In this paper, we consider multi-party coordination in a supply chain (SC) that consists of a set of independent producers and a set of resource managers. A decentralised decision-making approach is proposed for a coal SC, with three independent parties - multiple mines, a rail operator and a terminal. The rail operator and the terminal act as common resource managers and connects the independent mines via a rail network. The objective of this SC is to efficiently use an independent rail operator to transport coal from different mines to meet the shipping demand at the terminal. The underlying coordination problem can be seen as a multi-resource constrained scheduling problem. A major part of this paper addresses the key challenges in a decentralised approach based on column generation (CG), which are to compute the value of a column, better upper bounds and to update the multipliers using decentralised methods. We have also discussed the mathematical models for different decision units, the CG algorithm and different strengthening methods. A comprehensive computational experiment based on randomly generated instances highlights the effect of decentralisation and the value of information-sharing. The proposed solution approaches can be extended to a multi-party case with any number of common resources.
机构:
ENSAIT Text Inst, GEMTEX Lab, F-59056 Roubaix, France
Univ Lille Nord France, Automat Genie Informat Traitement Signal & Images, F-59000 Lille, France
Soochow Univ, Sch Text & Clothing Engn, Suzhou 215006, Peoples R China
Univ Boras, Dept Business Adm & Text Management, S-50190 Boras, SwedenENSAIT Text Inst, GEMTEX Lab, F-59056 Roubaix, France
Sirilertsuwan, Petchprakai
Thomassey, Sebastien
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Univ Lille Nord France, Automat Genie Informat Traitement Signal & Images, F-59000 Lille, FranceENSAIT Text Inst, GEMTEX Lab, F-59056 Roubaix, France
Thomassey, Sebastien
Zeng, Xianyi
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Univ Lille Nord France, Automat Genie Informat Traitement Signal & Images, F-59000 Lille, FranceENSAIT Text Inst, GEMTEX Lab, F-59056 Roubaix, France