A Hybrid metaheuristic algorithm based on tabu search, simulated annealing and neighborhood for the optimal design of multi-objective supply chain systems

Recently, attention has been paid to the design of multi-objective supply chain problems using meta-heuristics among practitioners and academics. This attention is indicated by the number of studies published in this field. However, there are still gaps in the development of efficient algorithms for the design of supply chain problems in the case of multi-objective. This research aims to develop a hybrid, effective metaheuristic algorithm based on a tabu search algorithm, simulated annealing algorithm and neighborhood algorithm to design a multi-objective supply chain design problem. In this study, the characteristics of the three algorithms will be first explored. Then the effective properties of each algorithm are used to develop the new hybrid algorithm. The developed algorithm will be designed, encoded, fine-tuned, and evaluated. In addition, an existing metaheuristic algorithm and another exact method will be used to validate the results of the proposed algorithm. A typical multi-objective supply chain model will be used for evaluating the performance of the developed algorithm. A well-designed study will be used to compare the algorithms performance using several performance metrics