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Showing 5 results for Robust Optimization
Dr Yahia Zare Mehrjerdi, Mitra Moubed,
Volume 6, Issue 1 (3-2015)
Abstract
This paper proposes a robust model for optimizing collaborative reverse supply chains. The primary idea is to develop a collaborative framework that can achieve the best solutions in the uncertain environment. Firstly, we model the exact problem in the form of a mixed integer nonlinear programming. To regard uncertainty, the robust optimization is employed that searches for an optimum answer with nearly all possible deviations in mind. In order to allow the decision maker to vary the protection level, we used the "budget of uncertainty" approach. To solve the np-hard problem, we suggest a hybrid heuristic algorithm combining dynamic programming, ant colony optimization and tabu search. To confirm the performance of the algorithm, two validity tests are done firstly by comparing with the previously solved problems and next by solving a sample problem with more than 900 combinations of parameters and comparing the results with the nominal case. In conclusion, the results of different combinations and prices of robustness are compared and some directions for future researches are suggested finally.
Mr. M. Namakshenas, Dr. Mir Saman Pishvaee, Dr. M. Mahdavi Mazdeh,
Volume 8, Issue 1 (4-2017)
Abstract
Over five decades have passed since the first wave of robust optimization studies conducted by Soyster and Falk. It is outstanding that real-life applications of robust optimization are still swept aside; there is much more potential for investigating the exact nature of uncertainties to obtain intelligent robust models. For this purpose, in this study, we investigate a more refined description of the uncertain events including (1) event-driven and (2) attribute-driven. Classical methods transform convex programming classes of uncertainty sets. The structural properties of uncertain events are analyzed to obtain a more refined description of the uncertainty polytopes. Hence, tractable robust models with a decent degree of conservatism are introduced to avoid the over-protection induced by classical uncertainty sets.
Mr. Yaser Rouzpeykar , Dr Roya Soltani, Dr Mohammad Ali Afashr Kazemi,
Volume 11, Issue 1 (9-2020)
Abstract
The hub location and revenue management problem are two research topics in the field of network design and transportation. The hub location model designs the structure of the transportation network, while the revenue management model allocates network capacity to different customer categories according to their price sensitivity. Revenue management determines which products to sell to which customers and at what price. On the other hand, due to the limited number of aircraft seats, the revenue management problem has been widely used in the aviation industry. In this study, a robust optimization model is developed for the hub location and revenue management problem. For this purpose, a real-world case study with a central hub and six airports is presented and solved using CPLEX solver in GAMS software. Finally, a sensitivity analysis was performed on the key parameters of the problem, and their effect on the objective functions of the problem was investigated. Results show that the proposed model achieved the feasible solution in reasonable time for real case problem by exact method.
H. Razavi, S.h. Motevalli, S. Emamgholizade, M. Rajaei Litkoohi,
Volume 16, Issue 1 (3-2025)
Abstract
This research proposes a robust multi-objective optimization model for blockchain-enabled smart supply chains under uncertainty. The model integrates forward and reverse logistics while incorporating blockchain transaction efficiency to enhance transparency, traceability, and trust among stakeholders. The objectives include minimizing total costs, reducing carbon emissions, maximizing service levels, and optimizing blockchain-related operations. To address uncertainties in demand and transportation costs, the model employs fuzzy robust optimization techniques, ensuring reliable decision-making. To solve the proposed model, several metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the newly developed Greedy Man Optimization Algorithm (GMOA) are utilized. Comparative analysis demonstrates the superiority of GMOA in achieving high-quality solutions with lower computational time. The results highlight the model’s practical applicability in designing sustainable, transparent, and efficient supply chains. Sensitivity analyses provide managerial insights, emphasizing the impact of key parameters on total costs and operational performance.
Dr. Amir Jalilvand-Nejad,
Volume 17, Issue 1 (5-2026)
Abstract
Coordination is a critical factor in optimizing supply chain performance. Given the pervasive uncertainties in supply chain management, it is essential to develop decisions that are robust against these uncertainties while preserving operational efficiency. This paper aims to determine an optimal supply chain policy that ensures the total system cost remains robust against correlated uncertainties in demand and lead time. To address the correlation among demand data and avoid overly conservative solutions, a novel robust optimization model is proposed based on a correlated polyhedral uncertainty set. This approach explicitly accounts for demand correlation, thereby reducing the price of robustness. Numerical results demonstrate that integrating coordination as a strategic decision and employing robust optimization as a tactical tool significantly enhances supply chain performance. Moreover, incorporating demand correlation in the proposed model leads to a substantial reduction in the price of robustness and, consequently, higher supply chain profitability. Extending this framework to more complex supply chain models with multiple sources of uncertainty holds great potential for further improving the robustness and practical applicability of supply chain decision-making.
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