A multi-attribute, rank-dependent utility model for selecting dispatching rules. (January 2018)

• Record Type: Journal Article
• Authors: Pergher, Isaac; de Almeida, Adiel Teixeira
• Is Part Of: Journal of manufacturing systems. Volume 46(2018)
• Page Start: 264
• Page End: 271
• Publication Date: 2018-01
• Subjects: Dispatching rules -- Simulation -- Rank-dependent utility -- Multi-attribute utility theory -- Maut -- Rdu; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2018.01.007
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A non-expected multi-attribute utility model for dispatching rule(s) selection problem is proposed. The model integrates discrete simulation, rank-dependent utility and multi-attribute utility theory. Number of tardy jobs, mean flow time and tardiness are the decision attributes. The model incorporates the decision-maker's subjective perception of probabilistic occurrences. The solution is consistent with the trade-offs judgments among multiple attributes. Abstract: The use of dispatching rules is one of the most popular solutions used when handling dynamic scheduling problems. However, how best to select a suitable dispatching rule or rules depends on the stochastic behavior of the performance of the system with respect to multiple conflicting attributes. Previous studies based on multi-objective optimization approaches have not explored the subjective information presented by the decision-maker in relation to the multiple impacts that arise from the decisions made using the dispatching rule. This study takes into consideration a rank-dependent utility approach combined with a multi-attribute utility theory to identify the best dispatching rule for dynamic job shop environments. An additive, non-expected, multi-attribute utility function is modeled to represent the decision-maker's preferences and attitude to risk over the multiple stochastic consequences associated with each simulated heuristic. A numerical simulation with realistic data from a furniture hardware industry is performed to demonstrate the usefulness of the proposed decision approach, and the practical issues that emerge are discussed.