Designing a Reverse Logistics Network for End‐of‐Life Vehicles Recovery | Zendy

Masoud Zarei | Zendy; Saeed Mansour | Zendy; Ali Husseinzadeh Kashan | Zendy; Behrooz Karimi | Zendy

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Designing a Reverse Logistics Network for End‐of‐Life Vehicles Recovery

Author(s) -

Masoud Zarei,

Saeed Mansour,

Ali Husseinzadeh Kashan,

Behrooz Karimi

Publication year - 2010

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2010/649028

Subject(s) - directive , reverse logistics , process (computing) , product (mathematics) , order (exchange) , quality (philosophy) , operations research , european union , genetic algorithm , risk analysis (engineering) , computer science , engineering , environmental economics , transport engineering , manufacturing engineering , business , economics , marketing , supply chain , philosophy , geometry , mathematics , finance , epistemology , machine learning , economic policy , programming language , operating system

The environmental factors are receiving increasing attention in different life cycle stages of products. When a product reaches its End-Of-Life (EOL) stage, the management of its recovery process is affected by the environmental and also economical factors. Selecting efficient methods for the collection and recovery of EOL products has become an important issue. The European Union Directive 2000/53/EC extends the responsibility of the vehicle manufacturers to the postconsumer stage of the vehicle. In order to fulfill the requirements of this Directive and also efficient management of the whole recovery process, the conceptual framework of a reverse logistics network is presented. The distribution of new vehicles in an area and also collecting the End-of-Life Vehicles (ELVs) and their recovery are considered jointly. It is assumed that the new vehicles distributors are also responsible for collecting the ELVs. Then a mathematical model is developed which minimizes the costs of setting up the network and also the relevant transportation costs. Because of the complexity of the model, a solution methodology based on the genetic algorithm is designed which enables achieving good quality solutions in a reasonable algorithm run time

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