Order Allocation using Genetic Algorithm

Hiri, Mariam El; Ennadi, A.; Chafi, Anas

doi:10.25103/jestr.132.17

Cited by 2 publications

(1 citation statement)

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“…Hu et al [12] tried to solve the storage position allocation problem using the artificial bee colony algorithm in their work. El Hiri et al [13] proposed a Genetic Algorithm (GA) based model to solve order allocation quantities problem according to their needs in a multi-supplier environment.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

Yilmaz

Tuncer

2020

International Journal of Applied Mathematics Electronics and Computers

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Efficient use of warehouse resources is an important issue that makes them more manageable and useful, also helps product flow faster. In multidimensional warehouses with many constraints such as weight, volume, product compatibility, etc., storage and retrieval processes are complex optimization problems that need to be solved. Considering the number of constraints, the solution to the storage and retrieval problems with traditional algorithms take a long time. Meta-heuristic algorithms are frequently used in the solution of many complex optimization problems as they can provide acceptable solutions in a short time. In this study, the Genetic algorithm which is one of the popular meta-heuristic methods was used to solve this problem, and the A-star algorithm was used to travel the shortest path between the shelves. A three-dimensional warehouse with operational constraints was designed. Storage and retrieval orders containing a different number of pallets were produced randomly to perform warehouse product flow, and some of these orders were assumed as storage requests and the remainder were retrieval requests. Results show that the proposed approach is capable of finding effective solutions for storage and retrieval problems with operational constraints in a short time.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

Yilmaz

Tuncer

2020

International Journal of Applied Mathematics Electronics and Computers

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A new model development for demand forecasting of supply chain from data fusion perspective

Wang

Guo-ping

Xiong

2021

IFS

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In the increasingly competitive society, the supply chain, as the third profit center of the enterprise, makes scholars and industry-related with supply chain more and more interesting in the study of relevant issues. Exiting studies believed that an application of predictive analytics could be a tremendous impact on supply chain management. The uncertainty of supply chain demand and bullwhip effect is a challenge in the supply chain. Data fusion can effectively reduce the uncertainty of demand and the amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation and to providing reasonable decision information for supply chain managers.

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Order Allocation using Genetic Algorithm

Cited by 2 publications

References 0 publications

Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

A new model development for demand forecasting of supply chain from data fusion perspective

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