Capacitated and multiple cross-docked vehicle routing problem with pickup, delivery, and time windows

Ahkamiraad, Azadeh; Wang, Yong

doi:10.1016/j.cie.2018.03.007

Cited by 53 publications

(16 citation statements)

References 26 publications

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“…e genetic algorithm is a well-known and powerful algorithm to solve different vehicle routing problems, reducing delivery costs significantly by producing better solutions. e following papers are proof of this: [25][26][27]46]. And Branke et al [49] showed that the hybrid algorithm could better reduce logistics costs than simple heuristics by developing an evolutionary algorithm (EA), combining savings heuristic for transport channel selection and vehicle routing problem.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach for Optimizing the Supply Chain: A Heuristic-Based Hybrid Algorithm

Kocaoğlu

Çakmak

Gümüş

2020

Mathematical Problems in Engineering

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Managing the distribution of goods is a vital operation for many companies. A successful distribution system requires an effective distribution strategy selection and optimum route planning at the right time and minimum cost. Furthermore, customer’s demand and location can vary from order to order. In this situation, a mixed delivery system is a good solution for it and allows the use of different strategies together to decrease delivery costs. Although the “distribution strategy selection” is a critical issue for companies, there are only a few studies that focus on the mixed delivery network problem. There is a need to propose an efficient solution for the mixed delivery problem to guide researchers and practitioners. This paper develops a new “modified” savings-based genetic algorithm which is named “distribution strategy selection and vehicle routing hybrid algorithm (DSSVRHA).” Our new algorithm aims to contribute to the literature a new hybrid solution to solve a mixed delivery network problem that includes three delivery modes: “direct shipment,” “milk run,” and “cross-docking” efficiently. It decides the appropriate distribution strategy and also optimal routes using a heterogeneous fleet of vehicles at minimum cost. The results of the hybrid algorithm are compared with the results of the optimization model. And the performance of the hybrid algorithm is validated with statistical analysis. The computational results reveal that our developed algorithm provides a good solution for reducing the supply chain distribution costs and computational time.

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Section: Literature Reviewmentioning

confidence: 99%

“…Ahkamiraad and Wang [46] studied a distribution problem with multiple cross-docks, where a set of homogeneous, number, and capacity-limited vehicles with time window was considered.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for Optimizing the Supply Chain: A Heuristic-Based Hybrid Algorithm

Kocaoğlu

Çakmak

Gümüş

2020

Mathematical Problems in Engineering

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“…In the real world, there are many problems that are similar to VRP but do not quite show the same behavior. Many articles propose various types of VRPs, such as VRP with time windows [23][24][25][26][27][28][29][30][31][32], heterogeneous fleets, the so-called multi-depot heterogeneous vehicle routing problem with time windows [25][26][27][28][29][30][31][32], VRP with pickup and delivery, and the multiple-vehicle pickup and delivery problem [33][34][35][36].…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

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“…Also, results showed that a solution approach based on particle swarm optimisation outperformed a genetic algorithm in different test cases. Ahkamiraad and Wang (2018) considered multiple cross-docks, with homogeneous fleets and limited capacity and pickup and delivery nodes with time windows. Their solution approach, a hybrid of genetic algorithm and particle swarm optimisation, outperformed CPLEX for medium and large problem sizes.…”

Section: Network Scheduling With Consolidation Centresmentioning

confidence: 99%

“…Most of the research exploring this kind of network focused on closed networks, i.e., the routes of the vehicles start and end at the same node (e.g. Lee, Jung and Lee (2006), Wen et al (2009), Dondo, Méndez and Cerdá (2011), Vahdani et al (2012), Moghadam, Ghomi and Karimi (2014), Chen et al (2016), Maknoon and Laporte (2017), Enderer, Contardo and Contreras (2017), Ahkamiraad and Wang (2018)). The open network scheduling problem with cross-docking remains, to the best of our knowledge, a rather unexplored case.…”

Section: Network Scheduling With Cross-dockingmentioning

confidence: 99%

Consolidation problems in freight transportation systems: mathematical models and algorithms

Castellucci¹

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Freight distribution systems are under stress. With the world population growing, the migration of people to urban areas and technologies that allow purchases from virtually anywhere, efficient freight distribution can be challenging. An inefficient movement of goods may lead to business not being economically viable and also has social and environmental negative effects. An important strategy to be incorporated in freight distribution systems is the consolidation of goods, i.e., group goods by their destination. This strategy increases vehicles utilisation, reducing the number of vehicles and the number of trips required for the distribution and, consequently, costs, traffic, noise and air pollution. In this thesis, we explore consolidation in three different contexts (or cases) from an optimisation point of view. Each context is related to optimisation problems for which we developed mathematical programming models and solution methods.

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Capacitated and multiple cross-docked vehicle routing problem with pickup, delivery, and time windows

Cited by 53 publications

References 26 publications

A Novel Approach for Optimizing the Supply Chain: A Heuristic-Based Hybrid Algorithm

A Novel Approach for Optimizing the Supply Chain: A Heuristic-Based Hybrid Algorithm

Modified Differential Evolution Algorithm for a Transportation Software Application

Consolidation problems in freight transportation systems: mathematical models and algorithms

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