A novel heuristic approach for planning decentralised supply chain under uncertainties

Haque, M; Paul, SK; Sarker, R; Essam, D

A novel heuristic approach for planning decentralised supply chain under uncertainties

Haque, M Paul, SK Sarker, R Essam, D

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Publisher:: Taylor and Francis Group
Publication Type:: Journal Article
Citation:: International Journal of Systems Science: Operations and Logistics, 2023, 10, (1)
Issue Date:: 2023-12-31

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Field	Value	Language
dc.contributor.author	Haque, M
dc.contributor.author	Paul, SK
dc.contributor.author	Sarker, R
dc.contributor.author	Essam, D
dc.date.accessioned	2024-02-12T02:41:28Z
dc.date.available	2024-02-12T02:41:28Z
dc.date.issued	2023-12-31
dc.identifier.citation	International Journal of Systems Science: Operations and Logistics, 2023, 10, (1)
dc.identifier.issn	2330-2674
dc.identifier.issn	2330-2682
dc.identifier.uri	http://hdl.handle.net/10453/175574
dc.description.abstract	Coordinating various activities among company members facing real-life uncertainties or disruptions is a great issue of concern in today’s business world. With this background, a multi-stage decentralised supply chain (SC) network is studied in this paper, where demand uncertainties are considered in each stage of the chain. We consider a serial SC network with single independent entities (manufacturer–distributor–retailer) in each level under restricted information sharing characteristics. The increased variability of uncertain demand through upward sections of the chain is studied. A two-phase planning model is proposed to coordinate the independent members with random customer demand. We develop a scenario-based stochastic optimisation approach where a probability is assigned for each scenario. A rolling horizon-based dynamic updating approach is proposed to update the model results for the current period as uncertainties are revealed. We develop a rule-based solution heuristic and conduct numerical analyses to validate the model. Our results are compared with two approaches–deterministic with mean demand and centralised structure with multiple scenarios. The comparative result shows that our model provides better feasible results with fewer shortage costs. Also, sensitivity analyses are performed on important parameters to observe their effect on the model.
dc.language	en
dc.publisher	Taylor and Francis Group
dc.relation.ispartof	International Journal of Systems Science: Operations and Logistics
dc.relation.isbasedon	10.1080/23302674.2023.2258778
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0915 Interdisciplinary Engineering
dc.subject.classification	4901 Applied mathematics
dc.title	A novel heuristic approach for planning decentralised supply chain under uncertainties
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0102 Applied Mathematics
utslib.for	0915 Interdisciplinary Engineering
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
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2024-02-12T02:41:26Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	1

Abstract:

Coordinating various activities among company members facing real-life uncertainties or disruptions is a great issue of concern in today’s business world. With this background, a multi-stage decentralised supply chain (SC) network is studied in this paper, where demand uncertainties are considered in each stage of the chain. We consider a serial SC network with single independent entities (manufacturer–distributor–retailer) in each level under restricted information sharing characteristics. The increased variability of uncertain demand through upward sections of the chain is studied. A two-phase planning model is proposed to coordinate the independent members with random customer demand. We develop a scenario-based stochastic optimisation approach where a probability is assigned for each scenario. A rolling horizon-based dynamic updating approach is proposed to update the model results for the current period as uncertainties are revealed. We develop a rule-based solution heuristic and conduct numerical analyses to validate the model. Our results are compared with two approaches–deterministic with mean demand and centralised structure with multiple scenarios. The comparative result shows that our model provides better feasible results with fewer shortage costs. Also, sensitivity analyses are performed on important parameters to observe their effect on the model.

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http://hdl.handle.net/10453/175574