Zeitschriftenartikel: „Bi-level dynamic programming“

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Singh, Vishnu Pratap. „On Solving Linguistic Bi-Level Programming Problem Using Dynamic Programming“. International Journal of Fuzzy System Applications 10, Nr. 1 (Januar 2021): 43–63. http://dx.doi.org/10.4018/ijfsa.2021010103.

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In this work, a linguistic bi-level programming problem has been developed where the functional relationship linking decision variables and the objective functions of the leader and the follower are not utterly well known to us. Because of the uncertainty in practical life decision-making situation most of the time, it is inconvenient to find the veracious relationship between the objective functions of leader, follower, and the decision variables. It is expected that the source of information which gives some command about the objective functions of leader and follower is composed by a block of fuzzy if-then rules. In order to analyze the model, a dynamic programming approach with a suitable fuzzy reasoning scheme is applied to calculate the deterministic functional relationship linking the decision variables and the objective functions of the leader as well as the follower. Thus, a bi-level programming problem is constructed from the actual fuzzy rule-based to the conventional bi-level programming problem. A numerical example has been solved to signify the computational procedure.

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Lu, Ting, Zhongzhen Yang, Dongfang Ma und Sheng Jin. „Bi-Level Programming Model for Dynamic Reversible Lane Assignment“. IEEE Access 6 (2018): 71592–601. http://dx.doi.org/10.1109/access.2018.2881290.

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ABO-SINNA, MAHMOUD A., und Azza H. Amer. „An Interactive Dynamic Fuzzy Goal Programming for Bi-level Multiobjective Linear Fractional Programming Problems.“ JOURNAL OF ADVANCES IN MATHEMATICS 12, Nr. 12 (28.02.2017): 6991–7007. http://dx.doi.org/10.24297/jam.v12i12.3720.

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This paper presents an interactive dynamic fuzzy goal programming (DFGP) approach for solving bi-level multiobjective linear fractional programming (BL MOLFP) problems with the characteristics of dynamic programming (DP). In the proposed approach, the membership function of the objective goals of a problem with fuzzy aspiration levels are defined first as the membership function for vector of fuzzy goals of the decision variables controlled by firstâ€“level decision maker are developed first in the model formulation of the problem. The method of variable change, on the under and over deviational variables of the membership goals associated with the fuzzy goals of the model, is introduced to solve the problem efficiently by using linear goal programming (LGP) methodology. Then, under the framework of preemptive priority based GP, a multi stage DP model of the problem is used for achievement of the highest degree (unity) of each of the membership functions. In the decision process, the goal satisficing philosophy of GP is used recursively to arrive at the most satisfactory solution and the suggested algorithm to simplify the solution procedure by DP at each stage is proposed. This paper is considered as an extension work of Mahmoud A. Abo-Sinna and Ibrahim A. Baky (2010) by using dynamic approach. Finally, this approach is illustrated by a given numerical example.

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Yao, Liming, Zhongwen Xu, Mahdi Moudi und Zongmin Li. „Optimal water allocation in Iran: a dynamic bi-level programming model“. Water Supply 19, Nr. 4 (27.09.2018): 1120–28. http://dx.doi.org/10.2166/ws.2018.165.

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Abstract Water resource scarcity increases societal instability, poverty, and economic recession. Therefore, sound water resource management is vital to alleviating water crises in the agricultural, domestic, and industrial sectors. Southeast Iran, which currently lacks good water resource management, is experiencing a severe water crisis. Taking Zabol and Zahedan as examples, this paper seeks to optimize water allocation between the two cities using a dynamic bi-level programming model; the upper level objective minimizes the deviation between water demand and supply, and the lower level objective maximizes the net economic benefits in each sector. A sensitivity analysis is then conducted on the different sources of available water to provide more information on water allocation over time.

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Lin, Dung-Ying, Avinash Unnikrishnan und S. Waller. „A genetic algorithm for bi-level linear programming dynamic network design problem“. Transportation Letters 1, Nr. 4 (Oktober 2009): 281–94. http://dx.doi.org/10.3328/tl.2009.01.04.281-294.

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Sun, Dazhi, Rahim F. Benekohal und S. Travis Waller. „Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization“. Computer-Aided Civil and Infrastructure Engineering 21, Nr. 5 (Juli 2006): 321–33. http://dx.doi.org/10.1111/j.1467-8667.2006.00439.x.

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Baozhuang, Niu. „A dynamic pricing model for postponement supply chain: the bi-level programming approach“. International Journal of Logistics Economics and Globalisation 1, Nr. 3/4 (2008): 383. http://dx.doi.org/10.1504/ijleg.2008.023169.

