An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem

Saif-Eddine, Ahmad Sayed; El-Beheiry, Mohammed M.; El-Kharbotly, Amin K.

doi:10.1016/j.asej.2018.09.002

Cited by 40 publications

(18 citation statements)

References 18 publications

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“…There have been different divisions of the study on reducing costs, and in some studies, this goal has even been replaced by maximizing profits by focusing on the difference between income and expenses. The cost variables include the costs of establishing a place for production [35] and a place for distribution [36], determining a location for retail [35], transportation costs [37], raw material costs [9], ordering costs [10], production costs [38], delayed payment penalties [38], cost of lost orders [39], inventory maintenance cost [37], cost of returned perishable goods [38], shipping costs for returned goods, and environmental costs.…”

Section: Model Formulationmentioning

confidence: 99%

The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty

Meidutė‐Kavaliauskienė

Yıldırım

Ghorbani

et al. 2022

Sustainability

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The value of superior supply network design is becoming increasingly important, especially in the perishable supply chain. Due to the recent developments in perishable products, perishable product supply chain (PPSC) management has attracted many researchers. The purpose of this study was to present a multi-period and multi-echelon perishable supply chain with regards to procurement time, cycle cost, and customer satisfaction. This study presented a new form of location-routing in a supply chain network for perishable products, accounting for environmental considerations, cost, procurement time, and customer satisfaction, such that the total costs, delivery time, and the emission of pollutants in the network were minimized while customer satisfaction was maximized. We formulated the problem as a multi-objective, nonlinear, mixed-integer program and the hybrid approach was proposed to solve the model. The mean error of the proposed algorithm for the objective function compared to the exact method in solving the sample problems was less than 3.4%. The computational results revealed the efficiency of the proposed algorithm for a wide range of issues of various sizes.

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Section: Model Formulationmentioning

confidence: 99%

The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty

Meidutė‐Kavaliauskienė

Yıldırım

Ghorbani

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The researchers in Nguyen et al (2019) used a hybrid optimization method based on differential evolution, iterated greedy search, mixed integer programming as well as parallel computing to solve the problem of resource-constrained job scheduling for large-scale instances. The problem of supply chains was tackled also in Saif-Eddine et al (2019), where specifically the total supply chain cost was optimized. Since this belongs to the group of NP-hard problems, an improved genetic algorithm was designed and used to address the problem.…”

Section: Use Of Evolutionary Computation In the Context Of Industry 40mentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

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Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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“…Por último, y no menos importante se presentan aportes de los AG en el área de almacenamiento y gestión de inventarios [45][46][47][48][49][50][51] y en el área de selección, evaluación de proveedores respecto a una demanda variable [52][53][54][55][56][57].…”

Section: Algoritmos Genéticosunclassified

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Palacio

2021

Rev. politec.

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La inteligencia artificial ofrece grandes oportunidades para la cadena de suministro, siendo esto una ventaja competitiva para el mercado cambiante de hoy en día. Este artículo tiene como objetivo identificar los impactos y oportunidades que puede ofrecer el software con inteligencia artificial para facilitar la operación y mejorar el desempeño de la cadena de suministro en el sector bananero de Colombia. La metodología de trabajo consta de seis pasos en donde se obtuvo un total de 72 investigaciones. Las fuentes de información fueron cuatro bases de datos. Como conclusión principal, la cadena de suministro del sector bananero tiene todo lo necesario para que se implementen soluciones basadas en software inteligente con el fin de lograr una adaptación, flexibilidad y sensibilidad al contexto y dominio de ejecución. Artificial intelligence offers great opportunities for the supply chain, making it a competitive advantage for today's changing market. This paper aims to identify the impacts and opportunities that artificial intelligence software can offer to supply chain in the Colombian banana sector to facilitate the operation and improve the performance. The searching method consists of six steps getting 72 investigations finally. The sources of information were four databases. The main conclusion is the supply chain of the banana sector has everything for implementation of solutions based on intelligent software in order to achieve adaptation, flexibility and context awarenes and execution domain.

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An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem

Cited by 40 publications

References 18 publications

The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty

The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty

Computational Intelligence in the Context of Industry 4.0

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

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