Simulated annealing approach to solve dual resource constrained job shop scheduling problems: layout impact analysis on solution quality

Maurizio Faccio, Jana Ries, Nicola Saggioro

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Abstract

Real-world manufacturing systems are operating subject to a substantial level of resource constraints. One characteristic model that considers the combination of human and machine resource constraints is called Dual Resource Constrained (DRC). In this context a number of machines nmach is managed by a number of operators nop , with typically nop < nmach. A real life case study for an Italian manufacturing company is introduced that uses a set of identical parallel machines being operated by a set of operators. Each job is scheduled to one machine with corresponding loading and unloading process times. A Simulated annealing approach is propose to solve the dual resource constrained job shop scheduling problem. A sensitivity analysis is developed for the algorithm-specific parameters selection of relevant DRC layouts. The high variability in jobs time, typical of the just in time production environment, is take into account. As demonstrated by the results, the selected layout in terms of nmach./ nop ratio, strongly influences the production system performances. The required nmach./ nop ratio, function of the jobs list and sequence, versus the nmach./ nop ratio given by the production system layout is deeply analysed. The worst scheduling performances are obtained in case of perfect DRC problem, while when it gradually turns into a Single Resource Constrained (SRC), performances increase. Results demonstrate also that this trend is not symmetrical as the quality of the solutions improves faster as the problem turns into being operator-constrained rather than machine-constrained.
Original languageEnglish
Pages (from-to)609-629
JournalInternational Journal of Mathematics in Operational Research
Volume7
Issue number6
DOIs
Publication statusPublished - 1 Dec 2015

Keywords

  • scheduling
  • dual resource constrained
  • DRC
  • resources utilisation
  • JIT production
  • job list variability

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