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Li, Minghua, Chengyong Xu, Yan Xu, Li Ma und Yun Wei. „Dynamic Sign Guidance Optimization for Crowd Evacuation considering Flow Equilibrium“. Journal of Advanced Transportation 2022 (21.01.2022): 1–19. http://dx.doi.org/10.1155/2022/2555350.

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The dynamic exit sign has been verified as an effective means to guide the pedestrian during evacuation. The most critical mechanism with dynamic exit sign guidance is to balance the pedestrian flow on each exit route by optimizing the direction of signs. This paper formulates a bi-level programming model for the direction optimization problem of dynamic signs in buildings. In the bi-level program, the upper-level model is a system optimal model, aiming to minimize the total travel time by optimizing the dynamic sign direction. The lower-level model is a pedestrian assignment model satisfying the dynamic user optimal principle that describes the evacuee exit/route choice behaviour to achieve a balanced pedestrian distribution on the route. A method based on the fundamental diagram, the cell transmission model, and the point-queuing theory is developed to estimate evacuation travel time considering congestion and queuing. A heuristic algorithm is extended to solve the bi-level program. Finally, the proposed methodology is validated with numerical examples. Results reveal that the proposed model can produce the optimal dynamic sign direction, significantly improving the evacuation efficiency.

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Wong, Jui-Tsung, Chwen-Tzeng Su und Chun-Hsien Wang. „Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques“. Applied Mathematical Modelling 36, Nr. 5 (Mai 2012): 2003–16. http://dx.doi.org/10.1016/j.apm.2011.08.017.

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Soleimani-Alyar, Maryam, und Alireza Ghaffari-Hadigheh. „Dynamic Network Interdiction Problem with Uncertain Data“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Nr. 02 (April 2018): 327–42. http://dx.doi.org/10.1142/s0218488518500174.

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This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network interdiction problem and the obtained deterministic correspondent. Application of the generalized Benders’ decomposition algorithm is considered as the solution approach to the resulting mixed-integer nonlinear programming problem. Finally, a numerical example is presented to illustrate the model and the algorithm.

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Hu, Zhineng, Shiyu Yan, Chengwei Lv und Liming Yao. „Sustainable development oriented bi-level dynamic programming method toward the coal–water conflict in China“. Energy & Environment 30, Nr. 8 (20.05.2019): 1396–436. http://dx.doi.org/10.1177/0958305x19845533.

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Chaari, Riadh, Moez Abennadher, Jamel Louati und Mohamed Haddar. „Mathematical methodology for optimization of the clamping forces accounting for workpiece vibratory behaviour“. International Journal for Simulation and Multidisciplinary Design Optimization 5 (2014): A13. http://dx.doi.org/10.1051/smdo/2013005.

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This paper addresses the problem of determining the minimum clamping forces that ensure the dynamic fixturing stability. The clamping force optimization problem is formulated as a bi-level nonlinear programming problem and solved using a computational intelligence technique called particle swarm optimization (PSO). Indeed, we present an innovative simulation methodology that is able to study the effects of fixture-workpiece system dynamics and the continuously change due to material removal on fixturing stability and the minimum required clamping forces during machining. The dynamic behaviour of the fixtured workpiece subjected to time-and space-varying machining loads is simulated using a forced vibration model based on the regenerative vibrations of the cutter and workpiece excited by the dynamic cutting forces. Indeed, Material removal significantly affects the fixture-workpiece system dynamics and subsequently the minimum clamping forces required for achieving fixturing dynamic stability.

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Lu, An Sheng, Hui Xiu Jin, Zi Hui Li und Jing Li. „Research and Implementation on a Statistic Algorithm Based on Multidimensional Dynamic Reports“. Applied Mechanics and Materials 411-414 (September 2013): 102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.102.

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The report presents information with dynamic data and various formats. It is the foundation of the basic business requirements of enterprise and the implementation BI strategy. A good report can reflect enterprise's current operation status and give support to strategic decisions for the future. This paper gives the definition of multidimensional dynamic reports. The method of configuring report information by using XML and realizing the statistics of multidimensional dynamic reports by high-level programming language are also in this paper.

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JI, Mingjun, und Maoying HE. „Optimization of Two-Stage Port Logistics Network of Dynamic Hinterland Based on Bi-level Programming Model“. Journal of Transportation Systems Engineering and Information Technology 10, Nr. 6 (Dezember 2010): 89–94. http://dx.doi.org/10.1016/s1570-6672(09)60076-3.

