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Articoli di riviste sul tema "Cash management problem with uncertain demands":
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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.
Tesi sul tema "Cash management problem with uncertain demands":
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Schroeder, Pascal. "Performance guaranteeing algorithms for solving online decision problems in financial systems". Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0143.
Abstract (sommario):
Cette thèse contient quelques problèmes de décision financière en ligne et des solutions. Les problèmes sont formulés comme des problèmes en ligne (OP) et des algorithmes en ligne (OA) sont créés pour résoudre. Comme il peut y avoir plusieurs OAs pour le même OP, il doit y avoir un critère afin de pouvoir faire des indications au sujet de la qualité d’un OA. Dans cette thèse ces critères sont le ratio compétitif (c), la différence compétitive (cd) et la performance numérique. Un OA qui a un c ou cd plus bas qu’un autre est à préférer. Un OA qui possède le c le plus petit est appelé optimal. Nous considérons les OPs suivants. Le problème de conversion en ligne (OCP), le problème de sélection de portefeuille en ligne (PSP) et le problème de gestion de trésorerie en ligne (CMP). Après le premier chapitre d’introduction, les OPs, la notation et l’état des arts dans le champ des OPs sont présentés. Dans le troisième chapitre on résoudre trois variantes des OCP avec des prix interconnectés. En Chapitre 4 on travaille encore sur le problème de recherche de série chronologie avec des prix interconnectés et on construit des nouveaux OAs. À la fin de ce chapitre l’OA k-DIV est créé pour le problème de recherche générale des k maximums. k-DIV est aussi optimal. En Chapitre 5 on résout le PSP avec des prix interconnectés. L’OA créé s’appelle OPIP et est optimal. En utilisant les idées de OPIP, on construit un OA pour le problème d’échange bidirectionnel qui s’appelle OCIP et qui est optimal. Avec OPIP, on construit un OA optimal pour le problème de recherche bidirectionnel (OA BUND) sachant les valeurs de θ_1 et θ_2. Pour des valeurs inconnues, on construit l’OA RUN qui est aussi optimal. Les chapitres 6 et 7 traitent sur le CMP. Dans les deux chapitres il y a des tests numériques afin de pouvoir comparer la performance des OAs nouveaux avec celle des OAs déjà établies. En Chapitre 6 on construit des OAs optimaux ; en chapitre 7 on construit des OA qui minimisent cd. L’OA BCSID résoudre le CMP avec des demandes interconnectées ; LOA aBBCSID résoudre le problème lorsqu’ on connaît les valeurs de θ_1,θ_2,m et M. L’OA n’est pas optimal. En Chapitre 7 on résout le CMP par rapport à m et M en minimisant cd (OA MRBD). Ensuite on construit l’OA HMRID et l’OA MRID pour des demandes interconnectées. MRID minimise cd et HMRID est un bon compromis entre la performance numérique et la minimisation de cdThis thesis contains several online financial decision problems and their solutions. The problems are formulated as online problems (OP) and online algorithms (OA) are created to solve them. Due to the fact that there can be various OA for the same OP, there must be some criteria with which one can make statements about the quality of an OA. In this thesis these criteria are the competitive ratio (c), the competitive difference (cd) and the numerical performance. An OA with a lower c is preferable to another one with a higher value. An OA that has the lowest c is called optimal. We consider the following OPS. The online conversion problem (OCP), the online portfolio selection problem (PSP) and the cash management problem (CMP). After the introductory chapter, the OPs, the notation and the state of the art in the field of OPs is presented. In the third chapter, three variants of the OCP with interrelated prices are solved. In the fourth chapter the time series search with interrelated prices is revisited and new algorithms are created. At the end of the chapter, the optimal OA k-DIV for the general k-max search with interrelated prices is developed. In Chapter 5 the PSP with interrelated prices is solved. The created OA OPIP is optimal. Using the idea of OPIP, an optimal OA for the two-way trading is created (OCIP). Having OCIP, an optimal OA for the bi-directional search knowing the values of θ_1 and θ_2 is created (BUND). For unknown θ_1 and θ_2, the optimal OA RUNis created. The chapter ends with an empirical (for OPIP) and experimental (for OCIP, BUND and RUN) testing. Chapters 6 and 7 deal with the CMP. In both of them, a numerical testing is done in order to compare the numerical performance of the new OAs to the one of the already established ones. In Chapter 6 an optimal OA is constructed; in Chapter 7, OAs are designed which minimize cd. The OA BCSID solves the CMP with interrelated demands to optimality. The OA aBBCSID solves the CMP when the values of de θ_1, θ_2,m and M are known; however, this OA is not optimal. In Chapter 7 the CMP is solved, knowing m and M and minimizing cd (OA MRBD). For the interrelated demands, a heuristic OA (HMRID) and a cd-minimizing OA (MRID) is presented. HMRID is good compromise between the numerical performance and the minimization of cd. The thesis concludes with a short discussion about shortcomings of the considered OPs and the created OAs. Then some remarks about future research possibilities in this field are given
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Bernardo, Papini Marcella Verfasser], Pannek [Akademischer Betreuer] Pannek, Pannek [Gutachter] Pannek e Hans-Dietrich [Gutachter] [Haasis. "Robust Capacitated Vehicle Routing Problem with Uncertain Demands / Marcella Bernardo Papini ; Gutachter: Pannek Pannek, Hans-Dietrich Haasis ; Betreuer: Pannek Pannek". Bremen : Staats- und Universitätsbibliothek Bremen, 2019. http://d-nb.info/1196286310/34.
