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

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

AgriEngineering ◽

10.3390/agriengineering1010006 ◽

2019 ◽

Vol 1 (1) ◽

pp. 75-93 ◽

Cited By ~ 2

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.

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Stratified Sampling Differential Evolution Algorithm for Global Optimization Problem

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.1491 ◽

2012 ◽

Vol 452-453 ◽

pp. 1491-1495

Author(s):

Shu Hua Wen ◽

Qing Bo Lu ◽

Xue Liang Zhang

Keyword(s):

Differential Evolution ◽

Optimization Problem ◽

Sampling Method ◽

Differential Evolution Algorithm ◽

Optimal Solution ◽

Stratified Sampling ◽

Local Optima ◽

De Algorithm ◽

Local Optimal Solution ◽

Evolution Algorithm

Differential Evolution (DE) is one kind of evolution algorithm, which based on difference of individuals. DE has exhibited good performance on optimization problem. However, when a local optimal solution is reached with classical Differential Evolution, all individuals in the population gather around it, and escaping from these local optima becomes difficult. To avoid premature convergence of DE, we present in this paper a novel variant of DE algorithm, called SSDE, which uses the stratified sampling method to replace the random sampling method. The proposed SSDE algorithm is compared with some variant DE. The numerical results show that our approach is robust, competitive and fast.

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An Extended Flexible Job Shop Scheduling Model for Flight Deck Scheduling with Priority, Parallel Operations, and Sequence Flexibility

Scientific Programming ◽

10.1155/2017/2463252 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Lianfei Yu ◽

Cheng Zhu ◽

Jianmai Shi ◽

Weiming Zhang

Keyword(s):

Differential Evolution ◽

Job Shop ◽

Job Shop Scheduling ◽

Time Budget ◽

Differential Evolution Algorithm ◽

Mixed Integer ◽

Aircraft Carrier ◽

Evolution Algorithm ◽

Improved Differential Evolution Algorithm ◽

Flight Deck

Efficient scheduling for the supporting operations of aircrafts in flight deck is critical to the aircraft carrier, and even several seconds’ improvement may lead to totally converse outcome of a battle. In the paper, we ameliorate the supporting operations of carrier-based aircrafts and investigate three simultaneous operation relationships during the supporting process, including precedence constraints, parallel operations, and sequence flexibility. Furthermore, multifunctional aircrafts have to take off synergistically and participate in a combat cooperatively. However, their takeoff order must be restrictively prioritized during the scheduling period accorded by certain operational regulations. To efficiently prioritize the takeoff order while minimizing the total time budget on the whole takeoff duration, we propose a novel mixed integer liner programming formulation (MILP) for the flight deck scheduling problem. Motivated by the hardness of MILP, we design an improved differential evolution algorithm combined with typical local search strategies to improve computational efficiency. We numerically compare the performance of our algorithm with the classical genetic algorithm and normal differential evolution algorithm and the results show that our algorithm obtains better scheduling schemes that can meet both the operational relations and the takeoff priority requirements.

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Research and Improvement on K-Means Clustering Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3231 ◽

2013 ◽

Vol 756-759 ◽

pp. 3231-3235

Author(s):

Xue Mei Wang ◽

Jin Bo Wang

Keyword(s):

Differential Evolution ◽

Clustering Algorithm ◽

Differential Evolution Algorithm ◽

Optimal Solution ◽

Clustering Quality ◽

Local Optimal Solution ◽

Evolution Algorithm ◽

Clustering Center ◽

Searching Ability ◽

Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

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