Handling uncertainty in agricultural supply chain management: A state of the art
| Autor: | Jean Bourtembourg, Faicel Hnaien, Valeria Borodin, Nacima Labadie |
|---|---|
| Přispěvatelé: | Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Société Coopérative Agricole de la Région d'Arcis-sur-Aube (SCARA), Société Coopérative Agricole de la Région d'Arcis-sur-Aube, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2016 |
| Předmět: |
Information Systems and Management
General Computer Science Supply chain 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research State of the art Uncertainty modeling Industrial and Manufacturing Engineering Lead (geology) Order (exchange) 0202 electrical engineering electronic engineering information engineering Economics Supply chain management 2. Zero hunger 021103 operations research business.industry Management science [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] OR in agriculture Risk analysis (engineering) Agriculture Modeling and Simulation Agricultural supply chain 020201 artificial intelligence & image processing State (computer science) business |
| Zdroj: | European Journal of Operational Research European Journal of Operational Research, Elsevier, 2016, ⟨10.1016/j.ejor.2016.03.057⟩ European Journal of Operational Research, 2016, ⟨10.1016/j.ejor.2016.03.057⟩ |
| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2016.03.057 |
| Popis: | International audience; Given the evolution in the agricultural sector and the new challenges it faces, managing agricultural supply chains efficiently has become an attractive topic for researchers and practitioners. Against this background, the integration of uncertain aspects has continuously gained importance for managerial decision making since it can lead to an increase in efficiency, responsiveness, business integration, and ultimately in market competitiveness. In order to capture appropriately the uncertain conjuncture of most agricultural real-life applications, an increasing amount of research effort is especially dedicated to treating uncertainty. In particular, quantitative modeling approaches have found extensive use in agricultural supply chain management. This paper provides an overview of the latest advances and developments in the application of operations research methodologies to handling uncertainty occurring in the agricultural supply chain management problems. It seeks to: (i) offer a representative overview of the predominant research topics, (ii) highlight the most pertinent and widely used frameworks, and (iii) discuss the emergence of new operations research advances in the agricultural sector. The broad spectrum of reviewed contributions is classified and presented with respect to three most relevant discerned features: uncertainty modeling types, programming approaches, and functional application areas. Ultimately, main review findings are pointed out and future research directions which emerge are suggested. |
| Databáze: | OpenAIRE |
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