Cost-Optimization Problem with a Soft Time Window Based on an Improved Fuzzy Genetic Algorithm for Fresh Food Distribution

Given the increasing demand for fresh food quality, fresh food plants must manage not only product cost but more importantly the product quality. The transportation requirements for fresh food delivery have been continuously increasing. The purpose of this paper is to develop a method to ensure that fresh food can be delivered just in time and with minimum total cost while maintaining the quality of fresh food. Considering that fresh food plants need multiple trucks to deliver multiple products to numerous geographically dispersed customers, the delivery of fresh food is considered in two stages in our study. The first stage is cluster consumers; that is, we determine to which consumers each truck is responsible for delivery. The second stage, which is based on the consumer grouping results, develops a total cost model that includes the transportation, refrigerated, devalued, and penalty costs incurred during distribution. This model is used to determine the optimal route selection, the temperature control, and the average speed of each truck in distribution. This paper designs decision variables based on a customer’s seven requirement attributes; it also proposes a fuzzy clustering method for grouping customers and improves a fuzzy genetic algorithm that is used to solve the proposed total cost model. The application of the proposed method is demonstrated using an example. The experimental results show that the proposed method has better performance than that of a traditional genetic algorithm. This research work provides an optimal distribution total cost decision method for the logistics managers. This research also provides an effective means to ensure the safety of fresh food.