NDLI: A novel adaptive hybrid framework for job shop scheduling problem

Content Provider	IEEE Xplore Digital Library
Author	Kalantari, S. SanieeAbadeh, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Comput. & Biomed. Eng., Islamic Azad Univ., Qazvin, Iran (Kalantari, S.) \|\| Dept. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (SanieeAbadeh, M.)
Abstract	This paper deals with the Job Shop Scheduling Problem (JSP) with the objective of minimizing the makespan criterion, the time elapsed between the start of the first job and end of the last job arranged in a job sequence. We propose a novel multi-population based framework called HADP-JSP to solve the JSP. In the HADP-JSP the main population is divided into several groups. Each group adaptively chooses one algorithm from the algorithm pool and then uses it to find solutions (schedules). The operation of selecting an algorithm is done based on the algorithm fitness. The fitness of the algorithms implies the average improvement which they make on the makespanes of the schedules in a group. The algorithm pool consists of five algorithms developed using the Memetic Algorithm, Genetic Algorithm, and Simulated Annealing Algorithm. We have assessed the efficiency of HADP-JSP by running it on a set of 20 well known instances introduced by Lawrence. We have compared the results obtained with those of the three algorithms established in the literature during the last five years. The results indicate that in 95% of all cases the proposed approach can yield the solutions that their values of the makespan are equal to the Best Known Solution (BKS).
Starting Page	1
Ending Page	7
File Size	269858
Page Count	7
File Format	PDF
e-ISBN	9781467363150
DOI	10.1109/RIOS.2013.6595314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-08
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Adaptive approaches Schedules Job shop scheduling Memetic Algorithm Simulated Annealing Genetic Algorithm Multi-population Statistics Genetic algorithms Job Shop Scheduling problem Sociology Simulated annealing
Content Type	Text
Resource Type	Article

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