Capitoli di libri sul tema "Cash management problem with uncertain demands":
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Werners, Brigitte, e Yuriy Kondratenko. "Alternative Fuzzy Approaches for Efficiently Solving the Capacitated Vehicle Routing Problem in Conditions of Uncertain Demands". In Complex Systems: Solutions and Challenges in Economics, Management and Engineering, 521–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69989-9_31.
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Nasiri, Gholam Reza, e Fariborz Jolai. "Supply Chain Network Design in Uncertain Environment". In Optimization Techniques for Problem Solving in Uncertainty, 262–82. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5091-4.ch010.
Abstract (sommario):
Dynamic environment imposes such conditions that make it necessary for companies to consider sources of uncertainty in designing core business processes and optimizing supply chain operations. Efficient management of a supply system requires an integrated approach towards various operational functions and related source of uncertainties. Uncertain conditions in supply network design problem such as market demands, delivery time, and facility capacity are considered and incorporated by many studies at the mathematical programming formulations as well. In this chapter, extensive review of existing SCND literature, brief overview and classification on uncertainty sources, useful strategies to deal uncertainties, model formulation with uncertain/stochastic parameters, efficient developed solution methodologies, and improvement adjustment mechanisms are discussed. Lastly, some directions for further research in this area are suggested.
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Zurbriggen, Cristina, e Mariana González Lago. "Navigating the Future through Experimental Policy Design". In The Future of Public Administration - Adapting to a Dynamic World [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004169.
Abstract (sommario):
Innovative strategies in public policy design are crucial to effectively address the complex and interconnected environmental challenges governments face today. The intricate and uncertain nature of these problems often requires experimental coproduction solutions that integrate and synthesize diverse areas of expertise and stakeholder viewpoints and demand experimental and adaptive capacity to respond in turbulent times. As policy-generative experiments in policy design spread and gain legitimacy, they pose substantial challenges: What challenges do governments encounter in implementing experimental coproduction solutions, and what capacities should public organizations develop to navigate complex and uncertain issues effectively? This article analyses the innovative patterns in policy design experiments and the public sector’s capacities to develop in the twenty-first century. It illustrates this discussion with the case of Uruguay’s soil conservation management plans (SUMPs) as an example of experimental public policy to address land degradation and promote sustainable land management practices. Through this analysis, this paper aims to contribute to evaluating the coproduction experiments and to current discussions on how governments can foster innovation and navigate change processes to address complex and uncertain issues in sustainability transitions.
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Polat, Olcay. "Designing of Container Feeder Service Networks Under Unstable Demand Conditions". In Logistics and Supply Chain Management in the Globalized Business Era, 115–36. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8709-6.ch006.
