Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2537-2542· 0 citations· 11 references
Abstract
The job sequencing and tool switching problem with non-identical parallel machines (SSP-NPM) is a challenging combinatorial optimization problem that arises in flexible manufacturing systems. It jointly involves job assignment, sequencing, and tool management decisions under limited magazine capacities to minimize the makespan. In this paper, we first propose an improved position-based MILP (IPM). This model builds upon the one presented by [1], offering a more compact representation of completion times and is strengthened by static improvement mechanisms inspired by [2]. We then develop a genetic algorithm (GA) using an explicit permutation-assignment encoding and a tool-switch evaluator. Computational experiments were conducted on benchmark instances from the literature. Results show that the IPM solves 58.43% of instances to optimality, outperforming existing formulations, while on medium-size large-scale instances, the proposed GA converges about 50% faster than existing metaheuristic approaches.
This work proposes an exact algorithm for the SSP, namely the Combinatorial Branch-and-Bound (C-B\&B) algorithm, which combines two distinct branch-and-bound algorithms, each introducing novel features compared with the existing literature.
Alberto Locatelli, Jean-François Côté, Leandro C. Coelho· 0 citations
The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makesp...
Experimental results show that the improved algorithm achieves an optimal Makespan value of 190 in dynamic disturbance scenarios and exhibits strong robustness, providing an efficient and feasible solution for job shop scheduling in complex production environments.
Jianguo Du, Chengkun Li, Zijie Tang· ITM Web of Conferences· 0 citations
This paper solves the scheduling problem of identical parallel machines with tooling and pallet–fixture constraints in a high‐mix, low‐volume, high‐complexity manufacturing environment. The machines are connected through an automated material handling device and in‐between buffer places that allow for autonomous prod...
Koen Herps, Quang-Vinh Dang, Tugce G. Martagan et al.· International Transactions i...· 0 citations
This study aims to minimize the makespan while considering sequence-dependent setup times for both machines and work orders and proposes a genetic algorithm integrating reinforcement learning and variable neighborhood search.
Yung-Chia Chang, Kuei-Hu Chang, Te-Chi Kong et al.· International Journal of Ind...· 0 citations
An in-depth analysis of job sequencing in flow-shop scheduling problems, with a focus on exploring alternative mean-based techniques alongside the classical Johnson’s method, highlights that mean based methods can serve as viable alternatives to Johnson’s method, offering flexibility in sequencing decisions while retai...
