Penentuan Rute Dan Jumlah Kendaraan Pada Kasus School Bus Routing Problem: Penerapan Algoritma Record-to-record Travel

Ramadhan, Fadillah; Imran, Arif

doi:10.25124/jrsi.v5i01.307

Cited by 2 publications

(2 citation statements)

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“…(Wulandari et al, 2019) used a heuristic method to solve the VRP, namely the Palgunadi algorithm. (Ramadhan & Imran, 2018) solved VRP using a heuristic method, namely the record-to-record travel algorithm. (Ramadhan & Imran, 2018) applied the VRP to the case of school buses in the city of Bandung.…”

Section: Introductionmentioning

confidence: 99%

“…(Ramadhan & Imran, 2018) solved VRP using a heuristic method, namely the record-to-record travel algorithm. (Ramadhan & Imran, 2018) applied the VRP to the case of school buses in the city of Bandung. (Saragih & Rachman, 2020) solved VRP by formulating the problem as an ILP (integer linear programming) model and applied it to the rice distribution system in Bandung City.…”

Section: Introductionmentioning

confidence: 99%

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Design of Vehicle Routes for Rice Distribution System in Bandung Using Simulated Annealing Algorithm

Saragih

Turnip²

2022

JRSI

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VRP (vehicle routing problem) belongs to the problem of NP-hard. It makes the computation time becoming longer as the number of data increases. This statement is in accordance with previous research which solved the VRP for the rice distribution system in Bandung which consisted of 40 demand points in the form of traditional markets using the optimal method. The computational time required is 167 hours 52 minutes 38 seconds. Therefore, a heuristic method is needed to solve VRP with a more efficient computation time and an acceptable solution. The purpose of this paper is to complete the VRP by using an annealing simulation to design a rice distribution system in Bandung. Simulated annealing (SA) is a local search algorithm (meta-heuristic) that is able to get out of the local optimum. Its ease of implementation, the use of hill-climbing motions to avoid local optimal, and the convergent nature, have made SA a popular technique over the past two decades. After comparing the results to the ILP model, the computational experiments show what the SA algorithm developed in this paper is able to produce a relatively small gap in terms of total transportation cost, which is 1.26%. This paper has also succeeded in improving the previous paper by reducing the computation time to 19 seconds using the developed SA algorithm.

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Section: Introductionmentioning

confidence: 99%