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

Abstract

This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks' and tanks' cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

Modified Differential Evolution Algorithm for U-Shaped Assembly Line Balancing Type 2

“…The DE algorithm was first described by Storn and Price [23] in 1997. It has been used to solve a wide variety of problems and objectives in, for example, assignment problem to minimize cost [22], transportation problem to maximize profit and minimize cost of transport [24][25][26], location routing problem to minimize the fuel usage [27], simple assembly line balancing problem-type 1 (SALBP-1) to minimize number of workstation [28,29] and Ushaped assembly line balancing problem-type 1 (UALBP-1) to minimize number of workstation [13]. Ramadas, Abraham and Kumar [30] proposed a new revised mutation strategy in DE algorithm to improve the optimal solution.…”

Novel Route Planning System for Machinery Selection. Case: Slurry Application

“…Therefore, this challenge has led to the need to develop algorithms that can produce near-optimal solutions in a reasonable amount of time [12]. During the years, different solution approaches have been developed including exact algorithms (such as branch-and-bound [13,14] and branch-and-cut [15]), heuristic algorithms (such as the Clarke-Wright savings algorithm [16]), and metaheuristic algorithms (such as simulated annealing [17,18], genetic algorithms [19], tabu search [20], and ant algorithms [21]. Earlier, conventional heuristic algorithms were designed as a response to limited computer processing power.…”

A novel two-phase approach for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles: a case study on fuel delivery

“…There are a lot of real-world problems which are very hard to tackle using exact methods. The Vehicle Routing Problem (VRP), which is famous as an NP-hard problem, is a well-known problem in operations research and combinatorial optimization (Chokanat et al, 2019;Wichapa & Khokhajaikiat, 2018). Much attention of researchers has been devoted on the development of the characteristics of the problem and assumptions, leading to an enormous number of VRPs and variants, as well as various heuristic/metaheuristic modifications to tackle the problem (Hanum et al, 2019).…”