A Combined Fuzzy AHP and GA Approach to Machine Scheduling Problems from an Environmental Perspective

C. Y. Ng and W. H. Lee ()
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C. Y. Ng: Lee Shau Kee School of Business and Administration, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong
W. H. Lee: Lee Shau Kee School of Business and Administration, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong

Journal of Environmental Assessment Policy and Management (JEAPM), 2023, vol. 25, issue 03, 1-33

Abstract: With increasing concerns about the need for environmental protection and reduction of energy consumption, enterprises have to demonstrate their capabilities in lowering resource consumption by enhancing the efficiency of their systems. Although some approaches to quantifying the environmental burden generated by a product or service system such as life cycle assessment (LCA) and carbon auditing have been developed, expert judgments are often required to implement them. From an industry’s perspective, small- and medium-sized enterprises need an efficient tool to determine the best solution when considering various attributes simultaneously. Thus, a combination of fuzzy analytical hierarchy process and genetic algorithm has been introduced to solve scheduling problems and support the decision-making process. This study aims to effectuate the green scheduling on optimized machine-task assignments with fuzzy evaluation. The proposed approach is illustrated using a case example from a centralized dishwashing company. Results show that the global warming potential value can be reduced by 1.86% and the cost of operation is slightly increased by only 1.28%. The result of the proposed approach is presented simply in the form of machine-task assignments with optimized environmental impact values and associated costs. Therefore, no further result interpretation by environmental experts is required. This study can be a reference for government policymakers in formulating policies to synthesize operation optimization and business sustainability.

Keywords: Fuzzy AHP; genetic algorithm; life cycle assessment; green scheduling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S1464333223500126

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