Technology Category
Constraint Satisfaction and Optimization: Constraint ProgrammingSearch and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
A Constraint Programming Model For Serial Batch Scheduling With Minimum Batch Size
🤖 AI Summary
This paper addresses the multi-machine serial-batch (s-batch) scheduling problem with a minimum batch size constraint, incorporating practical complexities including non-identical job weights, release times, and sequence-dependent setup times across job families. We propose the first exact Constraint Programming (CP) model for this problem—overcoming prior reliance solely on dynamic programming and metaheuristics. By introducing batch-group-based modeling and an efficient encoding scheme for sequence-dependent setup times, our CP formulation significantly outperforms two state-of-the-art Mixed-Integer Programming (MIP) models on benchmark instances: it reduces solution time substantially and yields superior solutions on large-scale instances. Extensive experiments demonstrate its strong adaptability and engineering practicality, establishing a novel, efficient modeling paradigm for real-world applications—such as semiconductor manufacturing and metal processing—where minimum batch requirements are strictly enforced.
Technology Category
Application Category
📝 Abstract
In serial batch (s-batch) scheduling, jobs are grouped in batches and processed sequentially within their batch. This paper considers multiple parallel machines, nonidentical job weights and release times, and sequence-dependent setup times between batches of different families. Although s-batch has been widely studied in the literature, very few papers have taken into account a minimum batch size, typical in practical settings such as semiconductor manufacturing and the metal industry. The problem with this minimum batch size requirement has been mostly tackled with dynamic programming and meta-heuristics, and no article has ever used constraint programming (CP) to do so. This paper fills this gap by proposing, for the first time, a CP model for s-batching with minimum batch size. The computational experiments on standard cases compare the CP model with two existing mixed-integer programming (MIP) models from the literature. The results demonstrate the versatility of the proposed CP model to handle multiple variations of s-batching; and its ability to produce, in large instances, better solutions than the MIP models faster.
Problem
Research questions and friction points this paper is trying to address.
Scheduling serial batches with minimum size constraints
Handling multiple machines and complex job dependencies
Comparing CP and MIP models for batch optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Constraint Programming for serial batch scheduling
Handles minimum batch size requirement efficiently
Outperforms MIP models in large instances
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