The ant colony optimization meta-heuristic
New ideas in optimization
Future Generation Computer Systems
An Evolutionary Algorithm for the Sequence Coordination in Furniture Production
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Supply chain scheduling: Batching and delivery
Operations Research
An ant colony system for permutation flow-shop sequencing
Computers and Operations Research
Ant Colony Optimization
Review: Meta knowledge of intelligent manufacturing: An overview of state-of-the-art
Applied Soft Computing
Computers and Industrial Engineering
A hybrid algorithm for the single-machine total tardiness problem
Computers and Operations Research
New robust and efficient ant colony algorithms: Using new interpretation of local updating process
Expert Systems with Applications: An International Journal
A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain
Computers and Industrial Engineering
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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This paper is concerned with the coordination of setup times in a two-stage production system. The problem is derived from a furniture plant, where there are two consecutive departments including cutting and painting departments. Items with the same levels of both attributes are grouped into a single batch in advance. A sequence-dependent setup time is required in a stage when a new batch has a different level of attribute from the previous one. The objective is to minimize the total setup time. In this paper, we first propose a simple dispatching rule called the Least Flexibility with Setups (LFS) rule. The LFS rule can yield a solution better than an existing genetic algorithm while using much less computation time. Using the LFS rule as both the initial solution method and the heuristic desirability, an Ant Colony Optimization (ACO) algorithm is developed to further improve the solution. Computational experiments show that the proposed ACO algorithm is quite effective in finding the near-optimal solution.