scholarly journals Analyzing the Performance of a Hybrid Heuristic for Solving a Bilevel Location Problem under Different Approaches to Tackle the Lower Level

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sayuri Maldonado-Pinto ◽  
Martha-Selene Casas-Ramírez ◽  
José-Fernando Camacho-Vallejo
Keyword(s):  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Location Problem ◽  
Facility Location Problem ◽  
Computational Time ◽  
Benchmark Instances ◽  
Crossover Phase ◽  
Bilevel Problems ◽  
Feasible Solutions ◽  
The Impact

The problem addressed here is a combinatorial bilevel programming problem called the uncapacitated facility location problem with customer’s preferences. A hybrid algorithm is developed for solving a battery of benchmark instances. The algorithm hybridizes an evolutionary algorithm with path relinking; the latter procedure is added into the crossover phase for exploring the trajectory between both parents. The proposed algorithm outperforms the evolutionary algorithm already existing in the literature. Results show that including a more sophisticated procedure for improving the population through the generations accelerates the convergence of the algorithm. In order to support the latter statement, a reduction of around the half of the computational time is obtained by using the hybrid algorithm. Moreover, due to the nature of bilevel problems, if feasible solutions are desired, then the lower level must be solved for each change in the upper level’s current solution. A study for illustrating the impact in the algorithm’s performance when solving the lower level through three different exact or heuristic approaches is made.

scholarly journals KGPMH: A hybrid approach for solving a novel facility location problem

2021 ◽  
Author(s):  
Maryam DehghanChenary ◽  
Arman Ferdowsi ◽  
Fariborz Jolai ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Evaluation Function ◽  
Genetic Approach ◽  
Hub Location ◽  
Hub Location Problem ◽  
Proposed Model ◽  
The Impact

<pre>The focus of this paper is to propose a bi-objective mathematical model for a new extension of a multi-period p-mobile hub location problem and then to devise an algorithm for solving it. The developed model considers the impact of the time spent traveling at the hubs' network, the time spent at hubs for processing the flows, and the delay caused by congestion at hubs with specific capacities. Following the unveiled model, a hybrid meta-heuristic algorithm will be devised that simultaneously takes advantage of a novel evaluation function, a clustering technique, and a genetic approach for solving the proposed model.</pre>

An Evolutionary Algorithm for Graph Drawing with a Multiobjective Approach

2012 ◽  
pp. 113-140 ◽  
Author(s):  
Sergio Enríquez Aranda ◽  
Eunice E. Ponce de León Sentí ◽  
Elva Díaz Díaz ◽  
Alejandro Padilla Díaz ◽  
María Dolores Torres Soto ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Quality Index ◽  
Pareto Front ◽  
Graph Drawing ◽  
Search Algorithm ◽  
Hill Climbing ◽  
Edge Crossing ◽  
Multiobjective Approach ◽  
Feasible Solutions

In this chapter a hybrid algorithm is constructed, implemented and tested for the optimization of graph drawing employing a multiobjective approach. The multiobjective optimization problem for graph drawing consists of three objective functions: minimizing the number of edge crossing, minimizing the graph area, and minimizing the aspect ratio. The population of feasible solutions is generated using a hybrid algorithm and at each step a Pareto front is calculated. This hybrid algorithm combines a global search algorithm (EDA — Estimation of Distribution Algorithm) with a local search Algorithm (HC — Hill Climbing) in order to maintain a balance between the exploration and exploitation. Experiments were performed employing planar and non-planar graphs. A quality index of the obtained solutions by the hybrid MOEA-HCEDA (Multiobjective Evolutionary Algorithm - Hill Climbing & Univariate Marginal Distribution Algorithm) is constructed based on the Pareto front defined in this chapter. A factorial experiment using the algorithm parameters was performed. The factors are number of generations and population size, and the result is the quality index. The best combination of factors levels is obtained.

scholarly journals Optimization of the Weighted Multi-Facility Location Problem Using MS Excel

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 191
Author(s):  
Petr Němec ◽  
Petr Stodola ◽  
Miroslav Pecina ◽  
Jiří Neubauer ◽  
Martin Blaha
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Dimensional Space ◽  
Continuous Optimization ◽  
Facility Location Problem ◽  
Benchmark Instances ◽  
Hardware Configuration ◽  
Complex Optimization ◽  
Excel Solver ◽  
Office Software

This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.

scholarly journals Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Carolina Lagos ◽  
Broderick Crawford ◽  
Enrique Cabrera ◽  
Ricardo Soto ◽  
José-Miguel Rubio ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Evolutionary Strategies ◽  
Cultural Algorithm ◽  
Historical Knowledge ◽  
Uncapacitated Facility Location Problem ◽  
Normative Knowledge ◽  
The Impact

Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

scholarly journals KGPMH: A hybrid approach for solving a novel facility location problem

2021 ◽  
Author(s):  
Maryam DehghanChenary ◽  
Arman Ferdowsi ◽  
Fariborz Jolai ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Evaluation Function ◽  
Genetic Approach ◽  
Hub Location ◽  
Hub Location Problem ◽  
Proposed Model ◽  
The Impact

<pre>The focus of this paper is to propose a bi-objective mathematical model for a new extension of a multi-period p-mobile hub location problem and then to devise an algorithm for solving it. The developed model considers the impact of the time spent traveling at the hubs' network, the time spent at hubs for processing the flows, and the delay caused by congestion at hubs with specific capacities. Following the unveiled model, a hybrid meta-heuristic algorithm will be devised that simultaneously takes advantage of a novel evaluation function, a clustering technique, and a genetic approach for solving the proposed model.</pre>

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