Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/306384
Title: Optimization of inventory vehicle routing and distribution center problems in a supply chain net work using meta heuristics algorithms
Researcher: Anix Joel Singh J
Guide(s): Saravanan M
Keywords: Engineering and Technology
Engineering
Engineering Mechanical
Inventory Routing Problem
Stochastic
Meta Heuristics
University: Anna University
Completed Date: 2019
Abstract: In this research work an inventory vehicle routing and distribution center were the most significant issues in supply chain network problems that has been proposed Inventory routing problem in a supply chain is made to determine delivery routes from suppliers to some retailers It consists of one supplier and multiple retailers who face a stochastic demand that is assumed to be independently and identically distributed over an infinite planning horizon It has been focused to reduce inventories mainly by eliminating inventory problems that are holding costs and demand distribution for products Vehicle routing problem allows direct shipping of goods from manufacturer storages to customers It satisfies customer demands at minimum total route cost and minimum of the penalties of low quality service It designs routes for delivery vehicles which operates from a single deport to supply a set of customers with locations and demands for a certain commodity Distribution centers location problem is concerned with the selection of distribution center from the potential set so that the total relevant cost is minimized Cross docking is an approach that can reduce inventories and lead times on customer response time Total cost and space requirement for inventory can be cut down Meta Heuristics is used to find the best sequence of inbound and outbound vehicles so that the objective of minimizing the total operation time is called make span The inventory vehicle routing and distribution center are widely preferred configurations in supply chain network due to their handling flexibility and agility in accommodating new meta heuristics algorithms methods. newline
Pagination: xviii, 149p
URI: http://hdl.handle.net/10603/306384
Appears in Departments:Faculty of Mechanical Engineering

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01_title.pdfAttached File104.48 kBAdobe PDFView/Open
02_certificates.pdf503.79 kBAdobe PDFView/Open
03_abstracts.pdf101.22 kBAdobe PDFView/Open
04_acknowledgements.pdf47.87 kBAdobe PDFView/Open
05_contents.pdf106.74 kBAdobe PDFView/Open
06_list_of_tables.pdf101.63 kBAdobe PDFView/Open
07_list_of_figures.pdf52.2 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf152.07 kBAdobe PDFView/Open
09_chapter1.pdf399.89 kBAdobe PDFView/Open
10_chapter2.pdf302.2 kBAdobe PDFView/Open
11_chapter3.pdf552.34 kBAdobe PDFView/Open
12_chapter4.pdf442.04 kBAdobe PDFView/Open
13_chapter5.pdf391.99 kBAdobe PDFView/Open
14_chpater6.pdf544.14 kBAdobe PDFView/Open
15_chapter7.pdf560.44 kBAdobe PDFView/Open
16_conclusion.pdf108 kBAdobe PDFView/Open
17_references.pdf265.17 kBAdobe PDFView/Open
18_list_of_publications.pdf166.44 kBAdobe PDFView/Open
80_recommendation.pdf100.45 kBAdobe PDFView/Open


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