Multi-scenario scheduling to maximise the weighted number of just-in-time jobs

Miri Gilenson, Dvir Shabtay

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We study a multi-scenario scheduling problem on a single-machine and a two-machine flow-shop system. The criterion is to maximise the weighted number of just-in-time jobs. We first analyze the case where only processing times are scenario-dependent. For this case, we prove that the single-machine problem is solvable in polynomial time. We also prove that the unit weight two-machine flow-shop problem is solvable in polynomial time if processing times are scenario-dependent only on the second machine, and is ordinary (Formula presented.) -hard when processing times are scenario-dependent only on the first machine. This ordinary (Formula presented.) -hard result holds as long as the number of scenarios is fixed. Otherwise, the problem becomes strongly (Formula presented.) -hard. We then analyze the case where only weights are scenario-dependent. We adopt a multi-criteria approach and define several problem variations. We prove that one of them is polynomial solvable on a single machine and ordinary (Formula presented.) -hard in a two-machine flow-shop system. We also prove that all other problem variations are ordinary (Formula presented.) -hard even if there are only two scenarios, and are strongly (Formula presented.) -hard when the number of scenarios is arbitrary. Finally, we provide two pseudo-polynomial time algorithms for solving all the hard problems when the number of scenarios is fixed.

Original languageEnglish
Pages (from-to)1762-1779
Number of pages18
JournalJournal of the Operational Research Society
Volume72
Issue number8
DOIs
StatePublished - 1 Jan 2021

Keywords

  • -hard
  • Single-machine scheduling
  • flow-shop scheduling
  • just-in-time scheduling
  • multi-scenario scheduling

ASJC Scopus subject areas

  • Modeling and Simulation
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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