Parallel machine scheduling problems in green manufacturing industry. (January 2016)

• Record Type: Journal Article
• Authors: Li, Kai; Zhang, Xun; Leung, Joseph Y.-T.; Yang, Shan-Lin
• Is Part Of: Journal of manufacturing systems. Volume 38(2016)
• Page Start: 98
• Page End: 106
• Publication Date: 2016-01
• Subjects: Green manufacturing -- Scheduling -- Identical and parallel machines -- Makespan -- Total completion time -- Heuristics; 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.2015.11.006
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract : Highlights: We consider two scheduling problems in green manufacturing industries. There is a set of n jobs to be scheduled on m parallel machines. Each machine has a cost which is the energy cost plus the pollutant removal cost. The goal is to minimize the makespan and the total completion time subject to the constraint that the total machine cost is not more than a given threshold. We propose two fast heuristics to solve the problems. Abstract: Manufacturing companies are now more conscious about the environment. As such, there are more concerns in reducing the consumption of energy and the production of pollutants. Reduced consumption of energy will save cost, while reduction of pollutants will decrease the cost of cleaning up the environment. This paper considers scheduling problems that arise in green manufacturing companies. Suppose the manufacturing company has a set of parallel machines. Each machine has a cost per unit time that differs from machine to machine. The cost here is the sum of the energy cost and the clean up cost. A set of jobs is to be processed by these machines. Our goal is to find a schedule that minimizes the makespan (schedule length) or the total completion time, subject to the constraint that the total cost is not more than a given threshold value. We propose efficient heuristics and show, by computational experiments, that they perform very well in practice.