitivity analysis was implemented on different elements of stochastic solution. Results sow that solution of recursive problem (RP) outperforms the solution obtained from the problem with expected values of uncertain parameters (EEV).
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Lin, Sichen. "Cash Operation Risk Management of Mount Putuo Commercial Bank in Zhoushan City". Finance and Market 5, n. 1 (5 gennaio 2020): 5. http://dx.doi.org/10.18686/fm.v5i1.1607.
Abstract (sommario):
<p>Due to the relatively inconvenient traffic conditions on Mount Putuo Island in Zhoushan City, commercial banks faced the problems of long cash escort routes, potential risks in social management and many uncertain factors after the banks setting up their institutions on the island. The island is relatively independent and rich in tourism resources. With the construction of the comprehensive bonded zone, Putuo's economic development has been continually developing, and the amount of cash withdrawal has increased significantly. Therefore, Mount Putuo Island Commercial Banking Institutions were selected to conduct a field survey. This article analyzes the specific situation of internal cash receipts and payments, and cash transfers with islands outside the island, and the major risks faced by various financial institutions operating on the island, and analyze how to solve the problem in combination with the existing environmental situation in Zhoushan City method.</p>
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Ji, Ying, Jianhui Wang, Jiacan Xu, Xiaoke Fang e Huaguang Zhang. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning". Energies 12, n. 12 (15 giugno 2019): 2291. http://dx.doi.org/10.3390/en12122291.
Abstract (sommario):
Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.
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Zhang, Qiao, e Gang Li. "A Game Theory Energy Management Strategy for a Fuel Cell/Battery Hybrid Energy Storage System". Mathematical Problems in Engineering 2019 (9 gennaio 2019): 1–12. http://dx.doi.org/10.1155/2019/7860214.
Abstract (sommario):
This paper introduces a game theory approach to implement power flow distribution mission for a fuel cell/battery hybrid system considering uncertain power information. To fully describe the vying interaction relationship between the fuel cell and the battery, we design the power distribution problem as a noncooperative game problem, in which the fuel cell and the battery are deemed to be two interactional players, and each one chooses proper amount of power supply to maximize its own optimization function relying on the other chosen. Different from all previous research work in the published papers, the power demand information of the adopted driving cycle is assumed to be absolutely known. In this paper, we discuss the case that when the power demand is uncertain how the players act and the Nash Equilibrium can be effectively achieved. Three original contributions are made. First, we develop the utility function for each player taking into account the uncertain behavior of the power demand due to inaccurate prediction of driving cycle. Second, an iterative algorithm with a fuzzy logical controller for correction is proposed to reduce the influence of uncertain power demand information on the decisions of the players. Finally, the effectiveness is validated by a comparison simulation test.
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Soshko, Oksana, Vilmars Vjakse e Yuri Merkuryev. "Modelling Inventory Management System at Distribution Company: Case Study". Scientific Journal of Riga Technical University. Computer Sciences 42, n. 1 (1 gennaio 2010): 87–93. http://dx.doi.org/10.2478/v10143-010-0047-1.
Abstract (sommario):
Modelling Inventory Management System at Distribution Company: Case Study The paper presents a case study on improving inventory management at the distribution company which operates in Latvia. The case study is focused on application of different modelling approaches in inventory management under uncertain demand, namely inventory models, simulation models and optimization model. The functionality of each model as well as its benefits for the current problem is discussed in the end of the paper.
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Allahyari, Somayeh, Saeed Yaghoubi e Tom Van Woensel. "The secure time-dependent vehicle routing problem with uncertain demands". Computers & Operations Research 131 (luglio 2021): 105253. http://dx.doi.org/10.1016/j.cor.2021.105253.
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Zhang, Bin, Li Sun, Mengyao Yang, Kin-Keung Lai e Bhagwat Ram. "A Robust Optimization Approach for Smart Energy Market Revenue Management". Energies 16, n. 19 (9 ottobre 2023): 7000. http://dx.doi.org/10.3390/en16197000.
