Simulated Annealing for the Traveling Purchaser Problem in Cold Chain Logistics

Transportation of perishable food in cold chain logistics systems is crucial in order to preserve the freshness of the products. Due to the extended traveling times and frequent stops, planning the distribution operations in cold chain logistics plays a vital role in minimizing the deterioration cost of the products. In order to minimize the total cost of cold chain logistics activities related to the purchase of perishable products, the route and procurement operations have to be well-planned. In this context, this paper addresses the well-known traveling purchaser problem (TPP) and extends the TPP by considering the procurement of perishable products. This is called the traveling purchaser problem in cold chain logistics (TPP-CCL). In the TPP-CCL, the demand for a number of perishable products is provided from a number of markets, where the products purchased at markets are transported by a temperature-controlled vehicle. In addition to the transportation and procurement cost, the deterioration cost of the products is taken into account in the problem. The problem is formulated as a non-linear mixed-integer programming model in which the objective is to find the best procurement and route plan for the purchaser that minimizes the total cost. Considering the complexity of the problem, a simulated annealing (SA) algorithm is proposed to solve the TPP-CCL. The SA is formed by using a number of local search procedures, where the procedures are randomly selected to find a new solution in each iteration. The proposed SA is performed for a TPP-CCL problem set that includes different-sized instances. The results of the SA are compared to the GUROBI solver results. A better result is obtained by the SA for most of the instances. The computational results show that the proposed SA outperforms the GUROBI results by finding better results in shorter computational times.