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Dominance rules in combinatorial optimization problems

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  • Jouglet, Antoine
  • Carlier, Jacques

Abstract

The aim of this paper is to study the concept of a "dominance rule" in the context of combinatorial optimization. A dominance rule is established in order to reduce the solution space of a problem by adding new constraints to it, either in a procedure that aims to reduce the domains of variables, or directly in building interesting solutions. Dominance rules have been extensively used over the last 50 years. Surprisingly, to our knowledge, no detailed description of them can be found in the literature other than a few short formal descriptions in the context of enumerative methods. We are therefore proposing an investigation into what dominance rules are. We first provide a definition of a dominance rule with its different nuances. Next, we analyze how dominance rules are generally formulated and what are the consequences of such formulations. Finally, we enumerate the common characteristics of dominance rules encountered in the literature and in the usual process of solving combinatorial optimization problems.

Suggested Citation

  • Jouglet, Antoine & Carlier, Jacques, 2011. "Dominance rules in combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 212(3), pages 433-444, August.
    • Handle: RePEc:eee:ejores:v:212:y:2011:i:3:p:433-444
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    3. Rasti-Barzoki, Morteza & Hejazi, Seyed Reza, 2013. "Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains," European Journal of Operational Research, Elsevier, vol. 228(2), pages 345-357.
    4. Nasini, Stefano & Nessah, Rabia, 2022. "A multi-machine scheduling solution for homogeneous processing: Asymptotic approximation and applications," International Journal of Production Economics, Elsevier, vol. 251(C).
    5. Braune, R. & Zäpfel, G. & Affenzeller, M., 2012. "An exact approach for single machine subproblems in shifting bottleneck procedures for job shops with total weighted tardiness objective," European Journal of Operational Research, Elsevier, vol. 218(1), pages 76-85.
    6. Jacques Carlier & Antoine Jouglet & Eric Pinson & Abderrahim Sahli, 2022. "A data structure for efficiently managing a set of energy functions," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2460-2481, November.
    7. Falq, Anne-Elisabeth & Fouilhoux, Pierre & Kedad-Sidhoum, Safia, 2022. "Dominance inequalities for scheduling around an unrestrictive common due date," European Journal of Operational Research, Elsevier, vol. 296(2), pages 453-464.
    8. Silva, Marco & Poss, Michael & Maculan, Nelson, 2020. "Solution algorithms for minimizing the total tardiness with budgeted processing time uncertainty," European Journal of Operational Research, Elsevier, vol. 283(1), pages 70-82.
    9. Rico Walter & Alexander Lawrinenko, 2020. "A characterization of optimal multiprocessor schedules and new dominance rules," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 876-900, November.

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