A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation

Mohtashami, Ali

doi:10.1016/j.cie.2015.09.008

Cited by 26 publications

(10 citation statements)

References 44 publications

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“…As the mathematical model of the entire dynamic scheduling of supply inventory distribution is relatively complex, limited by the space, only the mathematical model of the manufacturer [6] is specified: objective:…”

Section: Figure 1: Supply Inventory Distribution Modelmentioning

confidence: 99%

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

2019

IJOMAM

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With the development of China's economy, various manufacturers have formed a supply inventory distribution network to meet more customer demands and improve production efficiency, and the dynamic scheduling of the network is particularly important. However, there are various uncertainties that can affect the stability of operation in the distribution network. In this study, in order to solve the dynamic scheduling problem in uncertain environment, the optimal solution of dynamic scheduling of supply inventory in uncertain environment was calculated based on the adaptive genetic algorithm, the validity of the genetic algorithm and adaptive genetic algorithm was compared and verified by numerical simulation experiment on Matlab, and the influencing factors of cost were analyzed. The results showed that: (1) the adaptive genetic algorithm was more efficient than genetic algorithm in solving the optimal solution of dynamic scheduling; (2) the cost of dynamic scheduling tended to decrease first and then increase with the increase of the maximum lead time of the manufacturers.

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Section: Figure 1: Supply Inventory Distribution Modelmentioning

confidence: 99%

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

2019

IJOMAM

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“…Se puede observar que los autores centrados en el tema de distribución tienen un gran énfasis en el estudio y optimización de inventarios, en donde el 50% de los artículos, como [27], [28], [29], [30], [31], [32], [33], [34], lo tratan y buscan soluciones adecuadas para los inventarios mencionados.…”

Section: Ingeniare Universidad Libre-barranquilla Año 12 No 20 Punclassified

“…Así mismo metodologías como la multi-objetivo desarrollada por [41], o la programación mixta como lo menciona [42]. Por ultimo cabe destacar la metodología de algoritmo genético que genera avances significativos y que desarrollan autores como [30], [38], [33]. Como metodologías adicionales se encuentran la programación lineal, colonia de hormigas, entre otras relevantes, a las que diferentes autores también hacen referencia (40% o 14 de los artículos revisados en las hojas).…”

Section: Ingeniare Universidad Libre-barranquilla Año 12 No 20 Punclassified

Algoritmos aplicados en la programación de las cadenas de suministros para minimizar costos. Revisión de literatura*

2016

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El objetivo del presente estudio fue recopilar la información existente de las herramientas más utilizadas en la planeación de las cadenas de suministros, donde el propósito fundamental es la reducción de los costos. esto se realizó a partir de una aplicación web, llamada tree of science, en donde se encontraron diversas tendencias a partir de las referencias bibliograficas. la revisión brindó conclusiones sobre las investigaciones y aplicaciones de herramientas de inteligencia artificial e investigacion de operaciones, como algoritmos, metaheurísticas , programación lineal, programacion no lineal, programación dinámica y algunos hibridos. Igualmente, el estudio concluyó acerca de los temas más tratados, vacíos de conocimiento y sectores económicos objeto de estudio, así como la identificación de objetivos y variables utilizadas en las publicaciones. Por último, se presentaron las metodologías identificadas y las más relevantes de la literatura. Palabras claves: Cadena de suministro, Algoritmo, Metaheurística, Programación, Costos, Tree of Science, Inteligencia Artificial.

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“…Some authors have studied cross-docking as a network and considered cross-docks in a supply chain system that cross-docks location and customer allocations are decision variables (Jayaraman and Ross 2003;Mousavi et al 2014;Ross and Jayaraman 2008;Seyedhoseini et al 2015). In addition, most of the papers related to the operational decisions focused on the trucks scheduling in cross-docking system (Alpan et al 2011;Arabani et al 2011;Donaldson et al 1998b;Gholami et al 2015;Konur and Golias 2013a, b;Kuo 2013;Liao et al 2014Liao et al , 2013Mohtashami 2015;Soltani and Sadjadi 2010;Vahdani and Zandieh 2010;Yu and Egbelu 2008). In addition, in the cross-docking literature, a few number of researches considered both vehicle routing and cross-docking together.…”

Section: Literature Reviewmentioning

confidence: 99%

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

2017

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Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.

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A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation

Cited by 26 publications

References 44 publications

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

Research on Dynamic Scheduling of Supply Inventory Distribution in Uncertain Environment

Algoritmos aplicados en la programación de las cadenas de suministros para minimizar costos. Revisión de literatura*

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

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