Fuzzy logic guided genetic algorithms for the location assignment of items

In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the 'Item-location assignment problem', faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the sum of the total traveling time of the workers required for all orders is minimized. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make a comparison with the FLGA through simulations, several search methods, branch and bound, standard GA (i.e. without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted. Results show that the FLGA outperforms the other search methods in the considered scenario.