Modified Differential Evolution Algorithm Solving the Special Case of Location Routing Problem

Abstract

This research article aims to solve the special case of the location routing problem (SLRP) when the objective function is the fuel consumption. The fuel consumption depends on the distance of travel and the condition of the road. The condition of the road causes the vehicle to use a different speed, which affects fuel usage. This turns the original LRP into a more difficult problem. Moreover, the volume of the goods that are produced in each node could be more or less than the capacity of the vehicle, and as the case study requires the transportation of latex, which is a sensitive good and needs to be carried within a reasonable time so that it does not form solid before being used in the latex process, the maximum time that the latex can be in the truck is limited. All of these attributes are added into the LRP and make it a special case of LRP: a so-called SLRP (a special case of location routing problem). The differential evolution algorithms (DE) are proposed to solve the SLRP. We modified two points in the original DE, which are that (1) the mutation formula is introduced and (2) the new rule of a local search is presented. We call this the modified differential evolution algorithm (MDE). From the computational result, we can see that MDE generates a 13.82% better solution than that of the original version of DE in solving the test instances.

Location Decision Making and Transportation Route Planning Considering Fuel Consumption

“…The objective was to minimize the total cost. According to fuel consumption cost, we categorized the roads into five types, which were classified by using the average driving speed and the fuel consumption rate that was adopted from Akararungruangkul and Kaewman [32]. Table 1 show the road type and fuel consumption rate.…”

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

“…This article added these two steps to increase the performance of the original DE. The DE can be improved by adding more steps into the original steps and other techniques such as use various mutation and recombination formula [21,22,28], add effective local search techniques [27], add the best vector reproduction process [29], develop new decoding method which is combined with ant colony optimization [30]. Therefore, in this article, the DE has been improved in following ways so that the effectiveness of DE is increased: (1) provided an effective decoding method; (2) presented new rules of the selection process to let DE escape from the local optimal; and (3) used 3 different local searches so that the DE has intensive search efficiency.…”

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

“…Braekers et al (2015) provided an overview of the scenario and the problem's physical characteristics, extending the basic incapacitated VRP, which was most often considered in the reviewed articles. The special case of the VRP problem was proposed by Kaewman and Akararungruangkul (2018) and Akararungruangkul and Kaewman (2018), where one pickup point can be visited more than once and the road condition is considered as the objective function.…”

“…Dechampai et al (2015) proposed a DE to solve the capacitated VRP with the flexibility of mixing pickup and delivery services and maximum duration of a route in the poultry industry. Akararungruangkul and Kaewman (2018) proposed a DE to solve the special case of VRP in a student pickup system. They also modify the mutation process by using a different formulae to increase the search capacity of DE.…”