Use Of Genetic Algorithms in Supply Chain Management. Literature Review and Current Trends

2013 ◽  
Vol 27 (1) ◽  
pp. 0-0
Author(s):  
Piotr Stawiński
Keyword(s):  
Supply Chain ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Design Problems ◽  
Research And Practice ◽  
Supply Chain Networks ◽  
The Past ◽  
Current Trends ◽  
Management Literature ◽  
The Subject

For the past few decades SCM has been one of the main objectives in research and practice. Since that time researchers have developed a lot of methods and procedures which optimized this process. To create an efficient supply chain network the resources and factories must be tightly integrated. The most supply chain network designs have multiple layers, members, periods, products, and comparative resources constraints exist between different layers. Supply chain networks design is related to the problems which are very popular in literature. The subject of this paper is to present the variants, configurations and parameters of genetic algorithm (GA) for solving supply chain network design problems. We focus on references from 2000 to 2011. Furthermore, current trends are introduced and discussed.

scholarly journals Modelling and solving spare parts supply chain network design problems

2020 ◽  
Vol 58 (17) ◽  
pp. 5299-5319
Author(s):  
Francisco J. Tapia-Ubeda ◽  
Pablo A. Miranda ◽  
Irene Roda ◽  
Marco Macchi ◽  
Orlando Durán
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Spare Parts ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Design Problems ◽  
Network Design Problems

scholarly journals An Inventory-Location Modeling Structure for Spare Parts Supply Chain Network Design Problems in Industrial End-User Sites

2018 ◽  
Vol 51 (11) ◽  
pp. 968-973 ◽  
Author(s):  
Tapia-Ubeda Francisco J. ◽  
Miranda Pablo A. ◽  
Roda Irene ◽  
Macchi Marco ◽  
Durán Orlando
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Spare Parts ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
End User ◽  
Design Problems ◽  
Network Design Problems ◽  
Location Modeling

scholarly journals Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review

2021 ◽  
Vol 13 (19) ◽  
pp. 10925
Author(s):  
Luis Francisco López-Castro ◽  
Elyn L. Solano-Charris
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Future Research ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Economic Dimension ◽  
The Sustainable Development ◽  
Supply Chain Networks ◽  
Sustainability Criteria ◽  
Development Goals

Nowadays, Supply Chain Networks (SCNs) must respond to economic, environmental, social, and uncertain considerations. Thus, sustainable and resilience criteria need to be incorporated as key criteria into the Supply Chain Network Design (SCND). This paper, as part of an emerging subject, reviews the literature between 2010 and 2021 that integrates sustainability and resilience on the SCND. The article classifies the literature according to the levels of the SCND, levels of the decision-making (i.e., strategic, tactical, and operational), resilience and sustainability criteria, solving approach, objective criteria, contributions to the Sustainable Development Goals (SDGs), and real-world applications. The main findings allow us to conclude that the decisions regarding the supply chain network design with sustainability and resilience criteria are mainly strategic, focusing on the forward flow. Most works address resilience through the evaluation of scenarios (risk assessment perspective), and in terms of the sustainability perspective, authors mainly focus on the economic dimension through the evaluation of income and costs along the chain. Based on the review and the proposed taxonomy, the paper proposes ideas for future research.

Development of a Generic decision support system based on multi-Objective Optimisation for Green supply chain network design (GOOG)

2015 ◽  
Vol 26 (7) ◽  
pp. 1069-1084 ◽  
Author(s):  
Kanda Boonsothonsatit ◽  
Sami Kara ◽  
Suphunnika Ibbotson ◽  
Berman Kayis
Keyword(s):  
Supply Chain ◽  
Environmental Impact ◽  
Lead Time ◽  
Decision Makers ◽  
Green Supply Chain ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective

Purpose – The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs. Design/methodology/approach – GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study. Findings – The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact. Research limitations/implications – Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations. Practical implications – GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives. Social implications – Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society. Originality/value – GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.

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