A genetic algorithm to vehicle routing problem in reverse logistics

Recently, the increasing focus on environmental protection has led to significant change in logistics processes. Reverse logistics begin to be paid more attention by researchers. In reverse logistics, the customers who have both a pick-up and a delivery demand are serviced with a single stop. In this paper, the reverse logistics problem of simultaneously distributing commodities and collecting re-usable empty packages with a single depot and a fleet of uniform vehicles is studied. Commodities to be distributed are loaded at the depot and delivered to the customers. The empty packages are collected from the customers and transported back to the depot. The objective is to minimize the total distance traveled by all vehicles while servicing all customers. After a simple description of the problem, the vehicle routing problems in reverse logistics. is investigated. a mathematic model of the problem is built. Then, a genetic algorithm to the problem is suggested. In this algorithm, the ranking replacement method is used, and the population is partitioned into four subsets with respect to the fitness and unfitness of the offspring. Finally, the proposed genetic algorithm is successfully applied to some problems, and the superiority of the genetic algorithm is proved through the comparison of the genetic algorithms with a heuristic algorithm.