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Fan, Cheng-li, Qing-hua Xing, Qiang Fu und Zhi-gang Zou. „Bi-level programming modeling and hierarchical hybrid algorithm for antimissile dynamic firepower allocation problem with uncertain environment“. Pattern Analysis and Applications 20, Nr. 1 (25.06.2016): 287–306. http://dx.doi.org/10.1007/s10044-016-0562-y.

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Li, Wen, Chao Wu und Lu Kai Zhang. „The Analysis of Transit Assignment with Variable Transit Price Based on Time Schedule“. Advanced Materials Research 1030-1032 (September 2014): 2260–63. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2260.

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In this paper, we propose a method of peak period pricing to alleviate peak period crowding on the transit vehicles and the platforms. We expanded the route network into a time-expanded network based on the transit schedule, so the dynamic transit assignment is transferred into static transit assignment. Congestion coefficient is used to express the crowd in transit vehicles. In order to reach the goal of both government and the travelers, we establish a bi-level programming model.

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van Dijk, Diana, Eligius M. T. Hendrix, Rene Haijema, Rolf A. Groeneveld und Ekko C. van Ierland. „On solving a bi-level stochastic dynamic programming model for analyzing fisheries policies: Fishermen behavior and optimal fish quota“. Ecological Modelling 272 (Januar 2014): 68–75. http://dx.doi.org/10.1016/j.ecolmodel.2013.09.015.

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Liu, Jian, Qi Huang, Yuhang Han, Shiyun Chen, Nan Pan und Renxin Xiao. „Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem“. Biomimetics 9, Nr. 1 (03.01.2024): 26. http://dx.doi.org/10.3390/biomimetics9010026.

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Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach’s efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

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Sun, Zhi-Yuan, Yue Li, Wen-Cong Qu und Yan-Yan Chen. „Variable cycle control model for intersection based on multi-source information“. International Journal of Modern Physics B 32, Nr. 13 (11.05.2018): 1850160. http://dx.doi.org/10.1142/s0217979218501606.

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In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

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WANG, XUE-FENG, XIAO-MING SUN und YANG FANG. „GENETIC ALGORITHM SOLUTION FOR MULTI-PERIOD TWO-ECHELON INTEGRATED COMPETITIVE/UNCOMPETITIVE FACILITY LOCATION PROBLEM“. Asia-Pacific Journal of Operational Research 25, Nr. 01 (Februar 2008): 33–56. http://dx.doi.org/10.1142/s0217595908001626.

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This paper addresses the multi-period two-echelon integrated competitive/uncompetitive facility location problem in a distribution system design that involves locating regional distribution centers (RDCs) and stores, and determining the best strategy for distributing the commodities from a central distribution center (CDC) to RDCs and from RDCs to stores. The goal is to determine the optimal numbers, locations and capacities of RDCs and stores so as to maximize the total profit of the distribution system. Unlike most of past research, our study allows for dynamic planning horizon, distribution of commodities, configuration of two-echelon facilities, availability of capital for investment, external market competition, customer choice behavior and storage limitation. This problem is formulated as a bi-level programming model and a mutually consistent programming mode, respectively. Since such a distribution system design problem belongs to a class of NP-hard problem, a genetic algorithm-based heuristic (GA) is presented and compared with random search solution and mutually consistent solution (MC) using numerical example. The computational results show that the GA approach is efficient and the values of the performance index were significantly improved relative to the MC.

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Yang, Hang, Zhongyu Wang, Yajie Zou, Bing Wu und Yinhai Wang. „Modified Stackelberg Games Approach for Dynamic Signal Control and Route Choice Equilibrium on Mixed Networks“. Transportation Research Record: Journal of the Transportation Research Board 2674, Nr. 9 (16.07.2020): 51–65. http://dx.doi.org/10.1177/0361198120926998.

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The dynamic signal control and route choice equilibrium are usually integrated into a noncooperative game between the network authority and the road users. There are mainly two problems in most existing optimization methods. Firstly, the authority is often placed in the upper level in bi-level programming models, this pure system-optimization-oriented framework may increase the difficulty in obtaining an equilibrium flow distribution. Secondly, the rate of drivers’ compliance on the control strategy has not been fully investigated, which makes the problem intractable in real time, especially in a connected vehicle (CV) environment. This paper proposes a modified Stackelberg games model to change the format of the authority-user and user-authority dynamically. The direct communication between the authority and users is established, and the drivers’ compliance rate is applied as the level-change threshold index. Considering the difference between the drivers’ realized travel time and the predicted travel time on the variable message sign (VMS), a logit model is formed to calibrate the compliance rate in every time step. Based on a modified wavelet neural network algorithm, the model predictive control (MPC) fulfills the level-change procedure using the software Matlab 2018b. Six benchmarks are applied in a numerical example. The results show that the proposed model with the centralized framework obtains the minimum total travel cost compared with the benchmarks. Combined with the real-time mutual feedback between drivers’ response and control strategy, the level-change procedure potentially maintains the compliance rate within a certain level.

