Fuzzy optimisation of multi-objective job shop scheduling based on inventory information

Islam, MN; Paul, SK; Azeem, A

Fuzzy optimisation of multi-objective job shop scheduling based on inventory information

Islam, MN Paul, SK

Azeem, A

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Publication Type:: Journal Article
Citation:: International Journal of Services and Operations Management, 2013, 15 (2), pp. 123 - 139
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Islam, MN	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Azeem, A	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	International Journal of Services and Operations Management, 2013, 15 (2), pp. 123 - 139	en_US
dc.identifier.issn	1744-2370	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117178
dc.description.abstract	Job shop scheduling problems are one of the oldest combinatorial optimisation problems being studied. In this paper, fuzzy processing times of operations and fuzzy due dates of jobs are considered to incorporate fuzziness in the problem. Percentage of inventory consumption and profit earned form the orders are also considered in this fuzzy multi-objective job shop scheduling problem. Fuzzy inference system (FIS) is used to calculate the job weights based on the percentage of inventory consumption for a particular job and profit can be earned from the jobs. Average weighted tardiness, number of tardy jobs, total flow time and idle times of machines are considered as objectives which should be minimised. In this paper, genetic algorithm (GA) is used as a heuristic technique with specially encoded chromosomes that denotes the complete schedule of the jobs. A local search technique, simulated annealing (SA) is also used to compare the results obtained in two different methods. Different problem sizes has been tested and the fitness function values and computation times of the problems for each method is compared. © 2013 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Services and Operations Management	en_US
dc.relation.isbasedon	10.1504/IJSOM.2013.053641	en_US
dc.subject.classification	Business & Management	en_US
dc.title	Fuzzy optimisation of multi-objective job shop scheduling based on inventory information	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	15	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	1505 Marketing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Job shop scheduling problems are one of the oldest combinatorial optimisation problems being studied. In this paper, fuzzy processing times of operations and fuzzy due dates of jobs are considered to incorporate fuzziness in the problem. Percentage of inventory consumption and profit earned form the orders are also considered in this fuzzy multi-objective job shop scheduling problem. Fuzzy inference system (FIS) is used to calculate the job weights based on the percentage of inventory consumption for a particular job and profit can be earned from the jobs. Average weighted tardiness, number of tardy jobs, total flow time and idle times of machines are considered as objectives which should be minimised. In this paper, genetic algorithm (GA) is used as a heuristic technique with specially encoded chromosomes that denotes the complete schedule of the jobs. A local search technique, simulated annealing (SA) is also used to compare the results obtained in two different methods. Different problem sizes has been tested and the fitness function values and computation times of the problems for each method is compared. © 2013 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/117178