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An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem

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JOURNAL OF CLEANER PRODUCTION 2019年 235卷 1499-1514页

作者： Mardan, Ehsan Govindan, Kannan Mina, Hassan Gholami-Zanjani, Seyed Mohammad Semnan Univ Coll Engn Sch Ind Engn Semnan Iran Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Recently, social awareness, governmental legislations and competitive business environment have spurred researchers to pay much attention to closed-loop supply chain network design. In order to support the arising trend, this paper presents a comprehensive mathematical model for a multi-period, multi-product, multi-modal and bi-objective green closed-loop supply chain. The objective of the model is to minimize the total cost and environmental emissions through making the best decisions on facility location, transportation amounts and inventory balances. According to the inherent complexity of the problem and considering multi-product, multi-period and multi-modality assumptions makes it hard to handle, and as for the solution approach, an effective accelerated benders decomposition algorithm is implemented. Then, computational results for a set of numerical example are discussed. Besides, the model and solution approach are applied on a wire-and-cable industry. Then, a sensitivity analysis is implemented in an effort to validate the model. Results reveal applicability of the proposed mathematical model and presented solution approach. Following the obtained results, it can be validly concluded that the suggested solution approach leads to more than 13 percent reduction in total cost for the studied case, and can be even employed for larger and more complex real-world industrial applications. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain Green networks Network design Multi-objective optimization benders decomposition algorithm

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A two-stage robust hub location problem with accelerated benders decomposition algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第17期60卷 5235-5257页

作者： Rahmati, Reza Bashiri, Mahdi Nikzad, Erfaneh Siadat, Ali Shahed Univ Dept Ind Engn Tehran Iran Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry W Midlands England Coventry Univ Ctr Business Soc Coventry W Midlands England Univ Lorraine HESAM Univ Arts & Metiers Inst Technol LCFC Metz France

In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand is uncertain and the level of conservatism is controlled by an uncertainty budget. In the first stage, locations for establishing hub facilities were determined, and allocation decisions were made in the second stage. An accelerated benders decomposition algorithm was used to solve the problem. Computational experiments showed better results in terms of number of iterations and computation time for benders decomposition with Pareto-optimal cuts in comparison with the classical benders decomposition algorithm. According to numerical analysis, it was concluded that increasing the uncertainty budget also increased total costs for more established hubs. To determine the uncertainty budget in an appropriate manner, a new expected aggregate function was introduced. The numerical studies demonstrated the usefulness of the proposed method in defining the appropriate uncertainty budget in the presence of uncertainty.

关键词： Two-stage robust hub location problem demand uncertainty benders decomposition algorithm Pareto-optimal cut

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An accelerated benders decomposition algorithm for the integrated storage space assignment, berth allocation, and yard crane deployment problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Soroushnia, Golsa Alinaghian, Mahdi Malekahmadi, Amirsalar Isfahan Univ Technol Dept Ind & Syst Engn Esfahan Iran Dalhousie Univ Dept Ind Engn Halifax NS Canada

In this paper, storage space assignment problem, berth allocation problem, and yard crane deployment problem are integrated with due attention to traffic congestion in the passing lines of storage yards. The paper provides a mixed-integer programming model for the integrated problem with the objective of minimizing the movement and operating costs of yard cranes, the cost of moving container flow between the storage yard and berths, and the delay cost of vessels. The planning horizon is considered to be one day and the berth layout is assumed to be discrete. Since the integrated storage space assignment and yard crane deployment problem is NP-hard and so is berth allocation problem, the integration of these three problems will be at least equally complex. Thus, an Accelerated benders decomposition algorithm is also developed for solving the integrated problem. This is done by introducing a combinatorial benders cut to the classical benders decomposition algorithm to improve its performance and also adding 8 valid inequalities derived from the problem properties and assumptions to the algorithm formulation to accelerate the solution process. The proposed mathematical model and solution approach are evaluated by generating instances of different sizes for the problem and assessing the solution results. The results show the good performance of the proposed accelerated benders decomposition algorithm. Taking the integrated approach to the modeling of the three problems rather than the non-integrated approach improved the cost of terminal operations by 11.15% on average.

