A Multiobjective Optimization Approach for Integrated Production Planning, Location-Allocation, and Routing in Three-Echelon Supply Chains

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Zolfaghari Razaji Bahareh; Varmazyar Mohsen; Fallahi Ali

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Journal Paper

Paper Information

Journal: Advances in Industrial Engineering Year:2024 | Volume:58 | Issue:2 Page(s): 307-324

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Title

A Multiobjective Optimization Approach for Integrated Production Planning, Location-Allocation, and Routing in Three-Echelon Supply Chains

Author(s)

Zolfaghari Razaji Bahareh | Varmazyar Mohsen | Fallahi Ali | Issue Writer Certificate

Keywords

Supply chain planning

Production Planning

Vehicle routing problem

location-allocation analysis

Multiobjective Optimization

Abstract

The focus of many researchers in the operations research field has been on supply chain design problems during past decades. Previous works have widely investigated production-inventory planning, vehicle routing, and location-allocation problems. This paper aims to consider these problems simultaneously and present a new integrated Production Planning-location-routing problem for a three-echelon supply chain, considering several real-world assumptions. The studied supply chain includes multiple production centers, distribution centers, and customers. The distribution centers use a set of non-homogeneous vehicles to deliver the products to the customers. Several features, such as regular and overtime production, production reliability, time-window constraints, and capacity constraints, are incorporated to provide a more realistic problem. The bi-objective model aims to determine the optimal location, allocation, production, and routing decisions to optimize the total cost and servicing time objective functions. Concerning problem's complexity, the non-dominated sorting genetic algorithm-II (NSGA-II) is designed and implemented as a solution approach. The results reveal that this algorithm can solve the model in an acceptable time interval. In addition, the results demonstrate that the NSGA-II algorithm is reliable in finding solutions, and there is no significant difference between the average solution and the best solution of the algorithm in several runs.

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