Acta Logistica | 2026
Authors: Fekih A.; Hajji M.K.
DOI: 10.22306/al.v13i1.734
Journal: Acta Logistica
Year: 2026
Publisher: 4S go, s.r.o
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 1
In industrial operations, the effective optimization of logistics and production flows, including material, energy, and information flows, is essential for improving operational performance while reducing environmental impacts. The combinatorial complexity of the green flexible job-shop scheduling problem (GFJSP) and its connection to sustainable manufacturing have attracted significant attention in recent research. This study aims to minimize total flow time and carbon emissions by optimizing the management of industrial flows, while accounting for technical elements of logistics such as production sequencing, resource allocation, and coordination of material and energy flows. Two complementary solution approaches are proposed: an exact constraint programming (CP) model for small-sized instances, ensuring optimality, and a particle swarm optimization (PSO) algorithm enhanced with an energy-aware encoding scheme and logistic map-based population initialization for medium and large-scale problems. Numerical experiments demonstrate the effectiveness of both approaches in addressing operational efficiency and environmental objectives. The novelty of this work lies in integrating the management of logistics and production flows with sustainable scheduling through a hybrid exact–metaheuristic strategy. Sensitivity analysis further validates the robustness of the PSO, highlighting how exact and metaheuristic methods complement each other to tackle complex industrial scheduling challenges. Overall, this study provides both scientific insights and practical guidance for managing logistics and production flows in sustainable industrial systems. © 2026, 4S go, s.r.o. All rights reserved.
constraint programming; green flexible job-shop; industrial logistics; particle swarm algorithm