DESIGNING A THREE LAYER SUPPLY CHAIN CONSIDERING DIFFERENT TRANSPORTATION CHANNELS AND DELIVERY TIME DEPENDENT DEMAND
DOI:
https://doi.org/10.23055/ijietap.2018.25.3.3451Keywords:
Keywords, Supply Chain Design, Delivery Time Dependent Demand, Transportation Channel, Discount, Genetic Algorithm, variable Neighborhood SearchAbstract
The present study is devoted to design a three layer supply chain including a number of plants, distribution centers and customers demanding for multiple products. Delivery time and cost are two important factors considered in designing the supply chain; nevertheless, it is hard to compare these factors due to their different natures. In the real world, delivery lags may cause serious loss in customer’s demand. Based on this fact, the current study assumes the customers’ demand to be dependent to the delivery time; thus, faster delivery will result in higher sales and incomes in the supply chain. A model is proposed in which the objective function is to maximize the profit calculated by subtracting costs from incomes. In this model, time and cost factors are merged into the addressed objective. To reflect the real world situations, some distribution channels with different transportation times and costs have been considered between each pair of distribution centers and customers. The required times for delivering products to customers are dependent on the choice of distribution centers. Moreover, in the real world, many logistic companies usually provide discounts for high volume shipments to attract more customers. In this model, some discounts have been considered on the transportation costs for the different transportation channels. The decision variables of the problem are the location of distribution centers, the selection of transportation channels and the amount of products transferred between plants and distribution centers. Two heuristic algorithms based on genetic algorithm and variable neighborhood search are developed to solve the problem. Each heuristic divides the problem variables into two sub-sets and determines their values recursively. The numerical results show that the genetic algorithm based heuristic generates solutions of higher quality while the other heuristic has shorter computation time.
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