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Synchronising Lot Sizing and Job Scheduling

Published: 07 December 2023 Publication History

Abstract

We deal here with the production by a subcontractor of resources that a final job scheduler will use to achieve a sequence of jobs. Both are provided with limited storage capacities and must synchronise respective production and consumption processes. The resulting scheduling problem appears, from the point of view of the resource producer, as a multi-stage Lot Sizing problem, where every stage has to be delimited by transfers, which become the core of the decision. Such a problem may typically arise in contexts when the resource is some renewable energy (hydrogen, photo-voltaic,...) required by the jobs and stored inside tanks or batteries. We first set the SLSS: Synchronised Lot Sizing/Scheduling problem in the MILP format and handle it through branch and cut. Next, we get rid of the non {0, 1} decision variables by applying Benders’ cut generation. Finally, we reformulate the SLSS problem as a path search problem set in a specific Transfer space and handle it through an adaptation of the A* algorithm.

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cover image ACM Other conferences
SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 07 December 2023

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Author Tags

  1. A* Algorithm
  2. Benders’ Decomposition
  3. Lot Sizing
  4. MILP
  5. Path Search
  6. Scheduling

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