A new mathematical model of vehicle routing problem based on milk-run

Author(s):  
Kong Ji-li ◽  
Jia Guo-zhu ◽  
Gan Cui-ying
DYNA ◽  
2015 ◽  
Vol 82 (189) ◽  
pp. 199-206 ◽  
Author(s):  
Elsa Cristina Gonzalez-L. ◽  
Wilson Adarme-Jaimes ◽  
Javier Arturo Orjuela-Castro

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Jianhua Ma ◽  
Guohua Sun

The objective of vehicle routing problem is usually to minimize the total traveling distance or cost. But in practice, there are a lot of problems needed to minimize the fastest completion time. The milk-run vehicle routing problem (MRVRP) is widely used in milk-run distribution. The mutation ACO is given to solve MRVRP with fastest completion time in this paper. The milk-run VRP with fastest completion time is introduced first, and then the customer division method based on dynamic optimization and split algorithm is given to transform this problem into finding the optimal customer order. At last the mutation ACO is given and the numerical examples verify the effectiveness of the algorithm.


2020 ◽  
Vol 1 (1) ◽  
pp. 58-68
Author(s):  
Elnaz Asadifard ◽  
Maryam Adlifard ◽  
Mohammad Taghipour ◽  
Nader Shamami

Purpose:  The purpose of this study is the well-known Heterogeneous Fleet Vehicle Routing Problem (HVRP) is one of the developed problems of vehicle routing, which involves optimizing a set of routes for a fleet of vehicles with different costs and capacities. Methods: HVRP is usually modeled as a single objective that aims to minimize overall transportation costs (total fixed costs and costs commensurate with total distance). Results: These vehicles are located in the central depot and serve customers’ demands. Implications: In this case, the number of vehicles available (of any type) may be limited.


2020 ◽  
Vol 26 (4) ◽  
pp. 174-184
Author(s):  
Thi Diem Chau Le ◽  
Duy Duc Nguyen ◽  
Judit Oláh ◽  
Miklós Pakurár

AbstractThis study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.


Author(s):  
Rodrigo De Alvarenga Rosa ◽  
Henrique Fiorot Astoures ◽  
André Silva Rosa

Oil exploration in Brazil is mainly held by offshore platforms which require the supply of several products, including diesel to maintain its engines. One strategy to supply diesel to the platforms is to keep a vessel filled with diesel nearby the exploration basin. An empty boat leaves the port and goes directly to this vessel, then it is loaded with diesel. After that, it makes a trip to supply the platforms and when the boat is empty, it returns to the vessel to be reloaded with more diesel going to another trip. Based on this description, this paper proposes a mathematical model based on the Vehicle Routing Problem with Intermediate Replenishment Facilities (VRPIRF) to solve the problem. The purpose of the model is to plan the routes for the boats to meet the diesel requests of the platform. Given the fact that in the literature, papers about the VRPIRF are scarce and papers about the VRPIRF applied to offshore platforms were not found in the published papers, this paper is important to contribute with the evolution of this class of problem, bringing also a solution for a real application that is very important for the oil and gas business. The mathematical model was tested using the CPLEX 12.6. In order to assess the mathematical model, tests were done with data from the major Brazilian oil and gas company and several strategies were tested.DOI: http://dx.doi.org/10.4995/CIT2016.2016.2217


2011 ◽  
Vol 4 (1) ◽  
pp. 17-29
Author(s):  
Hamed Fazlollaht ◽  
Iraj Mahdavi ◽  
Nezam Mahdavi-Am ◽  
Amir Mohajeri

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