Solving the flexible job shop scheduling and lot streaming problem with setup and transport resource constraints

This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-based objectives, setup and transport resource constraints were not considered simultaneously with this policy, limiting the resulting schedule's applicability in practice. For this reason, we propose a novel Constraint Programming (CP) model enriched by an efficient variable and value ordering strategy specifically designed for the FJSSP-LS with resource constraints. We also present a CP-based iterative improvement method, CP-based Large Neighbourhood Search (CP-based LNS), that focuses on exploring large neighbourhoods through the CP model. Both models are initially tested for the FJSSP and have been shown to provide the best solutions to well-known benchmark instances. Next, they are used for the FJSSP-LS, and the proposed CP-based LNS improves the objective function value by 4.68 percent on average compared to the CP model for the generated test problems.

Automated summary

This article proposes a solution for the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) considering setup and transport resource constraints. A novel Constraint Programming (CP) model and a CP-based Large Neighborhood Search (CP-based LNS) method are presented. The models are tested on FJSSP and shown to provide the best solutions. For FJSSP-LS, CP-based LNS improves the objective function value by an average of 4.68% compared to the CP model for the generated test problems.