Simultaneously solving the transfer line balancing and buffer allocation problems with a multi-objective approach

Two of the most researched problems on transfer line, transfer line balancing problem (TLBP) and buffer allocation problem (BAP), are usually solved separately, although they are closely interrelated. When machine tools have different reliability, the traditional balancing approaches lead to a deviation of the production rate from the actual throughput, which is used as the objective of the following optimization on BAP. This may not only reduce the solution space of BAP, but also bring about a biased overall result. In this paper, the simultaneous solution of these two problems is presented, which includes transfer line balancing problem, BAP, and selection of line configuration, machine tools and fixtures. Production rate computed through simulation software and total cost considering machine tools and buffer capacities are used as two objective functions. The problem is solved applying a multi-objective optimization approach. Two well-known evolutionary algorithms are considered: Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A real case study related to automotive sector is used to demonstrate the validity of the proposed approach.