Multiple Cross-docks Scheduling with Multiple Doors using Fuzzy Approach and Metaheuristic Algorithms

doi:10.1007/s40305-021-00362-9

Abstract

Abstract: The issue of supply chain in today’s world is a major competitive advantage in reducing costs. Supply chain includes procurement, logistics and transportation, marketing, organizational behavior, networking, strategic management, information systems management and operations management. One of the most important practices in logistics is cross-docking which sets its goals as inventory reduction and customer satisfaction increase. Customers receive goods through docks. Docks are responsible to provide a place for goods before being delivered to the customers. Then, these materials are directly loaded into outbound trucks with little or no storage in between to send to customers in the shortest possible time. This paper is mainly aimed at introducing a mixed integer linear programming model to solve scheduling several cross-docking problems. The proposed model is highly facilitated to allocate the optimal destinations to storage doors and truck scheduling in docks while selecting the collection and delivery routes. Using optimization approaches at uncertainty conditions is also of great importance. Mathematical programming techniques vividly fail to solve transportation problems that include fuzzy objective function coefficients. A fuzzy multi-objective linear programming model is proposed to solve the transportation decision-making with fuzzy objective function coefficients in this paper. On the other hand, the existences of computational complexities lead this model to be categorized as a NP-Hard one. Therefore, we applied metaheuristic algorithms such as genetic and ant colony in order to solve our proposed problem.

Key words: Cross-docks scheduling, Fuzzy logic, Genetic and ant colony metaheuristic

CLC Number:

90B06

Mitra Movassaghi, Soroush Avakh Darestani. Multiple Cross-docks Scheduling with Multiple Doors using Fuzzy Approach and Metaheuristic Algorithms[J]. Journal of the Operations Research Society of China, 2022, 10(4): 861-911.

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