Solving single-machine total weighted tardiness problems with sequence-dependent setup times by meta-heuristics

Simulated annealing (SA), genetic algorithms (GA), and tabu search (TS) are the three well known meta-heuristics for combinatorial optimization problems. In this paper, single-machine total weighted tardiness problems with sequence-dependent setup times are solved by SA, GA, and TS approaches. A random swap and insertion search is applied in SA, and a mutation operator performed by a greedy local search is used in a GA. Similarly, a swap and an insertion tabu list are adopted in TS. To verify these proposed approaches, computational experiments were conducted on benchmark problem sets. The experimental results show that these approaches find new upper bound values for most benchmark problems within reasonable computational expenses.

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