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Xu, Fangqiu, Xiaopeng Li und Chunhua Jin. „Optimal capacity configuration and dynamic pricing strategy of a shared hybrid hydrogen energy storage system for integrated energy system alliance: A bi-level programming approach“. International Journal of Hydrogen Energy 69 (Juni 2024): 331–46. http://dx.doi.org/10.1016/j.ijhydene.2024.05.011.

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Razzac, Amal Abdel, Linda Salahaldin, Salah Eddine Elayoubi, Yezekael Hayel und Tijani Chahed. „A Game Theoretical Real Options Framework for Investment Decisions in Mobile TV Infrastructure“. Asia-Pacific Journal of Operational Research 34, Nr. 04 (August 2017): 1750014. http://dx.doi.org/10.1142/s0217595917500142.

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This work presents a strategic investment framework for mobile TV infrastructure. We address the question of whether an operator should enter the mobile TV market and, if yes, how and when to do so. We perform a capacity and QoS analysis that shows that Mobile TV is not a simple added-on service but requires new planning strategies that need heavy investments. We consider two possible settings: a competitive mobile TV network where a digital video broadcasting (DVB) operator and a cellular network operator build their independent networks and a cooperative setting where the TV network is mainly relying on a DVB infrastructure whose coverage can be complemented by a cellular network. Two main issues alter the investment decision of the stakeholders, namely, the market uncertainty and the competition or the willingness of cooperation between the main technological players. We define a game theoretical real options framework and propose a novel bi-level dynamic programming algorithm that solves the underlying profit maximization problem. Our numerical results illustrate the optimal investment decisions of both actors and the impact of the system parameters as well as the degree of uncertainty on the investment dates.

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Hu, Guojing, Weike Lu, Feng Wang und Robert W. Whalin. „Macroscopic Fundamental Diagram Based Discrete Transportation Network Design“. Journal of Advanced Transportation 2020 (20.01.2020): 1–13. http://dx.doi.org/10.1155/2020/4951953.

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The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and k-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results.

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Zhao, Kegang, Jinghao Bei, Yanwei Liu und Zhihao Liang. „Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method“. Energies 12, Nr. 17 (25.08.2019): 3268. http://dx.doi.org/10.3390/en12173268.

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The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

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Dai, Qian, und Jiaqi Yang. „A Distributionally Robust Chance-Constrained Approach for Modeling Demand Uncertainty in Green Port-Hinterland Transportation Network Optimization“. Symmetry 12, Nr. 9 (10.09.2020): 1492. http://dx.doi.org/10.3390/sym12091492.

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This paper discusses a bi-objective programming of the port-hinterland freight transportation system based on intermodal transportation with the consideration of uncertain transportation demand for green concern. Economic and environmental aspects are integrated in order to obtain green flow distribution solutions for the proposed port-hinterland network. A distributionally robust chance constraint optimization model is then established for the uncertainty of transportation demand, in which the chance constraint is described such that transportation demand is satisfied under the worst-case distribution based on the partial information of the mean and variance. The trade-offs among different objectives and the uncertainty theory applied in the modeling both involve the notion of symmetry. Taking the actual port-hinterland transportation network of the Yangtze River Economic Belt as an example, the results reveal that the railway-road intermodal transport is promoted and the change in total network CO2 emissions is contrary to that in total network costs. Additionally, both network costs and network emissions increase significantly with the growth of the lower bound of probability for chance constraint. The higher the probability level grows, the greater the trade-offs between two objectives are influenced, which indicates that the operation capacity of inland intermodal terminals should be increased to meet the high probability level. These findings can help provide decision supports for the green development strategy of the port-hinterland container transportation network, which meanwhile faces a dynamic planning problem caused by stochastic demands in real life.

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Ahmad, Md Tanweer, und Sandeep Mondal. „Dynamic supplier selection approach for mining equipment company“. Journal of Modelling in Management 14, Nr. 1 (11.02.2019): 77–105. http://dx.doi.org/10.1108/jm2-04-2018-0046.

