A Hybrid DE Algorithm with a Multiple Strategy for Solving the Terminal Assignment Problem

2010 ◽  
pp. 303-308 ◽  
Author(s):  
Eugénia Moreira Bernardino ◽  
Anabela Moreira Bernardino ◽  
Juan Manuel Sánchez-Pérez ◽  
Juan Antonio Gómez-Pulido ◽  
Miguel Angel Vega-Rodríguez
Keyword(s):  
Assignment Problem ◽  
De Algorithm ◽  
Multiple Strategy

scholarly journals Differential Evolution Algorithm for Multilevel Assignment Problem: A Case Study in Chicken Transportation

2018 ◽  
Vol 23 (4) ◽  
pp. 55 ◽  
Author(s):  
Sasitorn Kaewman ◽  
Tassin Srivarapongse ◽  
Chalermchat Theeraviriya ◽  
Ganokgarn Jirasirilerd
Keyword(s):  
Differential Evolution ◽  
Assignment Problem ◽  
Differential Evolution Algorithm ◽  
Computation Time ◽  
Search Performance ◽  
De Algorithm ◽  
Minimum Number ◽  
Average Computation Time ◽  
Two Stages

This study aims to solve the real-world multistage assignment problem. The proposed problem is composed of two stages of assignment: (1) different types of trucks are assigned to chicken farms to transport young chickens to egg farms, and (2) chicken farms are assigned to egg farms. Assigning different trucks to the egg farms and different egg farms to the chicken farms generates different costs and consumes different resources. The distance and the idle space in the truck have to be minimized, while constraints such as the minimum number of chickens needed for all egg farms and the longest time that chickens can be in the truck remain. This makes the problem a special case of the multistage assignment (S-MSA) problem. A mathematical model representing the problem was developed and solved to optimality using Lingo v.11 optimization software. Lingo v.11 can solve to optimality only small- and medium-sized test instances. To solve large-sized test instances, the differential evolution (DE) algorithm was designed. An excellent decoding method was developed to increase the search performance of DE. The proposed algorithm was tested with three randomly generated datasets (small, medium, and large test instances) and one real case study. Each dataset is composed of 12 problems, therefore we tested with 37 instances, including the case study. The results show that for small- and medium-sized test instances, DE has 0.03% and 0.05% higher cost than Lingo v.11. For large test instances, DE has 3.52% lower cost than Lingo v.11. Lingo v.11 uses an average computation time of 5.8, 103, and 4320 s for small, medium and large test instances, while DE uses 0.86, 1.68, and 8.79 s, which is, at most, 491 times less than Lingo v.11. Therefore, the proposed heuristics are an effective algorithm that can find a good solution while using less computation time.

COMPARATIVE ANALYSIS OF A HYBRID DE ALGORITHM WITH THE VNS ALGORITHM AND ITS VARIATION SVNS TO SOLVE A REAL-WORLD FREQUENCY ASSIGNMENT PROBLEM

2011 ◽  
Vol 25 (3) ◽  
pp. 217-234 ◽  
Author(s):  
Marisa da Silva Maximiano ◽  
Miguel A. Vega-Rodríguez ◽  
Juan A. Gómez-Pulido ◽  
Juan M. Sánchez-Pérez
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Assignment Problem ◽  
Frequency Assignment ◽  
Frequency Assignment Problem ◽  
De Algorithm

A NEW APPROACH TO VARIANT ASSIGNMENT PROBLEM

2018 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
BALAKRISHNA U. ◽  
Keyword(s):  
Assignment Problem ◽  
New Approach

Algorithmic solution for nurse assignment problem based on GA with perturb mutation

2013 ◽  
Vol 32 (12) ◽  
pp. 3548-3552
Author(s):  
Lian-min HU ◽  
Xu-dong HONG ◽  
Han HUANG
Keyword(s):  
Assignment Problem ◽  
Algorithmic Solution ◽  
Nurse Assignment

A MODERN APPROACH FOR SOLVING INTERVAL BASED ASSIGNMENT PROBLEM

2020 ◽  
Vol 9 (5) ◽  
pp. 2541-2545
Author(s):  
M. Radha ◽  
S. Ananthalakshmi ◽  
R. U. Parameswari
Keyword(s):  
Assignment Problem ◽  
Modern Approach

A Parallel Shortest Augmenting Path Algorithm for the Assignment Problem

1989 ◽  
Author(s):  
Egon Balas ◽  
Donald Miller ◽  
Joseph Pekny ◽  
Paolo Toth
Keyword(s):  
Assignment Problem ◽  
Augmenting Path
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