A dynamic programming approach to solve the facility layout problem for reconfigurable manufacturing

Preparing manufacturing systems to deal with disruptions caused by unexpected factors such as COVID-19 is critical to remain in today's competitive market. Reconfigurable manufacturing systems (RMS) which are characterized by being rapid and cost-effective in response to market changes, are a good alternative to cope with such unexpected events. From the layout point of view, in an RMS, the layout of facilities needs to be changeable and able to be redesigned easily. Dynamic facility layout problem (DFLP) is a good approach to develop layouts that are capable to be changed and redesigned. Dynamic programming (DP) has been known as one of the effective methods to deal with DFLP. To optimize DFLP by DP, the set of possible layouts for every single period which is called the state-space is given to DP and the best multi-period layout is found. Since the number of possible layouts increases rapidly with the increase in the number of facilities, considering all these layouts encounters two major difficulties, memory requirements and computer time requirements. This paper proposes a method that has two main phases. In the first phase, the set of layouts to be considered in each period are determined using a heuristic approach. These layouts are the states in the DP approach where the periods constituted the decomposition stages. The recursive formulation of DP is solved in the second phase using a hybridized metaheuristic approach. The proposed approach restricts the DP to a good subset of the state-space. A genetic algorithm is applied to search for the best subset of layouts where each chromosome represents one subset of layouts. This subset is given to DP to be solved and the result is considered as the fitness of the chromosome. By the evolution of the chromosomes, the best subset of layouts that leads to the best multi-period layout plan is found. The proposed approach is evaluated against DP benchmarks in the literature. Computational results show that the proposed approach is able to provide more efficient solutions, especially for large-sized problems.