关键词： Logistics Storage space assignment problem Berth allocation problem Yard crane deployment benders decomposition algorithm

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An integrated (1, T) inventory policy and vehicle routing problem under uncertainty: an accelerated benders decomposition algorithm

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2021年第2期13卷 104-124页

作者： Nikfarjam, Adel Moosavi, Amirhossein

Inventory control and logistics are both important to the success of a supply chain. These problems become highly complicated when the demand of end-users is uncertain. The demand uncertainty could have a considerable destructive effect on a supply chain, e.g. bullwhip effect. Therefore, this paper proposes a two-stage approach to study an inventory-routing problem for a three-echelon single-item supply chain, consisting of suppliers, manufacturers, distributors, and wholesalers. The approach utilizes the ordering policy in the first stage to determine the optimal replenishment ordering quantity of wholesalers under uncertainty. This stage aims to minimize the inventory holding and lost-sales costs. To the best of our knowledge, this is the first application of the policy to an inventory-routing problem. Afterward, a mathematical formulation is proposed in the second stage to study a new vehicle routing problem. This model minimizes the transportation cost. Based on the literature, the vehicle routing problem is NP-hard;therefore, a new accelerated benders decomposition algorithm is developed to solve large-scale instances of the problem. The algorithm incorporates a modified -optimality accelerator. The computational results show the superior performance of the modified accelerator compared to the conventional one. To evaluate the performance of the accelerated Bender decomposition algorithm, we compare it with the mathematical programming and a Genetic algorithm using twenty benchmark examples. We also introduce and assess a real-case study in the automotive parts industry. Finally, we analyze some key features of the case study to provide managerial implications.

关键词： (1 T) Inventory Policy Bullwhip Effect Vehicle Routing Problem Uncertainty benders decomposition algorithm Genetic algorithm Automotive Parts Industry

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A benders decomposition algorithm for the multi-mode resource-constrained multi-project scheduling problem with uncertainty

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ANNALS OF OPERATIONS RESEARCH 2024年第3期339卷 1637-1677页

作者： Sadeghloo, Maryam Emami, Saeed Divsalar, Ali Babol Noshirvani Univ Technol Dept Ind Engn Babol *** Iran

In this research, we focus on the multi-mode resource-constrained multi-project scheduling problem from the perspective of consultants who attend design-bid-build contracts and put their efforts to optimize the client and contractors' profits simultaneously. Each contractor may have a different level of knowledge and experience to perform a project. Therefore, the contractor selection to execute a project may affect the quality and satisfaction level of the project. This study aims to schedule activities of a certain number of projects with multiple execution modes and pre-specified relations. A multi-objective mixed-integer linear programming model is proposed to maximize satisfaction in contractor selection, minimize the total cost of projects and earliness and tardiness penalties, and minimize the total completion time of projects subject to resource constraints. It is assumed that activity durations are uncertain;thereby the globalized robust counterpart of the formulation is presented such that the normal range of the perturbation is the intersection of a box and a polyhedral. The proposed multi-objective model is solved by the multi-choice goal programming approach. This problem is computationally complex and the benders decomposition (BD) method is utilized to solve it. Numerical experiments on different instances prove the quality of the proposed BD method and illustrate that the proposed model is capable of selecting a proper contractor and multi-project scheduling.

关键词： Multi-mode multi-project scheduling Multi-choice goal programming Globalized robust counterpart benders decomposition algorithm

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Enhancing benders decomposition algorithm to solve a combat logistics problem

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OR SPECTRUM 2020年第1期42卷 161-198页

作者： Marufuzzaman, Mohammad Nur, Farjana Bednar, Amy E. Cowan, Mark Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39762 USA US Army Engineer Res & Dev Ctr Informat Technol Lab Vicksburg MS 39180 USA

This paper proposes a multi-time period, two-stage stochastic programming model for the design and management of a typical combat logistics problem. The design shall minimize the total path setup cost, commodity preposition and processing costs, and expected transportation, storage, and shortage costs across all possible path failure scenarios. Due to the complexity associated with solving the model, we propose an accelerated benders decomposition algorithm to solve the model in a realistic-size network problem within a reasonable amount of time. The benders decomposition algorithm incorporates several algorithmic improvements such as pareto-optimal cuts, multi-cuts, knapsack inequalities, integer cuts, input ordering, mean-value cuts, and the rolling horizon heuristic. Computational experiments are performed to assess the efficiency of different enhancement techniques within the benders decomposition algorithm.