Abstract (sommario):
We propose a network optimization model for smart energy market management in the context of an uncertain environment. The network optimization considers the stochastic programming approach to capture the randomness of the unknown demands. We utilize the particle swarm optimization technique in the proposed model to solve the proposed optimization problem. The present research is based on the inclusion of stochastic demands and uncertain energy prices. Optimizing produced energy is crucial for efficient usage and meeting the targets. The proposed model also focuses on addressing sustainability concerns by minimizing energy consumption in the scheduling process. An improved particle swarm optimization technique is implemented for energy-efficient production. Parameters such as number of particles, iterations, and energy usage specification are customized. A fitness function is taken that considers both completion time and energy consumption. The optimal of energy consumption is also visualized. The decision makers employ risk aversion in the objective function of the optimization problem to measure the risk deviation of the expected energy management.
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Baghbani, Bijan. "A Mixed Integer Programming Optimization of Blood Plasma Supply Chain in the Uncertainty Conditions during COVID-19: A Real Case in Iran". Discrete Dynamics in Nature and Society 2022 (31 agosto 2022): 1–9. http://dx.doi.org/10.1155/2022/3783119.
Abstract (sommario):
Blood and its products, like plasma, are among the most sensitive products for the sake of transportation and storage. Special storage conditions, short shelf life, and lack of particular demand for blood products are among the most significant challenges to managing it. In this respect, it is necessary to implement the problem of supply chain network design in uncertain conditions to find a proper solution for the management of blood products. In this study, a multilevel supply chain is designed to supply plasma in the COVID-19 pandemic. First, the blood is sent to blood donation centers and then to the laboratory. Moreover, after that, it is sent to hospitals. To optimize the transfer rate at each level of the supply chain, a mathematical model is proposed to reduce total costs. Also, the fuzzy programming approach is used to deal with uncertainty in the parameters of the mathematical model. The results of model optimization show that this mathematical model has the required efficiency in finding optimal solutions for the distribution of blood products. According to the obtained results, value objective function in certain and uncertain values is determined. According to the results, the objective certain value is lower than the uncertain value. Uncertain value calculated is of three dimensions. According to this, the categorized objective value increased when the dimension is equal to 0.5. Finally, it shows that when demand increases, more blood and plasma need to be collected to meet the demand, which increases operating and health testing costs and ultimately increases total system costs.
30
Liu, Wei, Youfa Sun e Xu Chen. "Mean-field formulation for mean-variance asset-liability management with cash flow under an uncertain exit time". Open Mathematics 20, n. 1 (1 gennaio 2022): 24–37. http://dx.doi.org/10.1515/math-2022-0007.
Abstract (sommario):
Abstract The asset-liability management problem with cash flow under an uncertain exit time has been investigated in this article, which is based on the fundamental framework of the mean-variance model in the multi-period version. The liability and random cash flow will affect asset optimization, while the investor may be forced to withdraw from investments with a random probability at each period in our model. The closed-form expressions for the mean-variance optimal portfolio selection and its corresponding efficient frontier are obtained by employing the mean-field formulation and dynamic programming approach. Moreover, some numerical examples are provided to illustrate the validity and accuracy of the theoretical results.
31
Liang, Bian, Dapeng Yang, Xinghong Qin e Teresa Tinta. "A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment". International Journal of Environmental Research and Public Health 16, n. 20 (19 ottobre 2019): 4007. http://dx.doi.org/10.3390/ijerph16204007.
Abstract (sommario):
Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.
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Qin, Yichen, Hoi-Lam Ma, Felix T. S. Chan e Waqar Ahmed Khan. "A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider". Industrial Management & Data Systems 120, n. 9 (30 luglio 2020): 1635–57. http://dx.doi.org/10.1108/imds-03-2020-0131.
Abstract (sommario):
PurposeThis paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.Design/methodology/approachA stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.FindingsCompared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.Research limitations/implicationsThe results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.Practical implicationsExtending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.Originality/valueA novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.
