Smart and sustainable flow-shop scheduling problems: Scenario-based robust optimization and strong heuristics

Abstract

This Ph.D. thesis is dedicated to the development of a smart and sustainable approach to the Distributed Permutation Flow Shop Scheduling Problem (DPFSP) through the utilization of practical optimization models, efficient reformulations, heuristics, and advanced metaheuristics. The DPFSP is an extension of the Permutation Flow Shop Scheduling Problem (PFSP) and serves as its foundational model. The key distinction between the DPFSP and the PFSP lies in their respective scheduling scopes. While the PFSP focuses on scheduling tasks within a single plant, the DPFSP addresses the more complex challenge of scheduling tasks across multiple distributed factories. While prior research has made contributions to the field of DPFSP, this Ph.D. project stands out by incorporating the concepts of sustainability, real-time scheduling, and scenario-based robust optimization into the DPFSP framework. The primary objective of this research is to integrate environmental and social criteria based on the Triple Bottom Line (TBL) to meet the guidelines of the Sustainable Development Goals (SDGs). By considering criteria such as energy consumption, job opportunities, and lost workdays, a multi-objective optimization model and an efficient multi-objective metaheuristic algorithm are developed. Another critical research gap in the field of production scheduling involves the intelligent collection, analysis, and conversion of data into actionable information using real-time decision-making strategies for production systems. In response to this grand challenge, the second objective of this Ph.D. project is to address the uncertainty in the DPFSP by modeling it within the real-time optimization framework of Industry 4.0. A real-time optimization approach is proposed to handle task reassignment to machines under uncertain process times, new task arrivals, or planned machine breakdowns. By incorporating the concepts of Industry 4.0, a comprehensive optimization model using different manual and automated modes of production is proposed and various real-time scheduling strategies and policies are examined into this model. For solving it, constructive heuristics, Lagrangian relaxation and Benders decomposition reformulations are studied. While the second objective addresses uncertainty to some extent, the third objective utilizes a scenario-based robust optimization approach to efficiently address uncertainty in the DPFSP by considering all possible scenarios. The final objective of this Ph.D. project is to address the challenges of the smart and sustainable DPFSP through the development of a comprehensive optimization framework. This framework combines a scenario-based robust optimization model and an advanced metaheuristic algorithm based on adaptive large neighborhood search (ALNS) using various heuristic and local search algorithms. By employing a scenario-based robust optimization approach, the framework considers a range of possible scenarios that may arise due to various disruptions in production schedules. These disruptions can include machine breakdowns, arrival of new tasks, or variations in task processing times. By incorporating these uncertainties into the optimization process, the framework enables the identification of schedules that are robust and resilient to unforeseen circumstances. Overall, this Ph.D. project represents a significant advancement in the field of DPFSP by leveraging the principles of sustainability, real-time scheduling, and robust optimization. Through the application of practical optimization models, efficient reformulations, heuristics, and metaheuristics, this research aims to address the unique challenges posed by scheduling tasks across distributed factories. By contributing to the development of smarter and more sustainable production systems, this work has far-reaching implications for the field and industry as a whole.

Date	4 Oct 2023
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Lyne Woodward (Supervisor) & Ouassima Akhrif (Co-supervisor)