An Energy-Aware Multiobjective Optimization Framework to Minimize Total Tardiness and Energy Cost on a Single-Machine Nonpreemptive Scheduling

Rubaiee, Saeed; Cinar, Suna; Yildirim, Mehmet Bayram

doi:10.1109/tem.2018.2846627

Title: An Energy-Aware Multiobjective Optimization Framework to Minimize Total Tardiness and Energy Cost on a Single-Machine Nonpreemptive Scheduling

Journal Article · Tue Jul 03 00:00:00 EDT 2018 · IEEE Transactions on Engineering Management

DOI:https://doi.org/10.1109/tem.2018.2846627· OSTI ID:1869449

^[1]; Cinar, Suna ^[2];

^[3]

Univ. of Jeddah (Saudi Arabia)
Istanbul Technical Univ. (Turkey)
Wichita State Univ., Wichita, KS (United States)

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). Here, this paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method.

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Research Organization:: Wichita State Univ., Wichita, KS (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0007719

OSTI ID:: 1869449

Journal Information:: IEEE Transactions on Engineering Management, Vol. 66, Issue 4; ISSN 0018-9391

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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