Sustainable distributed permutation flow-shop scheduling model based on a triple bottom line concept

Based on a triple bottom line concept, sustainable development is characterized by the simultaneous pursuit of economic, environmental and social goals. The implementation of this concept in production scheduling can result in the resolution of a sustainable Distributed Permutation Flow Shop Scheduling Problem (DPFSP). The present study conceptually shifts an energy-efficient DPFSP to a sustainable DPFSP, simultaneously contributing to economic, environmental and social improvements. The study aims not only to minimize the total energy consumption related to production, but also, to maximize, for the first time, the social factors linked to job opportunities and lost working days. Different production centers and technologies are considered as new suppositions to establish a sustainable DPFSP. In this regard, a novel multi-objective mixed integer linear model is developed. To manage the high complexity of the proposed model, a novel multi-objective learning-based heuristic is established, as an extension of the Social Engineering Optimizer (SEO). The applicability of the proposed model is determined in the context of the wood industry in Canada. Several simulated tests are considered to verify the model. The proposed heuristic is compared with one of the other well-known, recent and state-of-the art methods. In order to guarantee a fair comparison, the Taguchi method is used to tune the parameters of the algorithms. Finally, sensitivity analyses are done to assess the efficiency of the proposed model.

Automated summary

The study introduces a new multi-objective mixed integer linear model to address the sustainable DPFSP in the wood industry, and utilizes a novel multi-objective learning-based heuristic method to manage the high complexity of the proposed model.