A Real Case of Routing Design for Less-Than-Truckload Motor Carriers Using Genetic Algorithm

2006 ◽  
Author(s):  
Zhang Jian ◽  
Wu Yaohua ◽  
Wang Jingbo
Keyword(s):  
Genetic Algorithm ◽  
Real Case ◽  
Motor Carriers ◽  
Routing Design

scholarly journals Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

2018 ◽  
Vol 10 (11) ◽  
pp. 4120 ◽  
Author(s):  
Xiuqiao Sun ◽  
Jian Wang ◽  
Weitiao Wu ◽  
Wenjia Liu
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Decision Makers ◽  
Incident Response ◽  
Matrix Encoding ◽  
Routing Design ◽  
Speed Up ◽  
Fleet Allocation ◽  
Patrol Routing ◽  
Elitist Strategy

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

scholarly journals Coevolution of Second-order-mutant

2018 ◽  
Vol 8 (5) ◽  
pp. 3238
Author(s):  
Mohamad Syafri Tuloli ◽  
Benhard Sitohang ◽  
Bayu Hendradjaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Mutation Testing ◽  
Second Order ◽  
Real Case ◽  
Test Case ◽  
Test Cases ◽  
Single Population ◽  
Testing Problem

<span>One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.</span>

scholarly journals Waste reduction in Rectangular Figure Cutting using a Genetic Algorithm

2016 ◽  
Vol 10 (19) ◽  
pp. 19
Author(s):  
Juan C. Rodríguez Noriega ◽  
Jairo R. Coronado-Hernández ◽  
Sergio Leottau
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Waste Reduction ◽  
Cutting Process ◽  
Real Case ◽  
Object Based

This paper introduces a genetic algorithm (GA) to minimize the waste produced during the cutting process of rectangular figures on a sheet. The chromosomes for solution codification use an object-based representation. It has the following operator: Partially Mapped Crossover (PMX), mutation based in double interchange (2-opt), and the elitism strategy for the selection process. The proposed algorithm was applied in a real case situation problem, where the numbers of items were 55 pieces. The result of this implementation was a reduction of the waste as a result of the decrease in the number of sheets used in the cutting process and at the same time an effective employment of the used area. 

scholarly journals A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

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