Open AccessImproving vehicle routing decision for subsidized rice distribution using linear programming considering stochastic travel times
Author(s) -
Filscha Nurprihatin,
Yuri Delano Regent Montororing
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1811/1/012007
Subject(s) - vehicle routing problem , subsidy , linear programming , trips architecture , distribution (mathematics) , routing (electronic design automation) , travelling salesman problem , destinations , goal programming , computer science , operations research , transport engineering , business , mathematical optimization , economics , mathematics , engineering , tourism , geography , computer network , mathematical analysis , archaeology , market economy
As the highest regency with the absolute poverty rate in Indonesia, Bogor Regency must be able to handle the poverty program appropriately and ensure its effectiveness and efficiency. Subsidized rice is one of the government programs for poor households. This program is important because rice is a food commodity with the largest contribution to the food poverty line. The effectiveness of the distribution is very dependent on the accuracy of the target number of beneficiaries and the accuracy of the amount of rice received at the consumption points. Meanwhile, the distribution efficiency is measured from the distribution route by taking into account the travel time that is directly related to transportation costs. This study discusses the distribution from only 1 (one) warehouse to 57 destinations. This study contributes to the enrichment of a cluster first route second approach to solve the routing problem. This paper applies Ward’s method and K-Means sequentially which generates 4 (four) clusters. Linear programming under the Capacitated Vehicle Routing Problem model is utilized to generate the routing decisions. Clusters 1 and 3 must take 1 and 2 trips, respectively. Meanwhile, clusters 2 and 4 each take 3 trips. As a result, the proposed method ensures all trips consume the shortest time.