A MILP model for an extended version of the Flexible Job Shop Problem
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Abstract (arXiv)
A MILP model for an extended version of the Flexible Job Shop Scheduling problem is proposed. The extension allows the precedences between operations of a job to be given by an arbitrary directed acyclic graph rather than a linear order. The goal is the minimization of the makespan. Theoretical and practical advantages of the proposed model are discussed. Numerical experiments show the performance of a commercial exact solver when applied to the proposed model. The new model is also compared with a simple extension of the model described by "Ozg"uven, "Ozbakir, and Yavuz (Mathematical models for job-shop scheduling problems with routing and process plan flexibility, Applied Mathematical Modelling, 34:1539--1548, 2010), using instances from the literature and instances inspired by real data from the printing industry.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2014.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2023Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methodscited by 12
- 2025Scalability of Reinforcement Learning Methods for Dispatching in Semiconductor Frontend Fabs: A Comparison of Open-Source Models with Real Industry Datasetscited by 1
- 2022A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraintscited by 0
- 2025Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimizationcited by 0
- 2026Constrained Reinforcement Learning Using Successor Representationscited by 0
- 2026Constrained Reinforcement Learning Using Successor Representations27 Jul 2026
- 2025Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization22 May 2025
- 2025Scalability of Reinforcement Learning Methods for Dispatching in Semiconductor Frontend Fabs: A Comparison of Open-Source Models with Real Industry Datasets16 May 2025
- 2023Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods24 Aug 2023
- 2022A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints9 May 2022