Proceedings of the 21st international conference on Computers and industrial engineering
A Simulated Annealing Approach to Bicriteria Scheduling Problems on a Single Machine
Journal of Heuristics
Supply chain modeling: past, present and future
Computers and Industrial Engineering - Supply chain management
Computers and Industrial Engineering - Supply chain management
Advanced planning and scheduling with outsourcing in manufacturing supply chain
Computers and Industrial Engineering - Supply chain management
An evolutionary algorithm for optimizing material flow in supply chains
Computers and Industrial Engineering
International Journal of Bio-Inspired Computation
Precast production scheduling using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
Lorenz versus Pareto dominance in a single machine scheduling problem with rejection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
An ant colony optimization algorithm for setup coordination in a two-stage production system
Applied Soft Computing
Scheduling with multi-attribute setup times
Computers and Industrial Engineering
International Journal of Applied Evolutionary Computation
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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.