A disruption recovery model in a production-inventory system with demand uncertainty and process reliability

Paul, SK; Sarker, R; Essam, D

A disruption recovery model in a production-inventory system with demand uncertainty and process reliability

Paul, SK

Sarker, R Essam, D

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Publication Type:: Chapter
Citation:: 2013, 8104 LNCS pp. 511 - 522
Issue Date:: 2013-10-15

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Field	Value	Language
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Sarker, R	en_US
dc.contributor.author	Essam, D	en_US
dc.date.issued	2013-10-15	en_US
dc.identifier.citation	2013, 8104 LNCS pp. 511 - 522	en_US
dc.identifier.isbn	9783642409240	en_US
dc.identifier.uri	http://hdl.handle.net/10453/74822
dc.description.abstract	This paper develops a risk management tool for a productioninventory system that involves an imperfect production process and faces production disruption and demand uncertainty. In this paper, the demand uncertainty is represented as fuzzy variable and the imperfectness is expressed as process reliability. To deal with the production scheduling in this environment, a non-linear constrained optimization model has been formulated with an objective of maximizing the graded mean integration value (GMIV) of the total expected profit. The model is applied to solve the production-inventory problem with single as well as multiple disruptions on a real time basis that basically revises the production quantity in each cycle in the recovery time window. We propose a genetic algorithm (GA) based heuristic to solve the model and obtain an optimal recovery plan. A numerical example is presented to explain usefulness of the developed model. © 2013 IFIP International Federation for Information Processing.	en_US
dc.relation.isbasedon	10.1007/978-3-642-40925-7_47	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A disruption recovery model in a production-inventory system with demand uncertainty and process reliability	en_US
dc.type	Chapter
utslib.citation.volume	8104 LNCS	en_US
utslib.for	0806 Information Systems	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.publication-status	Published	en_US
pubs.volume	8104 LNCS	en_US

Abstract:

This paper develops a risk management tool for a productioninventory system that involves an imperfect production process and faces production disruption and demand uncertainty. In this paper, the demand uncertainty is represented as fuzzy variable and the imperfectness is expressed as process reliability. To deal with the production scheduling in this environment, a non-linear constrained optimization model has been formulated with an objective of maximizing the graded mean integration value (GMIV) of the total expected profit. The model is applied to solve the production-inventory problem with single as well as multiple disruptions on a real time basis that basically revises the production quantity in each cycle in the recovery time window. We propose a genetic algorithm (GA) based heuristic to solve the model and obtain an optimal recovery plan. A numerical example is presented to explain usefulness of the developed model. © 2013 IFIP International Federation for Information Processing.

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http://hdl.handle.net/10453/74822