33
Huo, Jia-Zhen, Yan-Ting Hou, Feng Chu e Jun-Kai He. "A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem". Discrete Dynamics in Nature and Society 2019 (12 marzo 2019): 1–13. http://dx.doi.org/10.1155/2019/8651728.
Abstract (sommario):
This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.
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Makkar, Sandhya. "Two Stage Supply Chain Optimization for Perishable Products Under Fuzzy Environment". International Journal of Risk and Contingency Management 8, n. 3 (luglio 2019): 31–48. http://dx.doi.org/10.4018/ijrcm.2019070103.
Abstract (sommario):
In the changing market scenario, supply chain management is getting phenomenal importance amongst researchers. Studies on supply chain management have emphasized the vitality of a long-term strategic relationship between the supplier, distributor and retailer. In this article, the authors have studied a two-stage supply chain coordination problem under uncertain costs and demand information when integrated procurement and distribution decisions of supply chain has to be employed. The model incorporates a single supplier transporting its products to multiple destinations of a retailer. This process becomes tedious, as when items have an inventory carrying cost incurred due to perishable nature of products. Different discount policies are offered to procure and transport goods from the one stage to other stage. Fuzzy set theory is applied to estimate the uncertainty associated with the input parameters and triangular fuzzy numbers are used to analyze the model. A case is presented to validate the procedure.
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Liu, Peide, Ayad Hendalianpour, Jafar Razmi e Mohamad Sadegh Sangari. "A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand". Complex & Intelligent Systems 7, n. 3 (16 gennaio 2021): 1349–65. http://dx.doi.org/10.1007/s40747-020-00264-y.
Abstract (sommario):
AbstractSupply and distribution management of blood products is a challenging task due to their short lifespan. The problem is even more sophisticated considering uncertain demand for these products. This paper addresses integrated inventory-routing of blood in a supply chain network consisting of a single supplier and a group of blood centers. Transshipment among blood centers is allowed to decrease the cost of excess inventory and shortage of goods. A mathematical model is developed that decides on the optimal quantity of supplied blood, delivery plan, inventory level, and quantity of products transshipped between blood centers with the objective of minimizing total costs. In addition, a robust optimization approach is adopted to deal with uncertainty in demand. Since the proposed model is NP-hard, a heuristic solution algorithm is developed that improves solution quality by determining the most efficient change in vehicle routes in each search stage. The efficiency of the proposed algorithm is examined in a set of numerical experiments and using data from a real case of supply and distribution management of blood platelets. The results indicated that allowing transshipment reduces the need for supply capacity at the supplier, product shortage, inventory level, and the total cost.
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Muneeb, Syed Mohd, Mohammad Asim Nomani, Malek Masmoudi e Ahmad Yusuf Adhami. "A bi-level decision-making approach for the vendor selection problem with random supply and demand". Management Decision 58, n. 6 (15 aprile 2019): 1164–89. http://dx.doi.org/10.1108/md-10-2017-1017.
