Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches

This paper studies a multi-objective production–distribution system. the objectives are to minimize total costs and maximize the reliability of transportations system. each transportation system is assumed to be of unique reliability. in the real world, some parameters be of vagueness; therefore, some tools such as fuzzy logic is applied to tackle with. the problem is formulated using a mixed integer programming model. commercial software can optimally solve small sized instances. we propose two novel heuristics called ranking genetic algorithm (rga) and concessive variable neighborhood search (cvns) in order to solve the large sized instances. rga utilizes various crossover operators and compares their performances so that better crossover operators are used during the solution process. cvns applies several neighborhood search structures with a novel learning procedure. the heuristics can recognize which neighborhood structure performs well and applies those more than the others. the results indicated that rga is of higher performance.