A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain

In this paper, a multi-objective simulated annealing (MOSA) solution approach is proposed to a bi-criteria sequencing problem to coordinate required set-ups between two successive stages of a supply chain in a flow shop pattern. Each production batch has two distinct attributes and a set-up occurs in each stage when the corresponding attribute of the two successive batches are different. There are two objectives including: minimizing total set-ups and minimizing the maximum number of set-ups between the two stages that are both NP-hard problems. The MOSA approach starts with an initial set of locally non-dominated solutions generated by an initializing heuristic. The set is then iteratively updated through the annealing process in search for true Pareto-optimal frontier until a stopping criterion is met. Performance of the proposed MOSA was evaluated using true Pareto-optimal solutions of small problems found via total enumeration. It was also compared against a lower bound in large problems. Comparative experiments show that the MOSA is robust in finding true Pareto-optimal solutions in small problems. It was also shown that MOSA is very well-performing in large problems and that it outperforms an existing multi-objective genetic algorithm (MOGA) in terms of quality of solutions.