Abstract (sommario):
Purpose Supplier selection problem is the key process in decision making of supply chain management. An effective selection of vendors is heavily responsible for the success of any organization. Vendor selection problem (VSP) reflects a more practical view when the decision makers involved in the problem are present on different levels. Moreover, vendor selection consists of various random parameters to be dealt with in real life. The purpose of this paper is to present a decentralized bi-level VSP where demand and supply are normal random variables and objectives are fuzzy in nature. Decision makers are present at two levels and are called as leader and follower. As the next purpose, this paper extends and presents a solution approach for fuzzy bi-level multi-objective decision-making model with stochastic constraints. Different scenarios have been developed within a real-life case study based on different sets of controlling factors under the control of leader. Design/methodology/approach This study uses chance-constrained programming and fuzzy set theory to generate the results. Stochastic constraints are converted into deterministic constraints using chance-constrained programming. Decision variables in the bi-level VSP are partitioned between the two levels and considered as controlling factors. Membership functions based on fuzzy set theory are created for the goals and controlling factors and are used to obtain the overall satisfactory solutions. The model is tested on a real-life case study of a textile industry and different scenarios are constructed based on the choice of leader’s controlling factors. Findings Results showed that the approach is quite helpful as it generates efficient results producing a good level of satisfaction for the decision makers of both the levels. Results showed that on choosing the vendors that are associated with worst values in terms of associated costs, vendor ratings and quota flexibilities as controlling factors by the leaders, the level of satisfaction achieved is highest. The level of satisfaction of solution is lowest for the scenario when the leader chooses to control the decision variables associated with vendors that are profiled with minimum vendor ratings. Results also showed that higher availability of materials and budget with vendors proved helpful in obtaining quota allocations. Different scenarios generate different results along with different values of satisfaction degrees and objective values which shows the flexible feature of the approach based on leader’s choice of controlling factors. Numerical results showed that the leader’s control can be effectively incorporated maintaining satisfaction levels of the followers under various scenarios or conditions. Research limitations/implications The paper makes a certain contribution toward the study of vendor selection existing in a hierarchical manner under uncertain environment. A wide set of data of different factors is needed which can be seen as a limitation when the available time is short for the supplier selection process. Practical implications VSP which is generally adopted by most of the large organizations is characterized with hierarchical decision making. Moreover, dealing with the real-life concern, the data available for some of the parameters are not complete, representing an uncertainty of parameters. This study is quite helpful for decentralized VSP under uncertain environment to reduce the costs, improve profit margins and to create long-term relationships with selected vendors. The proposed model also provides an avenue to explore the decision making when the leader has control over some of the decision variables. Originality/value Reviewing the literature available, this is the first attempt to present a multi-objective VSP where the decision makers are at hierarchical levels considering uncertain parameters such as demand and supply as per the best knowledge of authors. This research further provides an approach to construct scenarios or different cases based on the choice of leader’s choice of controlling factors.
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Raskin, Lev, Oksana Sira e Viacheslav Karpenko. "TRANSPORTATION MANAGEMENT IN A DISTRIBUTED LOGISTIC CONSUMPTION SYSTEM UNDER UNCERTAINTY CONDITIONS". EUREKA: Physics and Engineering 4 (31 luglio 2019): 82–90. http://dx.doi.org/10.21303/2461-4262.2019.00936.
Abstract (sommario):
The problem of supply management in the supplier-to-consumer logistics transport system has been formed and solved. The novelty of the formulation of the problem consists in the integrated accounting of costs in the logistic system, which takes into account at the same time the cost of transporting products from suppliers to consumers, as well as the costs for each of the consumers to store the unsold product and losses due to possible shortages. The resulting optimization problem is no longer a standard linear programming problem. In addition, the work assumes that the solution of the problem should be sought taking into account the fact that the initial data of the problem are not deterministic. The analysis of traditional methods of describing the uncertainty of the source data. It is concluded that, given the rapidly changing conditions for the implementation of the delivery process in a distributed supplier-to-consumer system, it is advisable to move from a theoretical probability representation of the source data to their description in terms of fuzzy mathematics. At the same time, in particular, the fuzzy values of the demand for the delivered product for each consumer are determined by their membership functions. Distribution of supplies in the system is described by solving a mathematical programming problem with a nonlinear objective function and a set of linear constraints of the transport type. In forming the criterion, a technology is used to transform the membership functions of fuzzy parameters of the problem to its theoretical probabilistic counterparts – density distribution of demand values. The task is reduced to finding for each consumer the value of the ordered product, minimizing the average total cost of storing the unrealized product and losses from the deficit. The initial problem is reduced to solving a set of integral equations solved, in general, numerically. It is shown that in particular, important for practice, particular cases, this solution is achieved analytically. The paper states the insufficient adequacy of the traditionally used mathematical models for describing fuzzy parameters of the problem, in particular, the demand. Statistical processing of real data on demand shows that the parameters of the membership functions of the corresponding fuzzy numbers are themselves fuzzy numbers. Acceptable mathematical models of the corresponding fuzzy numbers are formulated in terms of bifuzzy mathematics. The relations describing the membership functions of the bifuzzy numbers are given. A formula is obtained for calculating the total losses to storage and from the deficit, taking into account the bifuzzy of demand. In this case, the initial task is reduced to finding the distribution of supplies, at which the maximum value of the total losses does not exceed the permissible value.
