A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains

Kusolpuchong, Siwaphong; Chusap, Krerkkiat; Alhawari, Omar; Süer, Gürsel A.

doi:10.1016/j.promfg.2020.01.356

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Additionally, the algorithm may identify opportunities for consolidating shipments to reduce the number of vessel trips and optimize fuel consumption 76,77 . It may also prioritize deliveries based on urgency and demand 78 , allocating resources efficiently to meet critical needs while optimizing overall route efficiency.…”

Section: Utilization Efficiencymentioning

confidence: 99%

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Malashin,

Tynchenko,

Masich

et al. 2024

Preprint

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This study explores groupage delivery optimization through a two-stage GA framework. The goal of optimization is to define locations of regional branch warehouses of Logistics Network to reduce transportation costs. The first stage of the GA focuses on identifying branch warehouses from a list of cities and uses the standard binary string approach, while the second stage solves Vehicle Routing Problem (VRP) using combinatorial GA. This research contributes to advancing logistics optimization methodologies and provides valuable insights for solving Location Routing Problem (LRP) which entails two problems: the Facility Location Problem (FLP) and the Multiple Vehicle Routing Problem (MVRP).

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Section: Utilization Efficiencymentioning

confidence: 99%

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Malashin,

Tynchenko,

Masich

et al. 2024

Preprint

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“…The problem studied in this article was used to provide a model for the optimal sequence of trucks and the cost of operations within the supply chain concerning three objectives (minimizing the cost of transportation, minimizing the sequence of transporting trucks, and minimizing carbon dioxide emissions) [42][43][44][45]. The mathematical model approach is complicated due to the high number of variables and limitations related to the number of trucks sending and receiving and the number of products required to solve the problem.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

Meidutė‐Kavaliauskienė

Sütütemiz

Yıldırım

et al. 2022

Energies

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Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

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“…Así mismo las investigaciones se enfocan en mejorar la sostenibilidad de las cadenas, como por ejemplo, midiendo y reduciendo la huella de carbono generada [23]. Otros trabajos se han enfocado en la optimización de la producción, en problemas específicos como la programación y asignación de turnos de despacho en muelles [24], en sistemas de planeación en cargas [25] y en mejoramiento de mecanismos de distribución en sistemas de bodegas [26].…”

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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A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains

Cited by 16 publications

References 14 publications

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery

Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

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

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