关键词： Combat logistics benders decomposition algorithm Input ordering Multi-cut Mean-value cut

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A Closest benders Cut Selection Scheme for Accelerating the benders decomposition algorithm

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INFORMS JOURNAL ON COMPUTING 2022年第5期34卷 2804-2827页

作者： Seo, Kiho Joung, Seulgi Lee, Chungmok Park, Sungsoo Korea Adv Inst Sci & Technol KAIST Dept Ind & Syst Engn Daejeon South Korea Chonnam Natl Univ Dept Ind Engn Gwangju South Korea Hankuk Univ Foreign Studies Dept Ind & Management Engn Youngin Si South Korea

The benders decomposition algorithm often shows poor convergence. To improve the convergence of the benders decomposition algorithm. Recently, it was proposed the use of feasibility cuts closest to a solution in the set defined by all feasibility cuts. We extend this feasibility cut selection scheme to a new cut selection scheme for optimality cuts and propose a new benders separation framework that a single linear programming problem can solve. We show that optimality cuts generated by this scheme are Pareto optimal when some conditions are satisfied. Theoretical connections to the existing benders cut generation methods are also identified. Extensive computational experiments on the multiple classes of benchmark problems demonstrate that the proposed algorithm improves the convergence speed and computational time.

关键词： benders decomposition algorithm optimality cut fractional programming problem Pareto-optimal cut multicommodity capacitated fixed-charge network design problem

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A two-stage approach for the multi-floor facility layout problem: benders decomposition algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2021年第2期36卷 953-969页

作者： Karateke, Huseyin Sahin, Ramazan Gazi Univ Fac Engn Ind Engn Dept TR-06570 Ankara Turkey

Today, the use of multi-floor facilities is quite common due to the high land costs and limited areas. However, the locations of the departments on the floor and floor to which departments are assigned become important when considering the total material handling costs, distance traveled and employee movements. The problem where the departments are assigned to which floor and their locations within the floor are determined is called as a multi-floor facility layout problem (MFLP). Different decision variables such as the number of elevators, the location of elevator, the number of floors, and the number of departments increase the complexity of the MFLP. In this study, a two-stage approach is proposed for the solution of MFLP. In the first stage of the proposed approach, the departments are assigned to floors, while in the second stage, the benders decomposition algorithm is used to determine the locations of departments within the floor in a way to minimize the total material handling costs between the departments. The contribution of the study to the literature is that it is the first study which employs the benders decomposition algorithm to solve the MFLP. The proposed benders decomposition algorithm was tested on five problems taken from the literature. In addition, in this paper, three large-scale problems adapted as MFLP were solved. When the results are analyzed, it is seen that the proposed benders decomposition algorithm was conducted cost improvements ranging 0.12% to 7.64% for five problems taken from the literature.

关键词： Facility layout problem multi-floor facility layout problem benders decomposition algorithm

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An effective benders decomposition algorithm for solving the distributed permutation flowshop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 123卷 105006-105006页

作者： Hamzadayi, Alper Van Yuzuncu Yil Univ Dept Ind Engn TR-65080 Van Turkey

In today's centralized globalized economy, large manufacturing firms operate more than one production center. Therefore, the multifactory production scheduling environment, so-called the distributed scheduling problem, is gaining more and more attention from the authors. In this context, which factory will manufacture which product is an important decision making process. The distributed permutation flowshop scheduling problem (DPFSP) provided with real life applications has attracted attention of the researchers for nearly one decade as one of the special cases of the distributed scheduling problem. In the current literature, approximation methods have been intensely used for solving the DPFSP and only one paper containing the exact solution methods has been published to solve this problem. In this paper, the best mathematical formulations available in the current literature has been further improved and traditional and hybrid benders decomposition algorithms are presented through the proposed new mathematical model. The developed new model is a position based model intended for restricting the domains of decision variables and assigning jobs to sequential positions in the related decision variables. The proposed hybrid benders decomposition algorithm consists of the hybridization of NEH2_en local search algorithm, a mathematical model to find the upper bound for the number of positions used in the related decision variables, the LS3 algorithm, with the benders decomposition algorithms. The new and best exact methods available in the literature are compared with each other by using the benchmark data sets and the experimental results showed that the new exact methods developed in this paper are superior to the existing exact methods in all aspects. In this paper, 18 new best solutions are founded for the DPFSP. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Distributed flowshop problem Mixed integer linear programming benders decomposition algorithm NEH2_en algorithm LS3 algorithm

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Blood supply chain configuration and optimization under the COVID-19 using benders decomposition based heuristic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第2期63卷 571-593页

作者： Abdolazimi, Omid Pishvaee, Mir Saman Shafiee, Mohammad Shishebori, Davood Ma, Junfeng Entezari, Sarah Mississippi State Univ Dept Ind & Syst Engn Starkville MS USA Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Yazd Univ Dept Ind Engn Yazd Iran 260K McCain Mississippi State Starkville MS 39762 USA

During COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient benders decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters;consequently, several managerial insights have been derived.

关键词： Supply chain management COVID-19 Heuristic/meta-heuristic algorithms benders decomposition algorithm

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