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Dwijendra, Ngakan Ketut Acwin, Wongchai Anupong, Ahmed Majed Althahabi, Sabah Auda Abdulameer, Waleed Khalid Al-Azzawi, Mustafa Musa Jaber, Musaddak Maher Abdul Zahra e Zuhair I. Al Mashhadani. "Optimal Dispatch of the Energy Demand in Electrical Distribution Grid with Reserve Scheduling". Environmental and Climate Technologies 27, n. 1 (1 gennaio 2023): 80–91. http://dx.doi.org/10.2478/rtuect-2023-0007.
Abstract (sommario):
Abstract The operation of the electrical systems is a major problem for electrical companies’ subject to uncertainties threatening. In this study, the optimal management of the energy demand in the electrical distribution grid is done by interval optimization approach under electrical price uncertainty. The management of the energy demand is implemented via incentive-based modelling of the demand response programs (DRPs). The incentive-based modelling as reserve, and based on bid price for reduction of the electrical demand at peak hours is proposed. The interval optimization approach is used for the minimization of the electrical price uncertainty effects. The main objective in the proposed approach is minimizing operation cost; epsilon-constraint method is utilized to solve the problem. Finally, an electrical distribution grid has been used at various case studies to numerical simulation results and positive effects of the proposed modelling under uncertainties.
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Testuri, Carlos E., Héctor Cancela e Víctor M. Albornoz. "A multistage stochastic lot-sizing problem with cancellation and postponement under uncertain demands". RAIRO - Operations Research 55, n. 2 (marzo 2021): 861–72. http://dx.doi.org/10.1051/ro/2021042.
Abstract (sommario):
A multistage stochastic capacitated discrete procurement problem with lead times, cancellation and postponement is addressed. The problem determines the procurement of a product under uncertain demand at minimal expected cost during a time horizon. The supply of the product is made through the purchase of optional distinguishable orders of fixed size with delivery time. Due to the unveiling of uncertainty over time it is possible to make cancellation and postponement corrective decisions on order procurement. These decisions involve costs and times of implementation. A model of the problem is formulated as an extension of a discrete capacitated lot-sizing problem under uncertain demand and lead times through a multi-stage stochastic mixed-integer linear optimization approach. Valid inequalities are generated, based on a conventional inequalities approach, to tighten the model formulation. Experiments are performed for several problem instances with different uncertainty information structure. Their results allow to conclude that the incorporation of a subset of the generated inequalities favor the model solution.
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Abd ul Muqeet, Hafiz, Hafiz Mudassir Munir, Aftab Ahmad, Intisar Ali Sajjad, Guang-Jun Jiang e Hong-Xia Chen. "Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes". Mathematical Problems in Engineering 2021 (27 maggio 2021): 1–18. http://dx.doi.org/10.1155/2021/5569701.
Abstract (sommario):
Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.
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Weintraub, Andrés, e Alejandra Abramovich. "Analysis of Uncertainty of Future Timber Yields in Forest Management". Forest Science 41, n. 2 (1 maggio 1995): 217–34. http://dx.doi.org/10.1093/forestscience/41.2.217.
Abstract (sommario):
Abstract We consider the effect that the uncertainty of future yields has on forest management. One way of modeling this problem is through chance constrained linear programming, where constraints are represented as probabilistic statements. Under normality conditions, an equivalent deterministic nonlinear program can be solved in an efficient way using a cutting plane algorithm, which takes advantage of the characteristics of the problem. To deal with uncertainty, we propose a forest planning approach, based on chance constrained linear programming. We also analyze the importance of including the consideration of uncertainty in the planning process. For this purpose, we simulate two scenarios, a deterministic one and another where uncertainty is included in the models. Results of a test case show that not considering uncertainty in the models when production demand constraints have small slack can lead to management situations with infeasible solutions. For. Sci. 41(2):217-234.
