Please use this identifier to cite or link to this item:
Title: Heuristic algorithms for the multi-period fixed charge models
Researcher: Balaji N
Guide(s): Jawahar N
Keywords: Multiperiod distribution problem
Fixed charge transportation
Non-deterministic Polynomial-time
Simulated Annealing Algorithm
Genetic Algorithm
Equivalent Variable Cost
Upload Date: 15-Jul-2013
University: Anna University
Completed Date: 01/06/2011
Abstract: The multi-period fixed charge problem is an extension of the multiperiod distribution problem and general fixed charge transportation problem, where the time based decisions on the size of the shipments, simultaneous consideration of both suppliers and customers. Inventories and backorders/subcontracts can make an economical distribution. The conventional transportation problem considers only per unit cost of transportation. The other that has wide acceptance is FCT problem. Concerning the above, this thesis addresses four multiperiod fixed charge models. They are (1) Multi-period fixed charge distribution problem associated with backorder and inventories.(2) Multi-period fixed charge distribution problem associated with subcontract and inventories. (3) Multi-period fixed charge production-distribution problem associated with backorder and inventories. (4) Multi-period fixed charge production-distribution problem associated with subcontract and inventories. In recent years, problem specific simple heuristic algorithm, neighbourhood search based Simulated Annealing Algorithmand population search based Genetic Algorithm and have been increasingly applied to various search and optimization problems and have emerged as potential techniques to provide solutions with acceptable accuracy for NPhard problems. In the light of the above consideration, this thesis proposes Equivalent Variable Cost heuristic from simple problem specific heuristics, from neighbourhood search based heuristics and GA from population search based heuristics to solve the above four multi-period fixed charge models to minimize the total cost. The proposed heuristics are evaluated for their solution quality by comparing them with lower bound value and LINGO solutions. The comparison reveals that the proposed SAA and GA generate better solutions than the EVC heuristic solutions and are capable of providing solution either equal or close to the lower bound value and optimal solution of the problems.
Pagination: xxvii, 225p.
Appears in Departments:Faculty of Mechanical Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File56.21 kBAdobe PDFView/Open
02_certificates.pdf226.72 kBAdobe PDFView/Open
03_abstract.pdf54.06 kBAdobe PDFView/Open
04_acknowledgement.pdf59.14 kBAdobe PDFView/Open
05_contents.pdf146.36 kBAdobe PDFView/Open
06_chapter 1.pdf124.93 kBAdobe PDFView/Open
07_chapter 2.pdf151.5 kBAdobe PDFView/Open
08_chapter 3.pdf213.65 kBAdobe PDFView/Open
09_chapter 4.pdf173.29 kBAdobe PDFView/Open
10_chapter 5.pdf411.24 kBAdobe PDFView/Open
11_chapter 6.pdf575.58 kBAdobe PDFView/Open
12_chapter 7.pdf563.88 kBAdobe PDFView/Open
13_chapter 8.pdf451.09 kBAdobe PDFView/Open
14_chapter 9.pdf73.58 kBAdobe PDFView/Open
15_appendix.pdf275.9 kBAdobe PDFView/Open
16_references.pdf98.41 kBAdobe PDFView/Open
17_publications.pdf60.86 kBAdobe PDFView/Open
18_vitae.pdf50.85 kBAdobe PDFView/Open

Items in Shodhganga are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: