Modeling the Municipal Waste Collection Using Genetic Algorithms

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

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CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING PROBLEM?

FLORESTA ◽

10.5380/rf.v51i3.72612 ◽

2021 ◽

Vol 51 (3) ◽

pp. 751

Author(s):

Carlos Alberto Araújo Júnior ◽

Renato Vinícius Oliveira Castro ◽

João Batista Mendes ◽

Helio Garcia Leite

Keyword(s):

Genetic Algorithm ◽

Linear Programming ◽

Variable Neighborhood Search ◽

Clonal Selection ◽

Neighborhood Search ◽

Initial Population ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Forest Production ◽

Relaxed Problem

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

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An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2018.2881686 ◽

2019 ◽

Vol 49 (10) ◽

pp. 1933-1945 ◽

Cited By ~ 15

Author(s):

Xinyu Li ◽

Liang Gao ◽

Quanke Pan ◽

Liang Wan ◽

Kuo-Ming Chao

Keyword(s):

Genetic Algorithm ◽

Process Planning ◽

Variable Neighborhood Search ◽

Hybrid Genetic Algorithm ◽

Neighborhood Search ◽

Integrated Process ◽

Planning And Scheduling ◽

Packaging Machine

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Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

10.5121/csit.2020.101701 ◽

2020 ◽

Author(s):

Jiawei LI ◽

Tad Gonsalves

Keyword(s):

Genetic Algorithm ◽

Real Data ◽

Initial Population ◽

Exam Timetabling ◽

Timetabling Problem ◽

Training Approach ◽

Chromosome Representation ◽

Genetic Algorithm Approach ◽

Hard And Soft Constraints ◽

Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

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Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

Yugoslav journal of operations research ◽

10.2298/yjor200115014k ◽

2020 ◽

pp. 14-14

Author(s):

Manel Kammoun ◽

Houda Derbel ◽

Bassem Jarboui

Keyword(s):

Genetic Algorithm ◽

Local Search ◽

Variable Neighborhood Search ◽

Search Algorithm ◽

Descent Method ◽

Computational Experiments ◽

Neighborhood Search ◽

Variable Neighborhood Descent ◽

Covering Tour Problem ◽

Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

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