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

2018 ◽  
Vol 23 (3) ◽  
pp. 34 ◽  
Author(s):  
Raknoi Akararungruangkul ◽  
Sasitorn Kaewman
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Evolution Algorithm ◽  
Special Case

scholarly journals A Sustainable Multimodal Transport System: The Two-Echelon Location-Routing Problem with Consolidation in the Euro–China Expressway

2019 ◽  
Vol 11 (19) ◽  
pp. 5486 ◽  
Author(s):  
Lu ◽  
Lang ◽  
Yu ◽  
Li
Keyword(s):  
Transport System ◽  
Emission Reduction ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Multimodal Transport ◽  
Random Instances ◽  
Evolution Algorithm

Sustainable development of transport systems is a common topic of concern and effort in multiple countries, in which reducing carbon emissions is one of the core goals. Multimodal transport is an effective way to achieve carbon emission reduction and to efficiently utilize transport resources. The intercontinental transport system, represented by the Euro–China Expressway, is a prominent exploration that has recently received attention, which promotes the sustainable development of transport between countries and carbon emission reduction. In the intercontinental multimodal transport system, the reasonable connection of roads and railways, especially the optimization of consolidation, is an important link which affects the system's carbon emissions. This paper focuses on the consolidation of sustainable multimodal transport and summarizes the multimodal transport two-echelon location-routing problem with consolidation (MT-2E-LRP-C). We aim to solve multimodal consolidation optimization problem, especially locations of multimodal station, by routing of highway and railway. We propose a two-layer mixed integer linear problem (MILP) model, which highlights the consolidation of roads and railways, focuses on road and rail transport connections, and optimizes road routes and railway schemes. To validate the MT-2E-LRP-C model, we design a series of random instances for different quantities of nodes. In order to solve large-scale instances and realistic transport problems, we propose a hybrid differential evolution algorithm, which decomposes the problem into a railway layer and a highway layer for heuristic algorithm solving. Furthermore, the MILP model and algorithm are tested by small-scale random instances, and the hybrid differential evolution algorithm is solved for the large-scale random instances. Finally, we solve the realist instance from the Euro–China Expressway to develop instructive conclusions.

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

2019 ◽  
Vol 1 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Peerawat Chokanat ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan
Keyword(s):  
Differential Evolution ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Differential Evolution Algorithm ◽  
Transportation Cost ◽  
Raw Milk ◽  
Routing Problem ◽  
Trial Vector ◽  
Evolution Algorithm ◽  
Special Case

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.

scholarly journals A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

2022 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Karn Moonsri ◽  
Kanchana Sethanan ◽  
Kongkidakhon Worasan
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Computational Time ◽  
Poultry Industry ◽  
Routing Problem ◽  
De Algorithm ◽  
Evolution Algorithm

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

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