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Green logistics for regional industr...

Pourmohammadi, Hamid.

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Green logistics for regional industrial waste materials and by-products.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Green logistics for regional industrial waste materials and by-products./
Author:	Pourmohammadi, Hamid.
Description:	161 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6204.
Contained By:	Dissertation Abstracts International66-11B.
Subject:	Engineering, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3196877
ISBN:	0542427516

Green logistics for regional industrial waste materials and by-products.
Pourmohammadi, Hamid.

Green logistics for regional industrial waste materials and by-products. - 161 p.

Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6204.

Thesis (Ph.D.)--University of Southern California, 2005.

In this study, a reverse logistics model is developed to minimize the environmental and operational costs of exchanging waste and by-product materials in a business-to-business network. One of the goals of the research is to investigate the opportunities for substituting raw materials usage with value added waste/by-product materials collected from a regional network. The network contains firms, collection centers, value added process centers (e.g. disassembly, recycling, or remanufacturing), disposal centers, and virgin material market. The developed reverse logistics model is a mixed integer linear model. The model captures environmental costs as well as the operational costs. Since the problem is NP-hard, we developed a Genetic Algorithm (GA) to efficiently solve the model for large problem instances. A case study on the Aluminum industry in LA County was administered to validate the proposed approach. An Economic Input-Output (EIO) clustering method and Principal Component Analysis (PCA) were employed to develop industrial clusters in the region. Studying these clusters expanded our knowledge regarding materials flow in the region. Based on the outcome of the clustering, Electronics industries cluster was discovered to be one of the major links in terms of the economics indices. A survey study was administered among the cluster firms in order to discover supply and demand linkages between the industries. The data obtained in this stage was also used for the reverse logistics model implementation.

ISBN: 0542427516Subjects--Topical Terms:

626639
Engineering, Industrial.

Green logistics for regional industrial waste materials and by-products.
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500 $a Advisers: Maged Desouky; Mansour Rahimi.
502 $a Thesis (Ph.D.)--University of Southern California, 2005.
520 $a In this study, a reverse logistics model is developed to minimize the environmental and operational costs of exchanging waste and by-product materials in a business-to-business network. One of the goals of the research is to investigate the opportunities for substituting raw materials usage with value added waste/by-product materials collected from a regional network. The network contains firms, collection centers, value added process centers (e.g. disassembly, recycling, or remanufacturing), disposal centers, and virgin material market. The developed reverse logistics model is a mixed integer linear model. The model captures environmental costs as well as the operational costs. Since the problem is NP-hard, we developed a Genetic Algorithm (GA) to efficiently solve the model for large problem instances. A case study on the Aluminum industry in LA County was administered to validate the proposed approach. An Economic Input-Output (EIO) clustering method and Principal Component Analysis (PCA) were employed to develop industrial clusters in the region. Studying these clusters expanded our knowledge regarding materials flow in the region. Based on the outcome of the clustering, Electronics industries cluster was discovered to be one of the major links in terms of the economics indices. A survey study was administered among the cluster firms in order to discover supply and demand linkages between the industries. The data obtained in this stage was also used for the reverse logistics model implementation.
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