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PurposeThis paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.Design/methodology/approachAn integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.FindingsThe ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.Practical implicationsThis paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.Originality/valueIt provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

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Liu, Feng, Jian-Jun Wang, Haozhe Chen und De-Li Yang. „Machine scheduling with outsourcing“. International Journal of Logistics Management 25, Nr. 1 (06.05.2014): 133–59. http://dx.doi.org/10.1108/ijlm-12-2012-0142.

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Purpose – The purpose of this paper is to study the use of outsourcing as a mechanism to cope with supply chain uncertainty, more specifically, how to deal with sudden arrival of higher priority jobs that require immediate processing, in an in-house manufacturer's facility from the perspective of outsourcing. An operational level schedule of production and distribution of outsourced jobs to the manufacturer's facility should be determined for the subcontractor in order to achieve overall optimality. Design/methodology/approach – The problem is of bi-criteria in that both the transportation cost measured by number of delivery vehicles and schedule performance measured by jobs’ delivery times. In order to obtain the problem's Pareto front, we propose dynamic programming (DP) heuristic solution procedure based on integrated decision making, and population-heuristic solution procedures using different encoding schemes based on sequential decision making. Computational studies are designed and carried out by randomly generating comparative variations of numerical problem instances. Findings – By comparing several existing performance metrics for the obtained Pareto fronts, it is found that DP heuristic outperforms population-heuristic in both solutions diversity and proximity to optimal Pareto front. Also in population-heuristic, sub-range keys representation appears to be a better encoding scheme for the problem than random keys representation. Originality/value – This study contributes to the limited yet important knowledge body on using outsourcing approach to coping with possible supply chain disruptions in production scheduling due to sudden customer orders. More specifically, we used modeling methodology to confirm the importance of collaboration with subcontractors to effective supply chain risk management.

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Bou Nader, Wissam, Yuan Cheng, Emmanuel Nault, Alexandre Reine, Samer Wakim, Bilal Kabalan und Maroun Nemer. „Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles“. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, Nr. 6 (06.12.2019): 1544–62. http://dx.doi.org/10.1177/0954407019890160.

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Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine–system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems’ efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine–system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine–auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

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Faghihmaleki, Hadi, Elmira Khaksar Najafi und Ali Hooshmand Aini. „Seismic rehabilitation effect in a steel moment frame subjected to tow critical loads“. International Journal of Structural Integrity 8, Nr. 1 (06.02.2017): 25–34. http://dx.doi.org/10.1108/ijsi-09-2015-0034.

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Purpose The purpose of this paper is to present a probabilistic assessment and verify the effectiveness of seismic improvement schemes against earthquake, blast and progressive collapse. The probabilistic analysis is performed by taking into account the uncertainties in loading such as planar configuration and amplitude of the blast loading. A standard Monte Carlo (MC) simulation method is employed to generate various concepts of the uncertain parameters within the problem. For a given concept, various local dynamic analyses are performed within a certain range of distance, in order to quantify and locate the damage induced by impact wave on structural elements. In the next step, a limit state analysis is performed in order to investigate whether a progressive collapse mechanism forms under the acting loads or not. Design/methodology/approach ( | ) and ( | ) are blast fragility and seismic fragility, respectively; ( ) and ( ) are annual occurrence rate of earthquake and blast, respectively. The purpose of the current study is to calculate for the primary structure as well as the retrofitted structure. Annual occurrence rate of earthquake can be calculated by using probability seismic hazard analysis for the site of interest, where the structure is located. In this paper, blast fragility and seismic fragility are defined rather differently; in other words, seismic fragility is defined as the probability of structural collapse given a specified level of seismic intensity whereas blast fragility is defined as the probability of collapse given that a significant blast event takes place. Both blast and earthquake loading conditions involve the activation of energy dissipation mechanism and, as a consequence, both can be resisted employing ductility enhancing techniques, such as column wrapping or jacketing and steel bracing. Findings The current paper aims to present a probabilistic assessment of progressive collapse under blast and earthquake loads. Non-dependent and incompatible events are considered to obtain a general rate of collapse. Finally, probabilistic collapse rate was obtained for a moment frame before and after modifying with convergent steel brace (CBF). The purpose of doing so is to investigate whether seismic improvement schemes can reduce collapse risk of different critical events or not. Originality/value Objective of the present work is to present a methodology for calculating the annual risk of collapse for a civil structure subjected to both seismic and blast loads, using a bi-hazard approach. Given that a blast event takes place, the probability of progressive collapse is calculated using a MC simulation procedure. The simulation procedure implements an efficient non-linear limit state analysis, formulated and solved as a linear programming problem. The probability of collapse caused by an earthquake event can be calculated by integrating the seismic fragility of the structure and the seismic hazard for the site.

