Integrating waste minimization concerns in operations scheduling

PhD student: 
Co-supervisor(s): 
External supervisors: 

V. LAFOREST, EVS

Starting date: 
November 2016
Host institution: 

Faced with growing environmental and economic concerns, the industrial world needs to adapt in order to tackle these issues. Industrial production is responsible for 83% of the global solid waste production and 40% of worldwide energy consumption. Operations scheduling appears to be a promising tool to address both the environmental and economic aspects of this problem. A literature review shows that numerous studies have been focusing on reducing energy consumption. This dissertation focuses on a relatively nascent field, namely the topic of waste generation minimization through operations scheduling. The motivating research question can be formulated as: How to integrate waste minimization into operations scheduling? A state-of-the-art on the subject shows a heterogeneous field with a disparate terminology, and a classification scheme is proposed to help unify research on this topic. To answer the research question, a methodology combining flow assessment tools and scheduling parameters is proposed, which enables the identification of waste-minimizing scheduling opportunities in a production system and the characterization of the corresponding scheduling problem. A case study is carried out and validates the applicability of this methodology and the interest of the results it provides. Based on those results, a single-machine waste-minimizing scheduling problem with reentrance in a make-to-order context is modeled using linear programming. Two solving approaches – one exact and one metaheuristic – are compared, and highlight the potential of operations scheduling to reduce industrial waste. Alternative solutions provide relevant trade-offs to decision-makers, offering significant waste reduction in return for a limited increase in inventory. As this methodology focuses on waste, it paves the way for the integration of new environmental aspects such as energy consumption and atmospheric emissions, as well as the social criteria in order to fully encompass the triple bottom line of sustainable development.

Keywords: Scheduling, Waste prevention, Linear Programming, Environmental assessment, Biobjective optimization, Flow assessment, Genetic Algorithm