Efficiency-aware parallel machine scheduling with machine and server selection: MILP models and a matheuristic algorithm
COMPUTERS & INDUSTRIAL ENGINEERING, cilt.220, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 220
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.cie.2026.112249
- Dergi Adı: COMPUTERS & INDUSTRIAL ENGINEERING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, DIALNET, Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
This study addresses the unrelated parallel machine scheduling problem, in which jobs require servers during setup. This problem holds a significant place in the literature due to its widespread application. However, a considerable portion of the literature assumes that the machines to be used are known in advance, and a single server serves all setups. Nevertheless, increasing the number of machines and servers can accelerate production, while using fewer machines and servers can yield benefits such as lower labor costs, reduced energy consumption, and the reallocation of unused resources to other jobs. Therefore, selecting the machines and servers to use in production is essential. In this study, we consider the integrated scheduling and machine and server selection problem. Two multi-objective mixed-integer linear programming models, a time-indexed and a sequence-based indexed model, are proposed for the considered problem. The objectives of the mathematical models are to minimize the total completion time, the number of selected machines, and the number of selected servers. Furthermore, a matheuristic based on an adaptive large neighborhood search algorithm is proposed to solve large-sized problems. The performances of the proposed solution approaches are compared using randomly generated test problems. In large-sized problems, the matheuristic algorithm provides an average 40% improvement over the time-indexed model, and an average 36% improvement over the sequence-based indexed model. To evaluate its effectiveness, the proposed matheuristic is also compared with the classical simulated annealing, and the results indicate its superiority.