A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.