42
Ji, Ying, Shaojian Qu e Zhensheng Yu. "A New Method for Solving Multiobjective Bilevel Programs". Discrete Dynamics in Nature and Society 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/2870420.
Abstract (sommario):
We study a class of multiobjective bilevel programs with the weights of objectives being uncertain and assumed to belong to convex and compact set. To the best of our knowledge, there is no study about this class of problems. We use a worst-case weighted approach to solve this class of problems. Our “worst-case weighted multiobjective bilevel programs” model supposes that each player (leader or follower) has a set of weights to their objectives and wishes to minimize their maximum weighted sum objective where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto optimum concept, which we call “robust-weighted Pareto optimum”; for the worst-case weighted multiobjective optimization with the weight set of each player given as a polytope, we show that a robust-weighted Pareto optimum can be obtained by solving mathematical programing with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multiobjective optimization to a supply chain risk management under demand uncertainty. By the comparison with the existing weighted approach, we show that our method is more robust and can be more efficiently applied to real-world problems.
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Shrivastava, Himanshu, Andreas T. Ernst e Mohan Krishnamoorthy. "Distribution and Inventory Planning in a Supply Chain Under Transportation Route Disruptions and Uncertain Demands". International Journal of Information Systems and Supply Chain Management 12, n. 3 (luglio 2019): 47–71. http://dx.doi.org/10.4018/ijisscm.2019070103.
Abstract (sommario):
This article considers transportation disruptions and its detrimental impact on the quality of the enroute shipment. The authors consider a supply chain system of a short life cycle product that has a capacitated supplier, a retailer and multiple routes of transportation under different disruption risks, uncertain cost of transportation, and uncertain demands. The authors investigate a hybrid problem in which the firm needs to develop a suitable distribution strategy under disruption risks along with an optimal checking policy when faced with the supply of varying quantities of damaged items. The authors formulate a non-linear mathematical model in which the overall objective is to maximise the expected profit and to help the firm in decision making under uncertain environments. Lastly, a statistical study is carried out to perform uncertainty analysis.
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Montoya, Oscar Danilo, Federico Martin Serra e Walter Gil-González. "A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants". Energies 16, n. 18 (7 settembre 2023): 6470. http://dx.doi.org/10.3390/en16186470.
Abstract (sommario):
This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.
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KUMAR, MANEESH, e Barjeev Tyagi. "Optimal Energy Management and Sizing of a Community Smart Microgrid Using Demand Side Management with Load Uncertainty". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, n. 2 (23 aprile 2021): 186–97. http://dx.doi.org/10.37936/ecti-cit.2021152.240491.
Abstract (sommario):
This paper presents an optimal energy management and sizing of a smart community microgrid (MG) with the uncertainty in load demand. An isolated small scale microgrid is considered with no access to the main supply grid. For simplicity, a small community of 15 houses located in a remote area is considered, and the loads are divided into controllable and uncontrollable categories. Demand side management (DSM) is being utilized to produce a feasible alteration to the controllable part of the load. The Overall problem is formulated to fix the optimal size of distributed generations (DGs) used in the MG by using a heuristic approach to minimize the net cost-based optimization problem. This cost includes initial capital costs, operation, and maintenance costs, and other running costs associated with MG. The optimization is completed in two parts. The first part of optimization is done without DSM implementation, and second part optimization is done on the modified system peak load after DSM implementation. Quantitative results on a numerical case study give an optimal number of distributed generation (DGs), their corresponding optimal ratings, optimal cost value, reduction in carbon footprint, and annual cost savings in the form of CO2 emission tax.
46
Zhang, Rui, e Rex Kincaid. "Robust Optimization Model for Runway Configurations Management". International Journal of Operations Research and Information Systems 5, n. 3 (luglio 2014): 1–26. http://dx.doi.org/10.4018/ijoris.2014070101.