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Ji, Shiqian, Jiaming Zhong und Zhaocheng He. „A Bus Subsidy Scheme Design Model Considering Competition between Bus Companies“. Sustainability 14, Nr. 7 (01.04.2022): 4201. http://dx.doi.org/10.3390/su14074201.

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The competition between bus companies plays an important role in the effects of bus subsidy schemes for operation-sharing networks. This paper presents a bi-level programming model considering the competition to design bus subsidy schemes. In the upper sub-model, a subsidy scheme is given with the target of minimizing the total social costs (TSC). Further, in the lower sub-model, each bus company competes with other companies under the subsidy scheme. The competition is portrayed as a non-cooperative game where each company changes the departure frequency of their lines separately to obtain the maximum profit. The subsidy schemes can minimize the TSC when all bus companies pursue the maximum profit. A specific approach combining genetic algorithm and best response dynamics is proposed to solve the model. To validate the constructed model, a real-world case study is conducted in Guangzhou. From the results, as the scheme is implemented, the bus companies will be inspired to increase the line departure frequency, leading to a reduction of the passenger waiting time and performing a higher level of service. The TSC are reduced by 28.6%, among which the total waiting time costs are reduced by 43.0%. The model can guide bus subsidy scheme design for operation-sharing bus networks.

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Zhao, Luda, Zongxu An, Bin Wang, Yanqiu Zhang und Yihua Hu. „A hybrid multi-objective bi-level interactive fuzzy programming method for solving ECM-DWTA problem“. Complex & Intelligent Systems, 25.04.2022. http://dx.doi.org/10.1007/s40747-022-00730-9.

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AbstractElectronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

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Li, Peiyue, Zhijie Wang, Jiahui Jin und Guibing Li. „Two-Stage Dynamic Alliance Bidding for Incremental Distribution and Retail Companies Based on Complex Network Theory“. Frontiers in Energy Research 10 (09.08.2022). http://dx.doi.org/10.3389/fenrg.2022.924106.

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With the increase in renewable energy penetration, energy deviation settlement penalties are imposed on incremental distribution and retail companies (IDRCs). Most IDRCs are at financial risk. Given this background, a bidding model of the two-stage dynamic alliance is proposed to maximize IDRCs’ profits. In the first stage, potential alliances of IDRCs are established by the gravity model. In the second stage, the bi-level bidding model is modeled in energy and primary frequency regulation markets, where the upper level maximizes alliance profits by considering the cooperation costs and alliance constraints, and the lower level simulates market clearing. Then, the bi-level model is transformed into a mixed-integer linear programming model by the strong duality theory, Karush–Kuhn–Tucker conditions, and large M method. Verified by the complex network theory, results show that a complex alliance network has a high transmission efficiency and capacity to resist risk. Moreover, IDRCs with large capacity and superior location are important in the alliance. In addition, the proposed model increases the satisfaction rate by 20%, which increases IDRCs’ profits and reduces cooperation costs.

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Saghian, Zahra, Akbar Esfahanipour und Behrooz Karimi. „A novel dynamic fare pricing model based on fuzzy bi-level programming for subway systems with heterogeneous passengers“. Computers & Industrial Engineering, September 2022, 108654. http://dx.doi.org/10.1016/j.cie.2022.108654.

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Ahwazian, Amin, Atefeh Amindoust, Reza Tavakkoli-Moghaddam und Mehrdad Nikbakht. „A mathematical tri-level programming model for designing an integrated dynamic petroleum product supply chain“. Journal of Advances in Management Research, 11.04.2022. http://dx.doi.org/10.1108/jamr-08-2021-0285.

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PurposeThe purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.Design/methodology/approachThis paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.FindingsThe concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.Research limitations/implicationsThis paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.Practical implicationsThe designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.Originality/valueThis paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.

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Asgari, Farid, Fariborz Jolai und Farzad Movahedisobhani. „An integrated mathematical model of dynamic production and maintenance planning in pumped-storage hydroelectricity“. Journal of Modelling in Management ahead-of-print, ahead-of-print (12.07.2021). http://dx.doi.org/10.1108/jm2-10-2020-0264.

