Compatibility graphs in scheduling for batch processing machines with setup times

In this paper, we address the problem of minimizing the makespan on batch processing with identical machines, subject to compatibility constraints. Two jobs are considered compatible if they can be processed simultaneously in the same batch. These compatibility constraints are represented by an undirected graph [Formula: see text], where compatible jobs correspond to adjacent vertices, and the batch capacity is limited to 2. We demonstrate that several sub-problems are solvable in polynomial time. Additionally, we propose exact polynomial algorithms to tackle these sub-problems. For the general case, we present a mixed-integer linear programming (MILP) formulation along with heuristic approaches. To evaluate the effectiveness of the proposed methods, we conduct computational experiments.

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Publication Details

Journal
Discrete Mathematics Algorithms and Applications
Published
2026-02-24
DOI
https://doi.org/10.1142/s1793830926500230
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Compatibility graphs in scheduling for batch processing machines with setup times

Mourad Boudhar, Khaoula Bouakaz
Discrete Mathematics Algorithms and Applications
Scheduling and Optimization Algorithms
article

Compatibility graphs in scheduling for batch processing machines with setup times

Mourad Boudhar, Khaoula Bouakaz
article en

Abstract

In this paper, we address the problem of minimizing the makespan on batch processing with identical machines, subject to compatibility constraints. Two jobs are considered compatible if they can be processed simultaneously in the same batch. These compatibility constraints are represented by an undirected graph [Formula: see text], where compatible jobs correspond to adjacent vertices, and the batch capacity is limited to 2. We demonstrate that several sub-problems are solvable in polynomial time. Additionally, we propose exact polynomial algorithms to tackle these sub-problems. For the general case, we present a mixed-integer linear programming (MILP) formulation along with heuristic approaches. To evaluate the effectiveness of the proposed methods, we conduct computational experiments.

Discrete Mathematics Algorithms and Applications
University of Sciences and Technology Houari Boumediene (DZ), University of Algiers Benyoucef Benkhedda (DZ)
Industry, innovation and infrastructure
Openalex Percentile: Top 48%
Scheduling and Optimization Algorithms
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