Abstract (sommario):
The COVID-19 pandemic has greatly magnified supply challenges in all industries, and virus waves continue to cause an extraordinary amount of variation in both the demand for and the availability of necessary products. This uncertainty has also forced many organizations including container liner shipping to redesign their supply chain. Feeder services from hub ports are essential chain of shipping networks. This chapter addresses the design of feeder networks under consideration of demand fluctuations over the year. For this purpose, a perturbation-based variable neighbourhood search approach is developed in order to determine the feeder ship fleet size and mix, the fleet deployment, service routes, and voyage schedules to minimize operational costs. In the case study investigation, the authors consider the feeder network design problem faced by a feeder shipping company as a sample application. The performance of alternate network configurations is compared under dynamic demand conditions. Numerical results highlight the advantage of dynamic and flexible design of feeder service networks.
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Rajesh, R., S. Pugazhendhi e K. Ganesh. "Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers". In Management Innovations for Intelligent Supply Chains, 184–203. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2461-0.ch010.
Abstract (sommario):
Third party logistics (3PL) service providers play a growing responsibility in the management of supply chain. The global and competitive business environment of 3PLs has recognized the significance of a speedy and proficient service towards the customers in the past few decades. Particularly in warehousing, distribution, and transportation services, a number of customers anticipate 3PLs to improve lead times, fill rates, inventory levels, etc. Therefore, the 3PLs are under demands to convene a range of service necessities of customers in an active and uncertain business environment. As a consequence of the dynamic environment in which supply chain must operate, 3PLs should sustain an effective distribution system of high performance and must make a sequence of inter-related decisions over time for their distribution networks. Warehouses play an important role in sustaining the continual flow of goods and materials between the manufacturer and customers. The performance of the 3PL supply chain network can be effortlessly enhanced by a balanced allocation of customers to warehouses. In this paper, the authors develop a genetic algorithm and a particle-swarm-optimisation algorithm for solving the balanced allocation problem and the results are encouraging.
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Farmani, R., D. A. Savic, H. J. Henriksen, J. L. Molina, R. Giordano e J. Bromley. "Evolutionary Bayesian Belief Networks for Participatory Water Resources Management under Uncertainty". In Green Technologies, 524–39. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-472-1.ch309.
Abstract (sommario):
A participatory integrated (social, economic, environmental) approach based on causal loop diagram, Bayesian belief networks and evolutionary multiobjective optimisation is proposed for efficient water resources management. The proposed methodology incorporates all the conflicting objectives in the decision making process. Causal loop diagram allows a range of different factors to be considered simultaneously and provides a framework within which the contributions of stakeholders can be taken into account. Bayesian belief networks takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of Bayesian belief network with evolutionary multiobjective optimisation algorithm allows analysis of trade-off between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision making process. The proposed methodology is used to model decision making process for complex environmental problems, considering uncertainties, addressing temporal dynamics, uncovering discrepancies in decision analysis process (e.g. completeness or redundancy of the model based on utility function) and generating policy options that trade-off between conflicting objectives. The effectiveness of the proposed methodology is examined in several water resources management problems. The case studies include optimum water demand management, UK; management of groundwater contamination of Copenhagen source capture zone areas, Denmark and simultaneous optimum management of four overexploited aquifers in Spain. It is shown that the proposed methodology generates large number of management options that trade-off between different objectives. The remaining task is to choose, depending on the preference of decision makers, a group of solutions for more detailed analysis.
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Nene, Dr Sanele Enock. "ARTIFICIAL INTELLIGENCE FOR SUSTAINABILITY: INNOVATIONS IN BUSINESS AND FINANCIAL SERVICES". In Artificial Intelligence and Emerging Technologies, 7–20. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/nbennurch302.
Abstract (sommario):
Artificial Intelligence (AI) is transforming how we live and projecting how future should look like. AI is a form of technology developed to mimic human intelligence through decision making, problem solving, learning and improving abilities to meet the demands posited by the environment. The businesses must be agile enough to effectively respond to the markets that are volatile, uncertain, complex and ambiguous. As a result, AI is a critical tool that businesses should adopt to respond to these markets and to remain sustainable. Global agents such as United Nations must ensure that developing countries are not left behind on the implementation of AI as this might have negative implications on the markets of these countries. Since AI is an object that should be embraced by humans, there are ethical concerns that must be addressed to ensure that it is effectively implemented. The implementation of AI should be facilitated in a manner that is safe for humans, and that will improve its uptake. AI researchers have an obligation to explore and explain for the society what AI means and the general ethics surrounding it, risks involved must be clearly articulated for the society. AI developers and implementers must ensure that the right to autonomy for the society is not diminished. This chapter is presenting sustainable AI ethics which constitutes of environmental impacts of AI, carbon emissions and AI modelling, data management, bias in AI, privacy and security, accountability and transparency, international perspectives, and future considerations of AI ethics
Atti di convegni sul tema "Cash management problem with uncertain demands":
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Rezapour, Shabnam, Janet K. Allen e Farrokh Mistree. "Uncertainty Propagation in a Supply Chain / Network With Uncertain Facility Performance". In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34255.
