Solving Multi-criteria Vehicle Routing Problem by Parallel Tabu Search on GPU

Jagiełło, Szymon; Żelazny, Dominik

doi:10.1016/j.procs.2013.05.434

Cited by 23 publications

(2 citation statements)

References 8 publications

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“…One of the core of several trajectory search based Metaheuristics is the use of Neighborhood Structures [56] and, consequently, local search mechanisms [57], which have the potential of proving optimality in some specific cases. For this purpose, efficient high-performance techniques have been suggested for tackling this problems, such as Graphics Processing Units [58,59].…”

Section: The Role Of Metaheuristic and High-performance Optimizationmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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Section: The Role Of Metaheuristic and High-performance Optimizationmentioning

confidence: 99%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…Metode VRP sendiri merupakan pengembangan dari Metode Travelling Salesman Probelm (TSP) dimana pengiriman dilakukan oleh 1 kendaraan saja [3]. Penerapan yang dilakukan dalam menggunakan metode VRP sangat beragam bergantung dari tujuannya.…”

Section: Metode Penelitianunclassified

Optimasi Rute Pengiriman dengan Heterogeneous Fleet Vehicle Routing Problem with Time Windows

Hendrawan

Widyadana

2018

j. sist. manaj. ind.

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PT. X merupakan distributor suku cadang dengan 128 pelanggan untuk Kota Surabaya dan Kota Sidoarjo. Jumlah kendaraan pengirim yang dimiliki oleh PT. X adalah 15. Penentuan rute pengiriman yang dimiliki oleh PT. X dilakukan secara manual, berdasarkan pengalaman pengemudi. Penelitian ini bertujuan membuat sebuah model penentuan rute dengan biaya minimum dengan mempertimbangkan waktu tempuh dan kapasitas kendaraan. A B S T R A C T Keywords:Evolutionary Algorithm HFVRP with Time Windows Rute Vehicle Routing Problem PT. X is a distributor of spare parts with 128 customers for Surabaya City and Sidoarjo City. Number of delivery vehicles owned by PT. X is 15. Determination of delivery route owned by PT. X is completed manually based on driver experience. This research is to make a model of determining the route with minimum cost by considering the travel time and the capacity of the vehicle. Model formation is accomplished in accordance with the PT X policy that each colleague can only be served by one vehicle and the maximum shipping time of 7 hours. The model formed is Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPWTW). The settlement method used is Evolutionary Algorithm (EA) by reviewing 3 cases. The number of vehicles decreased by the model is with an average difference of 1.67 vehicles. The shipping cost generated by the model is lower than the company for case 1 of 3,155 IDR and case 2 of 25,005 IDR. This condition indicates the model will use less number of vehicles with low shipping costs and large container utilities in 2 cases. *Corresponding AuthorEdwin Hendrawan E-mail: hendrawanedwin68@gmail.com © 2018 Penerbit UNSERA. All rights reserved PENDAHULUAN PT. X merupakan sebuah perusahaan yang bergerak dalam bidang jasa yaitu distributor suku cadang sepeda motor X. Kegiatan distribusi dilakukan dengan mengirimkan suku cadang kepada pelanggan yang sudah memesan sesuai dengan jadwal yang telah disepakati. Jumlah pelanggan yang dilayani oleh PT. X di Kota Surabaya dan Kota Sidoarjo sebanyak 128 pelanggan. Pengiriman suku cadang yang dilakukan PT. X menggunakan kendaraan operasional perusahaan yang berjumlah 15 kendaraan.

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Parallel Processing Algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future

Rabbouch

Saâdaoui

2020

Algorithms and Architectures for Parallel Processing

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Solving Multi-criteria Vehicle Routing Problem by Parallel Tabu Search on GPU

Cited by 23 publications

References 8 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Optimasi Rute Pengiriman dengan Heterogeneous Fleet Vehicle Routing Problem with Time Windows

Parallel Processing Algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future

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