Abstract (sommario):
The Runway Configuration Management problem governs what combinations of airport runways are in use at a given time for an airport or a collection of airports. Runway configurations (groupings of runways), operate under Runway Configuration Capacity Envelopes (RCCEs) which limit arrival and departure capacities. The RCCE identifies unique capacity constraints based on which runways are used for arrivals, departures, and their direction of travel. When switching between RCCEs, due to a change in weather conditions or a change in the demand pattern, a decrement in arrival and departure capacities is incurred during the transition. This paper reports computational experience with two distinct models—a robust optimization model that addresses uncertainty in the arrival demand, and a previously studied model that does not include uncertainty in any of the parameters. Test case scenarios are based on data from the John F. Kennedy international airport in New York.
47
Xue, Weili, Xiaolin Xu e Ruxian Wang. "Combined Sales Effort and Inventory Control under Demand Uncertainty". Discrete Dynamics in Nature and Society 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/984513.
Abstract (sommario):
We study the joint inventory and sales effort management problems of a retailer in a broad context and investigate the optimal policies for a single item, periodic-review system. In each period, the demand is uncertain depending on the sales effort level exerted by the retailer, which incurs an associated cost. The retailer’s objective is to find a joint optimal inventory replenishment and sales effort policy to maximize the discounted profit over a finite horizon. We first consider a basic setting with zero setup cost and no batch ordering, under which the base stock list sales effort policy is optimal. Two extensions are then investigated: (1) the case with nonzero setup cost, under which we show that(s,S,e)policy is optimal; and (2) the case with batch ordering, under which we prove the optimality of the(r,Nq,e)policy. Finally, we conduct numerical studies to provide additional managerial insights.
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Makropoulos, C. K., e D. Butler. "Spatial decisions under uncertainty: fuzzy inference in urban water management". Journal of Hydroinformatics 6, n. 1 (1 gennaio 2004): 3–18. http://dx.doi.org/10.2166/hydro.2004.0002.
Abstract (sommario):
The endogenous complexity and spatial nature of the problems encountered in the urban water management environment present decision-makers with three major problems: (a) in the urban environment, every decision is site-specific, almost on a case-by-case basis, (b) the decision-maker must access, simultaneously, a large amount of information, increasing with rising spatial resolution and (c) the information to be evaluated is heterogeneous, including engineering, economical and social characteristics and constraints. The first two problems indicate that urban water management is an ideal field to develop and use spatial decision support systems (SDSS), while the latter promotes the use of fuzzy inference systems as a key mathematical framework. This research discusses the nature of uncertainty in environmental management in general and urban water management in particular, argues that fuzzy, rule-based, inference systems can be an invaluable tool for uncertainty quantification and presents the relevant elements of a prototype SDSS for urban water management. The examples presented in this paper are based on an application of the SDSS in water demand management.
49
Fu, Dianzheng, Tianji Yang, Yize Huang e Yiming Tong. "Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties". Energies 15, n. 15 (26 luglio 2022): 5406. http://dx.doi.org/10.3390/en15155406.
Abstract (sommario):
The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying.
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SHEN, SALLY, ANTOON PELSSER e PETER SCHOTMAN. "Robust hedging in incomplete markets". Journal of Pension Economics and Finance 18, n. 3 (16 marzo 2018): 473–93. http://dx.doi.org/10.1017/s1474747218000069.
Abstract (sommario):
AbstractWe considered a pension fund that needs to hedge uncertain long-term liabilities. We modeled the pension fund as a robust investor facing an incomplete market and fearing model uncertainty for the evolution of its liabilities. The robust agent is assumed to minimize the shortfall between the assets and liabilities under an endogenous worst-case scenario by means of solving a min–max robust optimization problem. When the funding ratio is low, robustness reduces the demand for risky assets. However, cherishing the hope of covering the liabilities, a substantial risk exposure is still optimal. A longer investment horizon or a higher funding ratio weakens the investor's fear of model misspecification. If the expected equity return is overestimated, the initial capital requirement for hedging can be decreased by following the robust strategy.