Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/24055
Title: Scheduling practice and recent developments in flow shop and job shop scheduling
Authors: Yenisey, Mehmet Mutlu
Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0003-1744-3062
Yağmahan, Betül
B-5557-2017
23487445600
Keywords: Particle swarm optimization
Tabu search algorithm
Genetic algorithm
Heuristic algorithm
Total tardiness
Scatter search
M-machine
N-job
Minimizing makespan
Sequencing problem
Computer science
Issue Date: 2009
Publisher: Springer
Citation: Yağmahan, B. ve Yenisey, M. M. (2009). "Scheduling practice and recent developments in flow shop and job shop scheduling". Computational Intelligence in Flow Shop and Job Shop Scheduling, Studies in Computational Intelligence, 230, 261-300.
Abstract: Each plant and/or service provider performs several tasks to satisfy customer demand. Every task consumes several resources in order to be completed. Scheduling deals with the allocation of limited resources to tasks over time. Because the resources used in manufacturing activities are very limited, scheduling becomes a very important concept in managerial decision-making. This importance draws the attention of both practitioners and academicians to scheduling. Scheduling problems usually lie in the NP-hard problem class. Difficulty especially increases as the number of jobs or machines involved increases. As the problem size increases, exact solution techniques become insufficient. This chapter provides an overview of recent developments in computational intelligence approaches to flow shop and job shop scheduling.
URI: https://doi.org/10.1007/978-3-642-02836-6_9
https://link.springer.com/chapter/10.1007%2F978-3-642-02836-6_9
http://hdl.handle.net/11452/24055
ISSN: 1860-949X
Appears in Collections:Scopus
Web of Science

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