scholarly journals Analisa Optimasi Rute Transportasi Antar Jemput Siswa Menggunakan Model CGVRP dan Algoritma Dijkstra di SDIT Darus Sunnah

2019 ◽  
Vol 5 (2) ◽  
pp. 19-28
Author(s):  
Koko Hermanto ◽  
Tita Dwi Ermayanti
Keyword(s):  
Vehicle Routing Problem ◽  
Optimal Route ◽  
Dijkstra Algorithm ◽  
School Transportation ◽  
Routing Problem ◽  
The Real ◽  
The Difference ◽  
Transportation Routes ◽  
Generalized Vehicle Routing Problem

School transportation is one of the facilities provided by the school in the process of picking up students. In the process of picking up, taking the optimal route is needed to save costs and time. The purpose of this study is to develop a model School transportation routes using the Clustered Generalized Vehicle Routing Problem (CGVRP ) model and the Dijkstra algorithm to get a more optimal route from the route that has been used. Furthermore, comparing the real distance from the shuttle transportation routes with the distance from the results of this case study using the CGVRP model and the Dijkstra algorithm. From the research results obtained by using the CGVRP model and the Djikstra algorithm which is applied to the case study of shuttle students at Darus Sunnah SDIT We found that our result has shorter distance when compared to the distance of the usual route.. The total distance on the SDIT Darus Sunnah route is 2.746.416 meters and costs Rp. 2.214.288 in a month. While the distance from the proposed route obtained 2.333.616 meters for at Rp. 1.881.478 in a month. Therefore the difference in the distance from the real route and the proposed route is 357.288 meters with a difference in cost of Rp. 332.820, - in a month.

scholarly journals Contribution of simple heuristics for the vehicle routing problem – A case study of a small brewery

2013 ◽  
Vol 61 (7) ◽  
pp. 2393-2401 ◽  
Author(s):  
Petr Kučera ◽  
Igor Krejčí
Keyword(s):  
Vehicle Routing ◽  
Branch And Bound ◽  
Vehicle Routing Problem ◽  
Technical Equipment ◽  
Approximation Methods ◽  
Routing Problem ◽  
The Real ◽  
Basic Reason ◽  
Simple Heuristics

This paper presents a case study of a local brewery situated near to Prague. Even though its management already has the software which solves the vehicle routing problem by Mayer and Branch and Bound methods, it is still favourable to implement more approximation methods. The basic reason is that the Branch and Bound algorithm is complicated and software overflows may occur during its run in case of more cities in the cycle. The aim of this paper is to solve the real transportations using other methods that satisfy basic requirements of practicability. Transportation, which the brewery carried out during the selected week, provided data which were then analyzed using both the modifications of “classical” approximation methods (such as by Clark and Wright, Habr, and Mayer). Three types of the combination of these methods were also applied. The computed results were compared with the routes actually used by the brewery and with results from already implemented software. They showed that the brewery can save approximately 50,000 CZK (2,000 EUR) per year. Furthermore, the application of these methods needs neither any special technical equipment nor much time for the computation.

scholarly journals A New Approach to the Bi-objective Green Vehicle Routing Problem: Optimization in Newspaper Distribution

Exacta ◽  
2021 ◽  
Author(s):  
Júlio César Ferreira ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
New Approach ◽  
Three Stages ◽  
The Difference ◽  
Newspaper Distribution ◽  
Stage 1

The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and instances from the literature, was divided into three stages: Stage 1, data treatment; Stage 2, metaheuristic approaches (hybrid or non-hybrid), used comparatively, and, Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF1; minimization of CO2 emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF2; minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances.

scholarly journals Capacitated vehicle routing problem model for carriers

2018 ◽  
Vol 12 ◽  
Author(s):  
Irma-Delia Rojas-Cuevas ◽  
Santiago-Omar Caballero-Morales ◽  
Jose-Luis Martinez-Flores ◽  
Jose-Rafael Mendoza-Vazquez
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Network Flow ◽  
Route Planning ◽  
Test Problems ◽  
Optimal Route ◽  
Routing Problem ◽  
Problem Model ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP.Objective: To propose a model for carrier companies to obtain optimal route planning.Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where 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. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario.Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs.Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process.

scholarly journals Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

2018 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Malichan Thongkham ◽  
Sasitorn Kaewman
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Search Techniques ◽  
De Algorithm ◽  
Local Searches ◽  
Generalized Assignment ◽  
Linear Probability

This article presents algorithms for solving a special case of the vehicle routing problem (VRP). We define our proposed problem of a special VRP case as a combination of two hard problems: the generalized assignment and the vehicle routing problem. The different evolution (DE) algorithm is used to solve the problem. The recombination process of the original DE is modified by adding two more sets of vectors—best vector and random vector—and using two other sets—target vector and trial vector. The linear probability formula is proposed to potentially use one out of the four sets of vectors. This is called the modified DE (MDE) algorithm. Two local searches are integrated into the MDE algorithm: exchange and insert. These procedures create a DE and MDE that use (1) no local search techniques, (2) two local search techniques, (3) only the exchange procedure, and (4) only the insert procedure. This generates four DE algorithms and four MDE algorithms. The proposed methods are tested with 15 tested instances and one case study. The current procedure is compared with all proposed heuristics. The computational result shows that, in the case study, the best DE algorithm (DE-4) has a 1.6% better solution than that of the current practice, whereas the MDE algorithm is 8.2% better. The MDE algorithm that uses the same local search as the DE algorithms generates a maximum 5.814% better solution than that of the DE algorithms.

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