Capacitated vehicle routing problem model for carriers

Rojas-Cuevas, Irma-Delia; Caballero‐Morales, Santiago‐Omar; Martínez‐Flores, José‐Luis; Mendoza-Vázquez, José-Rafael

doi:10.4102/jtscm.v12i0.345

Cited by 11 publications

(14 citation statements)

References 28 publications

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“…In their work, there was 15.7% improvement on opt − A − n33 − k5 instance. Rojas-Cuevas et al, [34] used Capacitated Vehicle Routing Problem for Carriers (CVRPfC) to solve distribution problem. The route planning were obtained through CVRPfC and the authors concluded that the CVRPfC can improve the competitiveness of the carriers by providing better fares to their customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Capacitated vehicle routing problem with column generation and reinforcement learning techniques

İbrahim¹,

Ishaya²,

Lo³

et al. 2020

Open J. Discret. Appl. Math.

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Capacitated vehicle routing problem is one of the variants of the vehicle routing problem which was studied in this research. In this research we applied a reinforcement learning algorithm to find set of routes from a depot to the set of customers while also considering the capacity of the vehicles, in order to reduce the cost of transportation of goods and services. Each vehicle originates from a depot, service the customers and return to the depot. We compare the reinforcement learning model with an exact method; column generation and Google's OR-tool. Our objective is to solve a large-size of problem to near-optimality. We were able to use reinforcement learning to solve upto 101 nodes to near-optimality.

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Section: Literature Reviewmentioning

confidence: 99%

Capacitated vehicle routing problem with column generation and reinforcement learning techniques

İbrahim¹,

Ishaya²,

Lo³

et al. 2020

Open J. Discret. Appl. Math.

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show abstract

“…In Ref. [14], Rojas-Cuevas et al proposed a model named Capacitated Vehicle Routing Problem for Carriers to obtain optimal route planning. The distribution scenario they worked on deals with a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot.…”

Section: Literature Surveymentioning

confidence: 99%

Revenue Management Focused Capacitated Vehicle Routing Model in Supply Chain Management

Örnek¹

2020

IJST

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Objectives: To propose revenue management focused model for Capacitated Vehicle Routing Problem with Simultaneous Pickup and Delivery and pursue a solution to it. In some industries, customer demand for delivery and pickup fluctuates and the vehicle capacities are limited. Existing research focuses on either Revenue Maximization or Transportation Cost Minimization. To be able to run such businesses profitably, it is necessary to consider both objectives by the help of the proposed model. Dynamic pricing will be used for revenue maximization, by booking early reservations for a lower price. Vehicle Routing Problem will determine the route to minimize the total transportation cost. Methods: The capacitated vehicle routing model with simultaneous pickup and delivery is combined with a revenue management model in which low price early reservations are allowed. In case the customer orders cannot be handled with existing vehicle capacities, some of the early reservation orders are canceled and a cancellation fee is incurred. The model is solved with a mixed-integer linear programming (MILP) solver. Findings: The proposed model is tested against a real-world data set. The result shows that unprofitable customer orders are determined by the model and rejected despite there is a cancellation fee. By applying the revenue management model, it is observed that there is an increase in the total profit. Novelty: To the best of our knowledge, this study proposed a revenue management focused MILP model for VRP with simultaneous pickup and delivery for the first time.

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“…As geo-location data is mainly obtained from smart phones and other mobile devices used by people and organizations, using and sharing this data by developers of Geographic Information System (GIS) applications has been the subject of debate and discussion. This is due to concerns regarding the privacy and confidentiality of this data (Richardson, 2019;Blatt, 2012). Although security technology and government regulations are frequently developed and established to ensure the ethical use of geolocation data, publicly available databases are the main resource for academic and research purposes.…”

Section: Development Of Test Instancesmentioning

confidence: 99%

Development of a coded suite of models to explore relevant problems in logistics

Caballero‐Morales

2020

PeerJ Computer Science

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Logistics is the aspect of the supply chain which is responsible of the efficient flow and delivery of goods or services from suppliers to customers. Because a logistic system involves specialized operations such as inventory control, facility location and distribution planning, the logistic professional requires mathematical, technological and managerial skills and tools to design, adapt and improve these operations. The main research is focused on modeling and solving logistic problems through specialized tools such as integer programing and meta-heuristics methods. In practice, the use of these tools for large and complex problems requires mathematical and computational proficiency. In this context, the present work contributes with a coded suite of models to explore relevant problems by the logistic professional, undergraduate/postgraduate student and/or academic researcher. The functions of the coded suite address the following: (1) generation of test instances for routing and facility location problems with real geographical coordinates; (2) computation of Euclidean, Manhattan and geographical arc length distance metrics for routing and facility location problems; (3) simulation of non-deterministic inventory control models; (4) importing/exporting and plotting of input data and solutions for analysis and visualization by third-party platforms; and (5) designing of a nearest-neighbor meta-heuristic to provide very suitable solutions for large vehicle routing and facility location problems. This work is completed by a discussion of a case study which integrates the functions of the coded suite.

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Capacitated vehicle routing problem model for carriers

Cited by 11 publications

References 28 publications

Capacitated vehicle routing problem with column generation and reinforcement learning techniques

Capacitated vehicle routing problem with column generation and reinforcement learning techniques

Revenue Management Focused Capacitated Vehicle Routing Model in Supply Chain Management

Development of a coded suite of models to explore relevant problems in logistics

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