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Scheduling Jobs on Unrelated Machines with Job Splitting and Setup Resource Constraints for Weaving in Textile Manufacturing

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 630)

Abstract

This work considers the production scheduling of the weaving process in a real-life textile industry, where a set of jobs - linked to the production of a fabric type and accompanied by a quantity - arrive over time and have to be processed (woven) by a set of parallel unrelated machines (looms) with respect to their strict deadlines (delivery dates), under the goal of makespan minimization. A number of critical job and machine properties demonstrate the challenging nature of weaving scheduling, i.e., a) job splitting: each order’s quantity is allowed to be split and processed on multiple machines simultaneously, b) sequence-dependent setup times: the setup time between any two orders j and k is different than setup time between jobs k and j on the same machine and c) setup resource constraints: the number of setups that can be performed simultaneously on different machines is restricted due to a limited number of setup workers. We propose a MILP formulation that captures the entire weaving process. To handle large real instances, while also speeding up an exact solver on smaller ones, we propose two heuristics that perform job-splitting and assignment of jobs to machines either greedily or by using a relaxed version of our MILP model, respectively. We evaluate the impact of our approach on real datasets under user-imposed time limits and resources (machines, workers) availability.

Keywords

  • Textile
  • Weaving scheduling
  • Integer programming
  • Heuristics

This research has been supported by the EU through the FACTLOG Horizon 2020 project, grant number 869951.

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Correspondence to Stavros Vatikiotis .

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Mourtos, I., Vatikiotis, S., Zois, G. (2021). Scheduling Jobs on Unrelated Machines with Job Splitting and Setup Resource Constraints for Weaving in Textile Manufacturing. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_45

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  • DOI: https://doi.org/10.1007/978-3-030-85874-2_45

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