Optimisation of distribution networks using Genetic Algorithms. Part 2   the Genetic Algorithm and Genetic Operators

Marian, Romeo; Luong, Lee; Akararungruangkul, Raknoi

doi:10.1504/ijmtm.2008.018241

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Accordingly, the best solution among the ones obtained from different runs should be selected. If the number of runs is large enough, this validates the global optimality of the solution [17,18].…”

Section: B Results and Discussionmentioning

confidence: 53%

Optimisation of Resource Scheduling in VCIM Systems Using Genetic Algorithm

Dao¹,

Abhary²,

Marian³

2012

IJARAI

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Abstract-The concept of Virtual Computer-IntegratedManufacturing (VCIM) has been proposed for one and a half decade with purpose of overcoming the limitation of traditional Computer-Integrated Manufacturing (CIM) as it only works within an enterprise. VCIM system is a promising solution for enterprises to survive in the globally competitive market because it can exploit effectively locally as well as globally distributed resources. In this paper, a Genetic Algorithm (GA) based approach for optimising resource scheduling in the VCIM system is proposed. Firstly, based on the latest concept of VCIM system, a class of resource scheduling problems in the system is modelled by using agent-based approach. Secondly, GA with new strategies of handling constraint, chromosome encoding, crossover and mutation is developed to search for optimal solution for the problem. Finally, a case study is given to demonstrate the robustness of the proposed approach.

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Section: B Results and Discussionmentioning

confidence: 53%

Optimisation of Resource Scheduling in VCIM Systems Using Genetic Algorithm

Dao¹,

Abhary²,

Marian³

2012

IJARAI

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“…This follows from the research in [20,21] which show that matrix based encoding can be effective for solving distribution network. Reference [22] have previously shown that matrix based approach is not efficient when it comes to linear transportation problem, but could be effective for non-linear transportation problem.…”

Section: Solution Methodlogy For Optimising Network Designmentioning

confidence: 96%

“…Reference [22] have previously shown that matrix based approach is not efficient when it comes to linear transportation problem, but could be effective for non-linear transportation problem. The problem encountered in [22] is the excessive computational time taken up in the use of specialized genetic operators for matrix-based encoding, which is not the case in [20,21] where segmental crossovers and pseudo-mutation is adopted. Reference [9] also shows that pruffer numbering GA is more effective than matrix-based GA.…”

Section: Solution Methodlogy For Optimising Network Designmentioning

confidence: 99%

An optimization framework for multistage production-distribution networks using genetic algorithms

Lai

Luong

Marian

2008

2008 IEEE International Conference on Industrial Engineering and Engineering Management

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Research in the optimal design of multi-echelon production-distribution networks has been focusing on twoechelon models, which comprise the location-allocation of plants and Distribution Centers subject to specific constraints. Research in two-echelon models could be for two-stage or three-stage optimization model. Currently, almost all the research is in the two-stage model. A threestage model which integrates DC and plant locationallocation decisions with vendor allocation decisions is, however, a more accurate abstraction of the real world, since the prices and transportation costs of raw materials can vary significantly amongst the vendors, depending on their locations viz-a-viz the plants to be opened. This problem is NP-complete and consists of a large number of variables. This paper provides a solution methodology using genetic algorithm for the optimization of a three-stage, twoechelon, multi-product, capacitated single sourcing production-distribution model.

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