A Hybrid Heuristic Algorithm to Provide a Multi-Objective Fuzzy Supply Chain Model with a Passive Defense Approach

‎‎‎In this paper‎, ‎a stable multi-objective model of location‎, ‎inventory‎, ‎and supply chain routing is presented under conditions of uncertainty and using a passive defense approach‎. ‎Parameters such as demand‎, ‎cost of setting up the facility and cost of maintaining inventory are considered uncertain and in the form of triangular fuzzy numbers‎.‎ ‎Also‎, ‎in order to increase supply chain resilience‎, ‎the characteristics and capabilities of passive defense in the supply chain‎, ‎such as ``ready flow rate''‎, ‎``security of backup routes''‎, ‎``possibility of deployment of resources and equipment''‎, ‎and ``the principle of dispersion for location'' are considered‎. ‎Multipurpose‎, ‎multipartite algorithms‎, ‎based on the Pareto archive and genetic algorithm‎, ‎are used to solve the model‎. ‎‎The results of validation show that the proposed model is valid and feasible‎, ‎and the proposed algorithm is also valid and converges to the optimal solution. ‎Sample problems‎, ‎in three groups of small‎, ‎medium and large‎, ‎are solved by two algorithms‎, ‎and the results are compared based on quality‎, ‎dispersion‎, ‎uniformity and execution time‎.‎ ‎The results of this section show that in all cases‎, ‎the multi-objective particle mass algorithm has a higher ability than the GA to produce solutions of higher quality and to explore and extract the scalable area of‎ ​‎​the solution. ‎Also‎, ‎the comparison of the execution times of the algorithms indicates that the multi-objective particle mass algorithm has a higher solution time.