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Purpose Pumped-storage hydroelectricity (PSH) is considered as an effective method to moderate the difference in demand and supply of electricity. This study aims to understanding of the high capacity of energy production, storage and permanent exploitation has been the prominent feature of pumped-storage hydroelectricity. Design/methodology/approach In this paper, the optimization of energy production and maintenance costs in one of the large Iranian PSH has been discussed. Hence, a mathematical model mixed integer nonlinear programming developed in this area. Minimizing the difference in supply and demand in the energy production network to multiple energies has been exploited to optimal attainment scheme. To evaluate the model, exact solution CPLEX and to solve the proposed programming model, the efficient metaheuristics are utilized by the tuned parameters achieved from the Taguchi approach. Further analysis of the parameters of the problem is conducted to verify the model behavior in various test problems. Findings The results of this paper have shown that the meta-heuristic algorithm has been done in a suitable time, despite the approximation of the optimal answer, and the consequences of research indicate that the model proposed in the studied power plant is applicable. Originality/value In pumped-storage hydroelectricity plants, one of the main challenges in energy production issues is the development of production, maintenance and repair scheduling concepts that improves plant efficiency. To evaluate the mathematical model presented, exact solution CPLEX and to solve the proposed bi-objective mixed-integer linear programming model, set of efficient metaheuristics are used. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7%–12% and effective optimization processes.

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Pandey, Yudhishthir, Naimul Hasan, Mohammed Aslam Husain, Ahmad Neyaz Khan, Farhad Ilahi Bakhsh, Ahmad Faiz Minai und Md Tabrez. „An Environment Friendly Energy-Saving Dispatch Using Mixed Integer Linear Programming Relaxation in the Smart Grid with Renewable Energy Sources“. Distributed Generation & Alternative Energy Journal, 06.05.2022. http://dx.doi.org/10.13052/dgaej2156-3306.37414.

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Electrical energy demand has risen over the world which results in developmentof smart grid. Smart grid has identified the areas that requiresimprovement. Because of the focus on cost-effective operation as well asenvironmental concerns in the electrical power system, the complexity ofthe optimization function has increased. In this study, a new energy-saving dispatch model is developed, which takes into account renewable energysources in the smart grid as well as dynamic generation-load interaction.Moreover, the active demand response idea is employed for interruptibleloads during peak demand. During off-peak load periods and compensatingloads, the proposed energy-saving dispatch system operates on a bi-leveldispatch system. Lower level dispatch works with four dispatch functionssuch as interruptible load cost, compensation load cost, renewable energysource cost, and emission saving cost. Whereas, upper level dispatch dealswith cost functions for operation and emission. Renewable energy sources arerepresented as a generating unit as well as a load based on usage in this work.Linear programming relaxation and mixed integer linear programming relaxationmethodologies are used to solve the constrained optimization problem.The outcomes of the experiments were compared with existing methodologiessuch as the classic NSGA-II and the improved NSGA-II. Furthermore,the algorithm’s time complexity was examined. The proposed solution hasbeen implemented using the IEEE-30 bus standard. The performance resultsdemonstrate considerable reductions in operating costs as well as reductionsin emissions.

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Rabbani, Masoud, Soroush Aghamohamadi Bosjin und Neda Manavizadeh. „Leagile supply chain network design through a dynamic two-phase optimization in view of Order Penetration Point“. RAIRO - Operations Research, 27.04.2020. http://dx.doi.org/10.1051/ro/2020041.

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In the contemporary world, combining the concept of agile and lean manufacturing (LM) is one of the most strategic and appealing concerns in the industrial environments. In this paper, a new Leagile structure is proposed for a supply chain. This research covers long term and mid-term horizon by designing a supply chain network up to the order penetration point (OPP) and final assembly and sale planning respectively. The problem is programmed in two phases. First, a bi-objective optimization is developed to minimize the total cost related with LM. In the second phase, the total cost and the customer service level (CSL) are considered as the agile manufacturing (AM) architecture. In the proposed model, a utility function is applied to set balance between the price and customer satisfaction. In addition, a robust credibility-based fuzzy programming (RCFP) is developed to handle uncertainty of the first phase. The proposed model and the solution method are implemented for a real industrial case study to show the applicability and usefulness of this study. According to the results, improving the customer service level can enhance the total cost of the second phase meaning that customer responsiveness price is too high for the proposed system.

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Chen, Yifan, Ziyuan Gu, Nan Zheng und Hai L. Vu. „Optimal coordinated congestion pricing for multiple regions: a surrogate-based approach“. Transportation, 20.06.2023. http://dx.doi.org/10.1007/s11116-023-10400-5.

