Showing 5 results for Benders Decomposition
Aliakbar Hasani,
Volume 28, Issue 2 (6-2017)
Abstract
In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the network design and power generation and transmission system via considering failures scenarios, as well as network preventive maintenance schedule, is presented. The aim of the proposed model is to minimize the expected total cost consisting of power plants set-up, power generation and the maintenance activities. The proposed mathematical model is solved by an efficient new accelerated Benders decomposition algorithm. The proposed accelerated Benders decomposition algorithm uses an efficient acceleration mechanism based on the priority method which uses a heuristic algorithm to efficiently cope with computational complexities. A large number of considered scenarios are reduced via using a k-means clustering algorithm to decrease the computational effort for solving the proposed two-stage stochastic programming model. The efficiencies of the proposed model and solution algorithm are examined using data from the Tehran Regional Electric Company. The obtained results indicate that solutions of the stochastic programming are more robust than the obtained solutions provided by a deterministic model.
Arash Khosravi, Seyed Reza Hejazi, Shahab Sadri,
Volume 28, Issue 4 (11-2017)
Abstract
Managing income is a considerable dimension in supply chain management in current economic atmosphere. Real world situation makes it inevitable not to design or redesign supply chain. Redesign will take place as costs increase or new services for customers’ new demands should be provided. Pricing is an important fragment of Supply chain due to two reasons: first, represents revenue based each product and second, based on supply-demand relations enables Supply chain to provide demands by making suitable changes in facilities and their capacities. In this study, Benders decomposition approach used to solve multi-product, multi-echelon and multi-period supply chain network redesign including price-sensitive customers.
Malieheh Ebrahimi, Reza Tavakkoli-Moghaddam, Fariborz Jolai,
Volume 30, Issue 2 (6-2019)
Abstract
Customization is increasing so build-to-order systems are given more attention to researchers and practitioners. This paper presents a new build-to-order supply chain model with multiple objectives that minimize the total cost and lead time, and maximize the quality level. The model is first formulated in a deterministic condition, and then investigated the uncertainty of the cost and quality by the stochastic programming based on the scenario. The return policy and outsourcing are the new issues in a build-to-order supply chain by considering the cost and inventory. A Benders decomposition algorithm is used to solve and validate the model. Finally, the related results are analyzed and compared with the results obtained by CPLEX for deterministic and stochastic models.
Ali Salmasnia, Elahe Heydarnezhad, Hadi Mokhtari,
Volume 35, Issue 2 (6-2024)
Abstract
Abstract. One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.
Khalil Abbal, Mohammed El Amrani, Youssef Benadada,
Volume 36, Issue 1 (3-2025)
Abstract
In this paper, we study the Multi-Level Multi-Capacitated Facility Location Problem (ML-MCLP), which was first introduced in 2022 as a double generalization of the Capacitated P-Median Problem (CPMP). The objective of this problem is to determine the optimal facilities to open at each level, and their appropriate capacities to meet customer demands, while minimizing assignment costs. We adopt the Benders Decomposition exact approach, complemented by modern acceleration techniques to enhance convergence speed. The performance of the accelerated BD algorithm is evaluated using a dataset generated based on justified difficulty criteria and data generation methods from the literature. The results showed that hybridization of acceleration techniques, such as subproblem reformulation and cut selection, significantly improves convergence. However, decomposition-based technique proved to be inefficient, particularly due to the structure of the ML-MCLP, and was therefore excluded.