Production and energy mode control of a production-inventory system

dc.contributor.author Barış Tan
dc.contributor.author Oktay Karabağ
dc.contributor.author Siamak Khayyati
dc.date.accessioned 2025-10-06T17:49:24Z
dc.date.issued 2023
dc.description.abstract Energy efficiency in manufacturing can be improved by controlling energy modes and production dynamically. We examine a production-inventory system that can operate in Working Idle and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival production and warm-up times we formulate the problem of determining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint production and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system. © 2023 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.1016/j.ejor.2022.12.021
dc.identifier.issn 03772217
dc.identifier.issn 0377-2217
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146283639&doi=10.1016%2Fj.ejor.2022.12.021&partnerID=40&md5=4b0082a78a0efa470a1e9eb74a7cdb59
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/8429
dc.language.iso English
dc.publisher Elsevier B.V.
dc.relation.ispartof European Journal of Operational Research
dc.source European Journal of Operational Research
dc.subject Dynamic Programming, Flexible Manufacturing Systems Control, Markov Processes, Or In Energy, Energy Efficiency, Flexible Manufacturing Systems, Inventory Control, Linear Programming, Markov Processes, Process Control, Production Control, Stochastic Control Systems, Stochastic Systems, Structural Optimization, Control Policy, Energy Modes, Exponentials, Flexible Manufacturing System Control, Joint Production, Manufacturing Systems Control, Or In Energy, Production Inventory System, Production Modes, Warm-up Time, Dynamic Programming
dc.subject Energy efficiency, Flexible manufacturing systems, Inventory control, Linear programming, Markov processes, Process control, Production control, Stochastic control systems, Stochastic systems, Structural optimization, Control policy, Energy modes, Exponentials, Flexible manufacturing system control, Joint production, Manufacturing systems control, OR in energy, Production inventory system, Production modes, Warm-up time, Dynamic programming
dc.title Production and energy mode control of a production-inventory system
dc.type Article
dspace.entity.type Publication
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gdc.description.endpage 1187
gdc.description.startpage 1176
gdc.description.volume 308
gdc.identifier.openalex W4312054362
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gdc.oaire.keywords dynamic programming
gdc.oaire.keywords flexible manufacturing systems control
gdc.oaire.keywords Markov processes
gdc.oaire.keywords OR in energy
gdc.oaire.keywords Dynamic programming
gdc.oaire.keywords Operations research and management science
gdc.oaire.keywords Management
gdc.oaire.keywords Flexible manufacturing systems control
gdc.oaire.keywords Dynamic programming; Flexible manufacturing systems control; Markov processes; OR in energy
gdc.oaire.keywords Management; Operations research and management science
gdc.oaire.keywords SDG 7 - Affordable and Clean Energy
gdc.oaire.popularity 1.2923731E-8
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gdc.oaire.sciencefields 0209 industrial biotechnology
gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 02 engineering and technology
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gdc.opencitations.count 13
gdc.plumx.crossrefcites 14
gdc.plumx.mendeley 24
gdc.plumx.scopuscites 16
oaire.citation.endPage 1187
oaire.citation.startPage 1176
person.identifier.scopus-author-id Tan- Barış (7402833947), Karabağ- Oktay (57196390808), Khayyati- Siamak (57212755888)
project.funder.name Funding text 1: This work was supported by TUBITAK (Grant number 221M393) and European Union's Horizon 2020 Research and Innovation Programme [IN4ACT project under grant agreement no. 810318]., Funding text 2: This work was supported by TUBITAK (Grant number 221M393) and European Union’s Horizon 2020 Research and Innovation Programme [IN4ACT project under grant agreement no. 810318 ].
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