An ERNSGA‐III algorithm for the production and distribution planning problem in the multiagent supply chain

2019 ◽  
Author(s):  
Ali Gharaei ◽  
Fariborz Jolai
Keyword(s):  
Supply Chain ◽  
Planning Problem ◽  
Distribution Planning ◽  
Production And Distribution ◽  
Production And Distribution Planning

Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm

2012 ◽  
Vol 1 (1) ◽  
pp. 38-54
Author(s):  
Babak Sohrabi ◽  
MohammadReza Sadeghi Moghadam
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Minimum Cost ◽  
Industrial Firms ◽  
Distribution Planning ◽  
Supply Chain Costs ◽  
Production And Distribution ◽  
Holding Cost ◽  
Production And Distribution Planning ◽  
Effective Use

The present study, using genetic algorithm, tries to improve material flow management in supply chain. Consequently, in this paper, an integrated supply-production and distribution planning (SPDP) is considered despite the fact that in most of the Iranian industrial firms, SPDP is done independently. The effective use of integrated SPDP not only enhances the performance rather decreases inventory cost, holding cost, shortage cost and overall supply chain costs. A quantitative mathematical model is used to the problem articulation, and then it is solved by applying heuristic genetic algorithm (GA) method. The proposed model with genetic algorithm could provide the best satisfactory result with the minimum cost. The reliability test was carried by comparing the model results with that of the amount of variables.

Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm

2012 ◽  
pp. 1316-1333
Author(s):  
Babak Sohrabi ◽  
MohammadReza Sadeghi Moghadam
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Minimum Cost ◽  
Industrial Firms ◽  
Distribution Planning ◽  
Supply Chain Costs ◽  
Production And Distribution ◽  
Holding Cost ◽  
Production And Distribution Planning ◽  
Effective Use

The present study, using genetic algorithm, tries to improve material flow management in supply chain. Consequently, in this paper, an integrated supply-production and distribution planning (SPDP) is considered despite the fact that in most of the Iranian industrial firms, SPDP is done independently. The effective use of integrated SPDP not only enhances the performance rather decreases inventory cost, holding cost, shortage cost and overall supply chain costs. A quantitative mathematical model is used to the problem articulation, and then it is solved by applying heuristic genetic algorithm (GA) method. The proposed model with genetic algorithm could provide the best satisfactory result with the minimum cost. The reliability test was carried by comparing the model results with that of the amount of variables.

An Efficient Approach for Solving Integrated Production and Distribution Planning Problems

2020 ◽  
Vol 10 (2) ◽  
pp. 25-44
Author(s):  
Besma Zeddam ◽  
Fayçal Belkaid ◽  
Mohammed Bennekrouf
Keyword(s):  
Supply Chain ◽  
Research Field ◽  
Sustainable Supply Chain ◽  
Integrated Planning ◽  
Routing Problem ◽  
Distribution Planning ◽  
Production And Distribution ◽  
Production And Distribution Planning ◽  
Lp Metric ◽  
Production Routing

The increasing customer expectations for customized products of high quality in short delays and the worldwide competition in terms of quality and costs have pushed industries to implement new strategies to manage their supply chain decisions. In this context, the integrated planning is becoming the most dominant over the operational research field because of its efficiency and its ability to cover the different aspects of the problem. Production routing problem is one of the problems of the integrated planning that is of interest in optimizing simultaneously production, inventory, and distribution planning. This paper has the purpose of developing two mono-objective models for the production-routing problem; one of them minimizes the total costs, while the other one minimizes the energy consumed by the production system. Finally, a bi-objective model is proposed to combine the two objectives mentioned previously using the LP-metric method in the context of a sustainable supply chain. Experimental results are also presented and discussed through the different scenarios.

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