Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/206194
Title: Decision support using multicriteria decision making for reverse logistics networks
Researcher: Uday K. Chhaya
Guide(s): M.B. Patel
Keywords: Reverse Logistics, MILP, Lingo
University: Gujarat Technological University
Completed Date: 25-01-2018
Abstract: Logistics is a key enabler for growth of the retail commerce and product manufacturing industry, and is increasingly emerging as a differentiator in terms of customer service and satisfaction. The logistics sector specific to manufactured product retailing in India was valued at US $ 0.46 billion in 2016 and is projected to witness a CAGR of nearly 45-48 per cent in the upcoming five years to reach US $ 2.2 billion by 2020. (Source: Inc42 report, Mar 2017). Reverse logistics has attained more and more pertinence during the recent years, as the economics and control over product returns is becoming far more crucial for industry, economy, and environment sustainability. Customers expect a seamless, economical and extended product usability, cost-efficient reuse thereof and safe disposal at its end-of-life. This focus leaves reverse logistics far more relevant in modern times. Because of the fluctuation and uncertainty in both quantity and quality of the reverse product returns flow, design and planning of reverse logistics network is much more complicated compared to the forward supply chain. Huge potentials and implications for acute optimization and seamless integration with the forward supply chain has necessitated focus on optimization of different entities/components of the reverse logistics components. This could be accomplished by development of decision support tools for designing reverse logistics network in an economically efficient and environment friendly manner. This research work, largely set up in Indian perspective, develops a conceptual framework of multi-criteria decisions involved in reverse logistics network configurations, identifies sector-specific network configuration preferences and validates it through multi-sector industry survey. A sensitivity analysis that determines cross-overs of prioritization in network preference is also validated. Further, a generic mathematical formulation using Mixed Integer Linear Programming is adapted for a typical multi-stage, multi-facility revere logisti
Pagination: 
URI: http://hdl.handle.net/10603/206194
Appears in Departments:Mechanical Engineering

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01_title.pdfAttached File156.02 kBAdobe PDFView/Open
02_certificate.pdf319.17 kBAdobe PDFView/Open
03_abstract.pdf111.68 kBAdobe PDFView/Open
04_declaration.pdf184.67 kBAdobe PDFView/Open
05_acknowledgement.pdf243.22 kBAdobe PDFView/Open
06_contents.pdf248.68 kBAdobe PDFView/Open
07_list_of_tables.pdf190.29 kBAdobe PDFView/Open
08_list_of_figures.pdf299.55 kBAdobe PDFView/Open
09_abbreviations.pdf6.29 kBAdobe PDFView/Open
10_chapter1.pdf909.35 kBAdobe PDFView/Open
11999719014_thesis_25012018.pdf4.43 MBAdobe PDFView/Open
11_chapter2.pdf864.85 kBAdobe PDFView/Open
12_chapter3.pdf1.4 MBAdobe PDFView/Open
13_chapter4.pdf610.66 kBAdobe PDFView/Open
14_conclusion.pdf1.09 MBAdobe PDFView/Open
15_summary.pdf224.51 kBAdobe PDFView/Open
16_bibliography.pdf298.62 kBAdobe PDFView/Open


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