A new hybrid genetic algorithm to optimize distribution and operational plans for cross-docking satellites.

This paper addresses an integrated material flow optimization problem of cross-docking satellites, in which the transportation problem, the truck-door assignment problem with material placement plans, and the two-dimensional truck loading problem are taken into account. The study aims to find the best distribution and operational plans for the cross-docking satellites to minimize the total transportation cost of the materials. To solve the considered problem, a hybrid genetic algorithm (HGA) is developed, which integrates simulated annealing (SA) algorithm within a genetic algorithm (GA). In this way, a new individual with a low solution quality is rejected by using the stochastic solution acceptance feature of the SA. Moreover, the HGA is enhanced with an advanced two-dimensional loading-check procedure and a rule-based material placement procedure to obtain efficient solutions. The proposed loading-check procedure reduces the processing time of the HGA by avoiding duplicate examinations for the truck loading plans. Likewise, the rule-based material placement procedure prevents unnecessary searches for the assignment plans of the products in a temporary storage area. In computational studies, the performance of the HGA is tested on two different problem sets by comparing HGA with the SA and GA. Furthermore, the HGA is applied to a problem set that is formed by using real-life data of a logistics company. The computational results show that the HGA introduces effective solutions and outperforms both the SA and GA. In addition, the results of the real-life application denote that the HGA can be employed to find effective material flow plans in real situations of cross-docking operations. [ABSTRACT FROM AUTHOR]