Abstract (sommario):
Decentralized production systems in supply chains / networks makes them more profitable and agile than traditional enterprises with centralized production systems. However, this decentralization makes supply chains / networks more vulnerable respect to uncertainties which are unavoidable. Today’s supply chains / networks producing and supplying their products to markets are characterized by uncertain demands (called demand-side uncertainty) and uncertainties associated with the performances of their constituent production facilities (called supply-side uncertainty). Supply-side uncertainty is due to the fact that there is not any perfect production system. Sparse literature of supply-side uncertainty management in supply chains / networks is only restricted to supply chains / networks with single-echelon supply processes. However most of the real case supply chains / networks have longer production processes involving suppliers of suppliers, suppliers, component manufacturers, assemblers, etc. In this paper we fill this gap of the literature by considering a supply chain/network with multi-echelon supply process including unreliable production facilities working in markets with uncertain demands. We show that in such a complex production process in addition to investigating the local effects of the uncertainties in the performances of their corresponding facilities, it is necessary to consider their global and cumulative effect on the performance of the entire supply chain/networks by following the propagation of these uncertainties in the flow of the material and product. Not only we introduce and describe the salient features of uncertainty propagation phenomenon in supply chains/networks, but also we demonstrate its quantification approach. Finally we propose mathematical models and solution approaches that can provide robust production plans for the supply chain/network that are protected against all demand and supply side uncertainties and their propagated effects. Performances of the proposed models and solution approaches are tested with test problems and a real case problem from automotive industry.
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De Filippo, Allegra, Michele Lombardi e Michela Milano. "Methods for off-line/on-line optimization under uncertainty". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/177.
Abstract (sommario):
In this work we present two general techniques to deal with multi-stage optimization problems under uncertainty, featuring off-line and on-line decisions. The methods are applicable when: 1) the uncertainty is exogenous; 2) there exists a heuristic for the on-line phase that can be modeled as a parametric convex optimization problem. The first technique replaces the on-line heuristics with an anticipatory solver, obtained through a systematic procedure. The second technique consists in making the off-line solver aware of the on-line heuristic, and capable of controlling its parameters so as to steer its behavior. We instantiate our approaches on two case studies: an energy management system with uncertain renewable generation and load demand, and a vehicle routing problem with uncertain travel times. We show how both techniques achieve high solution quality w.r.t. an oracle operating under perfect information, by obtaining different trade-offs in terms of computation time.
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De Filippo, Allegra, Michele Lombardi e Michela Milano. "How to Tame Your Anticipatory Algorithm". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/150.
Abstract (sommario):
Sampling-based anticipatory algorithms can be very effective at solving online optimization problems under uncertainty, but their computational cost may be prohibitive in some cases. Given an arbitrary anticipatory algorithm, we present three methods that allow to retain its solution quality at a fraction of the online computational cost, via a substantial degree of offline preparation. Our approaches are obtained by combining: 1) a simple technique to identify likely future outcomes based on past observations; 2) the (expensive) offline computation of a "contingency table"; and 3) an efficient solution-fixing heuristic. We ground our techniques on two case studies: an energy management system with uncertain renewable generation and load demand, and a traveling salesman problem with uncertain travel times. In both cases, our techniques achieve high solution quality, while substantially reducing the online computation time.
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Jaržemskis, Andrius, e Ilona Jaržemskienė. "FORECAST METHODS FOR INVESTMENT OF COUNTRY WIDE ELECTRIC VEHICLE CHARGING STATIONS: LITHUANIAN CASE". In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.753.
