A Genetic Algorithm Approach for Integrated Production and Distribution Problem

Abraham, Anand; Kumar, Kunal; Sridharan, R.; Singh, Dipti

doi:10.1016/j.sbspro.2015.03.213

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…There are seven demand points need to be delivered and then select distribution path based on the order [18]. Code the path as 0-32-40-22-34-35-6-3-01-2-3-4-5-6-7-8, firstly extract 0-32, 0 indicates distribution center and 32 represents No.…”

Section: Process Of Genetic Algorithmmentioning

confidence: 99%

“…Deal with illegal chromosome through pretreatment operator. Find two endpoints 10 and 17 of overload path on overload path D bk3 = [10,13,18,8,9,1,5,17] and adjust chromosome through moving breakpoint location. After finding out endpoints 10 and 17, determine to move the 10 point to the left or move the 17 point to the right through comparing the vehicle load capacity of the path D bk3 = [14,15] on the left of 10 and path D bk3 = [11,7,6] on the right of 17.…”

Section: Initial Population and Population Pretreatmentmentioning

confidence: 99%

See 1 more Smart Citation

Research and Application of Supply Chain Distribution Optimization Model based on Improved Genetic Algorithm

Wang¹,

Chen²

2016

IJUNESST

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Section: Process Of Genetic Algorithmmentioning

confidence: 99%

Section: Initial Population and Population Pretreatmentmentioning

confidence: 99%

Research and Application of Supply Chain Distribution Optimization Model based on Improved Genetic Algorithm

Wang¹,

Chen²

2016

IJUNESST

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show abstract

“…In recent years, numerous articles have been working in this field (Abraham et al (2015), Zhang et al (2015), Sukkerd and Wuttipornpun (2016)).…”

Section: Review Of the Literaturementioning

confidence: 99%

An Optimized Mathematical Model for Items Supplies Planning of a Logistic System

Khalili¹,

Razavi²,

Razavi³

2016

MAS

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Items supplies planning of a logistic system is one of the major issue in operations research. In this article the aim is to determine how much of each item per month from each supplier logistics system requirements must be provided. To do this, a novel multi objective mixed integer programming mathematical model is offered for the first time. Since in logistics system, delivery on time is very important, the first objective is minimization of time in delivery on time costs (including lack and maintenance costs) and the cost of purchasing logistics system. The second objective function is minimization of the transportation supplier costs. Solving the mathematical model shows how to use the Multiple Objective Decision Making (MODM) can provide the ensuring policy and transportation logistics needed items. This model is solved with CPLEX and computational results show the effectiveness of the proposed model.

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Two effective metaheuristic algorithms for solving a stochastic optimization model of a multi-echelon supply chain

Khalifehzadeh

Fakhrzad

Mehrjerdi

et al. 2019

Applied Soft Computing

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A Genetic Algorithm Approach for Integrated Production and Distribution Problem

Cited by 7 publications

References 5 publications

Research and Application of Supply Chain Distribution Optimization Model based on Improved Genetic Algorithm

Research and Application of Supply Chain Distribution Optimization Model based on Improved Genetic Algorithm

An Optimized Mathematical Model for Items Supplies Planning of a Logistic System

Two effective metaheuristic algorithms for solving a stochastic optimization model of a multi-echelon supply chain

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