NDLI: Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm

Content Provider	Springer Nature Link
Author	Yang, Hong an Sun, Qi feng Saygin, Can Sun, Shu dong
Copyright Year	2011
Abstract	This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.
Starting Page	657
Ending Page	666
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	61
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-11-20
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Job shop scheduling Earliness and tardiness Deadline Decoding Genetic algorithm Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics/Supply Chain Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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