Integrated locating in-house logistics areas and transport vehicles selection problem in assembly lines

Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets' location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP.

Automated summary

This study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets' location and develops a mixed-integer programming (MIP) model for the integrated supermarket location and transport vehicles selection problems (SLTVSP). A hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is proposed to address large-sized problems, outperforming other algorithms and providing a good approximation of the MIP solutions. Analysis reveals the benefits of applying different transport vehicles for SLTVSP.