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AbstractCongestion pricing is one of the efficient travel demand management strategies. Many existing researches focus on dealing with the toll optimization problem for a single area. However, the urban network is often composed of several administrative regions. Furthermore, even inside a single administrative region, there may be multiple subnetworks with different traffic dynamics. As a result, the centric pricing scheme may not be applicable. This paper aims to design a coordinated dynamic pricing scheme for such a scenario with multiple adjacent areas which experience an overlapping congested period. Unlike the traditional approach centered on the bi-level mathematical programming, we adopt the regressing Kriging model to estimate the input–output mapping, thus searching for the simulation-based optimal solution in the toll design problem. Two types of coordinated pricing schemes are proposed. The first or unconstrained scheme only focuses on the network performance, while the second or constrained scheme further takes into account the pricing efficiency. The proposed coordinated pricing scheme is further compared with the perimeter control. The results indicate that our scheme is more moderate without imposing traffic burden on the links/corridors heading to protected zones.

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Pourmoradnasseri, Mozhgan, Kaveh Khoshkhah und Amnir Hadachi. „Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation“. IET Smart Cities, 31.10.2023. http://dx.doi.org/10.1049/smc2.12071.

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AbstractThe emergence of smart cities is set to transform transportation systems by leveraging real‐time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real‐time traffic conditions. A framework for near‐real‐time city‐scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi‐level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high‐dimensional real‐time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.

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Li, Hui, Shuxin Li, Xu Zhang, Pei Tong und Yahui Guo. „Dynamic Signal priority of the self-driving bus at an isolated intersection considering private vehicles“. Scientific Reports 13, Nr. 1 (14.10.2023). http://dx.doi.org/10.1038/s41598-023-44864-3.

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AbstractThe transit signal priority leads to the delay of private vehicles in the priority and non-priority phases. To address this problem, a bi-level programming model is proposed based on the dynamic cycle and arrival rate of private vehicles under connected environment. The upper model is built by a delay triangle, with the maximum delay reduction of private vehicles between the decreased delay and increased delay in the experimental period. The lower model is constructed based on the Stackelberg model of game theory, and the objective is to obtain the dynamic cycle. A genetic algorithm (GA) is implemented to solve the proposed model. Based on SUMO, a case study of a self-driving bus in the city of Zhengzhou is conducted to demonstrate the effectiveness of the proposed model. The results from GA and SUMO are consistent, which verifies the effectiveness of the proposed model. The delay of the private vehicles with dynamic signal priority declines by 21.32% on average compared to that without priority. Compared with active signal priority, it declines by 22.63% on average. The proposed method is compared with the method proposed by other papers, and the delay per private vehicle is small. The effectiveness of the proposed method is further illustrated. The proposed methodology is helpful for improving the operation efficiency of intersections with minimum delay.

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Liu, Xuecheng, und Libao Shi. „A dynamic game model for assessing risk of coordinated physical-cyber attacks in an AC/DC hybrid transmission system“. Frontiers in Energy Research 10 (23.01.2023). http://dx.doi.org/10.3389/fenrg.2022.1082442.

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The widely used intelligent measuring equipment not only makes the operation of AC/DC hybrid transmission system more safe and reliable, but also inevitably brings new problems and challenges such as the threats and hidden dangers of cyber attacks. Given this, how to effectively and comprehensively assess the inherent vulnerabilities of AC/DC hybrid transmission systems under the coordinated physical-cyber attacks is of critical significance. In this paper, a three-stage physical-cyber attack and defense risk assessment framework based on dynamic game theory is proposed. In the framework, the dynamic game process between attacker and defender is carried out for the power grid risk, which is expressed as the product of the attacker’s success probability in attacking the substation and the load loss caused by the attack. Regarding the probability of a successful attack, it depends on the number of funds invested by both attacker and defender sides considering the marginal effect, while the corresponding load loss caused depends on the cyber attack vector and the optimal load shedding scheme. For the solution of the proposed three-stage dynamic game framework, it is converted into a bi-level mathematical programming problem, in which the upper-level problem is solved by using the backward induction method to get the subgame perfect Nash equilibrium, and the lower-level problem is solved by using an improved particle swarm optimization algorithm to get the optimal amount of load shedding. Finally, the case study is performed on a modified IEEE 14-node AC/DC hybrid transmission test system, and the inherent weaknesses of the power grid are identified based on the risk assessment results, verifying the effectiveness of the proposed framework and method.

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