An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling
Yildirim, Mehmet Bayram
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Rubaiee, Saeed; Yildirim, Mehmet Bayram. 2019. An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Computers & Industrial Engineering, vol. 127:pp 240-252
Energy-aware scheduling in manufacturing operations with time-of-use electricity tariffs is a challenging problem. Sustainable manufacturing is gaining significant momentum: companies are not only improving their product quality, but also optimizing the production processes to improve energy consumption (i.e., minimizing energy cost) in order to manage environmental challenges which contribute to global climate change. The purpose of this paper is to study a preemptive scheduling problem on a single-machine to minimize the total completion time and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multiobjective mathematical programming model. To solve these objectives, we develop several new holistic ant colony optimization algorithms. The proposed model is solved via several methods including weighted sum method (WSM) using CPLEX, and multiobjective ant colony optimization based on a dominance ranking (ACO-DR) or based on a dominance ranking procedure and crowding distance comparison (ACO-DRC) or based on a heuristic approach to obtain an approximate Pareto-front and also provide information on when to start and resume each job for any solution on the Pareto-front. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we demonstrate how the results of the multiobjective model could be utilized in decision making using the multiobjective optimization on the basis of ratio analysis (MOORA) method. This proposed model and heuristics allows decision makers to operate in challenging-data enabled environments in industrial internet of things ecosystem, and assists in optimizing production planning to improve energy cost.
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