Abstract
A meta-heuristic algorithm called, the cuckoo search algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature to show the performance of the proposed model; in addition, the results have been compared to those achieved by the bee colony optimization algorithm and genetic algorithm. Those obtained results indicate that the proposed cuckoo search algorithm is able to get better Pareto solutions (non-dominated set) for the supply chain problem.
Highlights
Nowadays, the complexity of the business environment is rapidly increasing due to several factors such as the expansion of the market, a wide range of suppliers, increased competition and customers demands on the performance of a companies that requires to continuously evaluate, configure their Supply Chains (SCs) and provide customers with high-quality products/ services at the lowest cost within the shortest time.According to this work, minimization the total cost (TC) and delivery lead time (TLT) are considered the two main objectives for multi-objective supply chain problem
Simulation experiment applied for testing the algorithm performance using MATLAB programming tool for a multi-objective supply chain case study by personal computer Pentium(R) Dual-Core CPU T4440 @ 2.20GHz, 2 GB of RAM
This paper proposed and applied Cuckoo search algorithm to solve multi-objective supply chain problem
Summary
The complexity of the business environment is rapidly increasing due to several factors such as the expansion of the market, a wide range of suppliers, increased competition and customers demands on the performance of a companies that requires to continuously evaluate, configure their Supply Chains (SCs) and provide customers with high-quality products/ services at the lowest cost within the shortest time. Minimization the total cost (TC) and delivery lead time (TLT) are considered the two main objectives for multi-objective supply chain problem. Many researchers use various objectives for multi-objective supply chain problem such as minimize total cost, minimize lead time, minimize environmental impact, maximize profit, maximize service level, etc. There are different types of optimization techniques developed and improved for solving multi-objective optimization (MOO) problems. Yuce et al [19] improved Bees Algorithm to reduce TLT and TC and find the optimal solution for the given supply chain problem. Zhao et al.[20] applied ACO algorithm for optimization SC design with a different business environment, and different customer demands to reduce total cost and total lead-times. Monkayo Martinez and Chang[22] designed SC to provide a satisfactory level of service to customers with minimizing total cost of SC
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