Abstract (sommario):
The aim of this article is to present a complex model for forecasting the required investments based on the forecast of the increase in the number of electric vehicles and their demand for energy and investments. Scientific problem is that current approach on forecasting of electric vehicles is to abstract, forecast models can’t be transferred from country to country. This article proposes a model of forecasting investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy. The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319 470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total en-ergy consumption in the country. It demands EUR 230.0 million in the low-voltage grid and EUR 209.0 million in the charging stations. Main limitations are related to statistics available for modelling and human behaviour uncertainty, especially in evaluation impact of measures to foster use of electric vehicles.
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Dong, Hang, Boshi Wang, Bo Qiao, Wenqian Xing, Chuan Luo, Si Qin, Qingwei Lin, Dongmei Zhang, Gurpreet Virdi e Thomas Moscibroda. "Predictive Job Scheduling under Uncertain Constraints in Cloud Computing". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/499.
Abstract (sommario):
Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.
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Deppen, Timothy O., Andrew G. Alleyne, Kim A. Stelson e Jonathan J. Meyer. "Model Predictive Control of an Electro-Hydraulic Powertrain With Energy Storage". In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-5969.
Abstract (sommario):
This paper presents a model predictive control approach to solving the energy management problem within a series hydraulic hybrid powertrain. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. Model predictive control was chosen for control design because this technique requires no information about the future drive cycle, which can be highly uncertain in urban settings. The proposed control strategy was validated experimentally using an electro-hydraulic powertrain testbed which includes energy storage. The experimental study demonstrates the controller’s ability to track a reference trajectory while achieving efficient engine operation.
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Ghedan, Shawket, Meher Surendra, Agustin Maqui, Mahmoud Elwan, Rami Kansao, Hesham Mousa, Raman Jha et al. "Rapid and Efficient Waterflood Optimization Using Augmented AI Approach in a Complex Offshore Field". In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207458-ms.
Abstract (sommario):
Abstract Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez. Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections. Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation. For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset. The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.
Rapporti di organizzazioni sul tema "Cash management problem with uncertain demands":
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Mayfield, Colin. Capacity Development in the Water Sector: the case of Massive Open On-line Courses. United Nations University Institute for Water, Environment and Health, gennaio 2017. http://dx.doi.org/10.53328/mwud6984.
Abstract (sommario):
The Sustainable Development Goal 6 targets are all dependent on capacity development as outlined in SDG 6a “Expand international cooperation and capacity-building support to developing countries in water- and sanitation related activities and programmes “. Massive Open On-line Courses (MOOCs) and distance learning in general have a significant role to play in this expansion. This report examines the role that MOOCs and similar courses could play in capacity development in the water sector. The appearance of MOOCs in 2010/11 led within 4 years to a huge increase in this type of course and in student enrollment. Some problems with student dropout rates, over-estimating the transformational and disruptive nature of MOOCs and uncertain business models remain, but less “massive” MOOCs with more engaged students are overcoming these problems. There are many existing distance learning courses and programmes in the water sector designed to train and/ or educate professionals, operators, graduate and undergraduate students and, to a lesser extent, members of communities dealing with water issues. There are few existing true MOOCs in the water sector. MOOCs could supply significant numbers of qualified practitioners for the water sector. A suite of programmes on water-related topics would allow anyone to try the courses and determine whether they were appropriate and useful. If they were, the students could officially enroll in the course or programme to gain a meaningful qualification or simply to upgrade their qualifications. To make MOOCs more relevant to education and training in the water sector an analysis of the requirements in the sector and the potential demand for such courses is required. Cooperation between institutions preparing MOOCs would be desirable given the substantial time and funding required to produce excellent quality courses. One attractive model for cooperation would be to produce modules on all aspects of water and sanitation dealing with technical, scientific, social, legal and management topics. These should be produced by recognized experts in each field and should be “stand-alone” or complete in themselves. If all modules were made freely available, users or mentors could assemble different MOOCs by linking relevant modules. Then extracts, simplified or less technical versions of the modules could then be used to produce presentations to encourage public participation and for other training purposes. Adaptive learning, where course materials are more tailored to individual students based on their test results and reactions to the material, can be an integral part of MOOCs. MOOCs efficiently provide access to quality courses at low or no cost to students around the world, they enable students to try courses at their convenience, they can be tailored to both professional and technical aspects, and they are very suitable to provide adaptive learning courses. Cooperation between institutions would provide many course modules for the water sector that collectively could provide excellent programmes to address the challenges of capacity development for SDG 6 and other issues within the water sector.