Ecodesign of Chemical Processes with Multi‐Objective Genetic Algorithms

Azzaro‐Pantel, Catherine; Ouattara, Adama; Pibouleau, Luc

doi:10.1002/9781118341704.ch12

Cited by 2 publications

(1 citation statement)

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“…In order to directly improve the overall performance of the Supplier Network (SN) by incorporating a long-term partnership in which an interchange of technological knowledge and risk sharing is made by contract, a multi-objective optimization formulation is proposed. This approach allows the consideration of multiple and possibly antagonistic objectives to be concurrently optimized [23,42].…”

Section: Partner For Sustainability: Supplier Network Modelmentioning

confidence: 99%

Green Supplier Selection in the Agro-Food Industry with Contract Farming: A Multi-Objective Optimization Approach

et al. 2019

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An important contribution to the environmental impact of agro-food supply chains is related to the agricultural technology and practices used in the fields during raw material production. This problem can be framed from the point of view of the Focal Company (FC) as a raw material Green Supplier Selection Problem (GSSP). This paper describes an extension of the GSSP methodology that integrates life cycle assessment, environmental collaborations, and contract farming in order to gain social and environmental benefits. In this approach, risk and gains are shared by both parties, as well as information related to agricultural practices through which the FC can optimize global performance by deciding which suppliers to contract, capacity and which practices to use at each supplying field in order to optimize economic performance and environmental impact. The FC provides the knowledge and technology needed by the supplier to reach these objectives via a contract farming scheme. A case study is developed in order to illustrate and a step-by-step methodology is described. A multi-objective optimization strategy based on Genetic Algorithms linked to a MCDM approach to the solution selection step is proposed. Scenarios of optimization of the selection process are studied to demonstrate the potential improvement gains in performance.

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Section: Partner For Sustainability: Supplier Network Modelmentioning

confidence: 99%