EURO 2024 Copenhagen
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4004. Solving flexible job shop scheduling problems with multiple resource constraints using a unified CP-based solution framework

Invited abstract in session WC-26: Topics in scheduling (Contributed), stream Combinatorial Optimization.

Wednesday, 12:30-14:00
Room: 012 (building: 208)

Authors (first author is the speaker)

1. Grigorios Kasapidis
Operations and Supply Chain Management, University of Liverpool
2. Dimitris Paraskevopoulos
Bayes Business School (formerly Cass), City, University of London
3. Yiannis Mourtos
Management Science & Technology, Athens University of Economics & Business
4. Panagiotis Repoussis
Department of Marketing and Communication, Athens University of Economics and Business

Abstract

This study focuses on flexible job shop scheduling problems with multiple resource constraints. It introduces a comprehensive approach for modeling various resource types like limited capacity machine buffers, tools, utilities, and work in progress buffers. The proposed methodology involves a Constraint Programming (CP) model alongside a CP-based Adaptive Large Neighbourhood Search (ALNS-CP) algorithm. The ALNS-CP algorithm incorporates long-term memory structures to retain valuable information about individual and paired operations assigned to machines, gathered from high-quality and diverse solutions during the search process. These memory structures aid in formulating additional constraints for the CP solver, and therefore guiding the search towards promising areas of the solution space. To evaluate its efficacy, extensive experiments were conducted on established benchmark sets, comparing the performance of ALNS-CP with existing state-of-the-art methods. Furthermore, new instances of varying sizes were utilized to explore how different resource types impact the makespan. The computational findings highlight the competitiveness of the proposed framework, notably yielding 39 new best solutions across well-known problem instances of the literature.

Keywords

Status: accepted

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