Location Modeling
Mona Alizadeh Firozi; Vahid Kiani; Hossein Karimi
Abstract
Purpose: The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only ...
Read More
Purpose: The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only in a few runs, and in other runs they are caught in the local optimum.Methodology: The proposed method uses appropriate genetic operators to increase diversity of the population and performs local search around the best answer to exploit promising areas of the solution space. The use of hub mutation operators along with allocation mutation operators in the proposed algorithm has increased its exploration ability and effectiveness, which has led to discovery of the optimal answer in most runs for large size problems. Also, searching for the local neighborhood of the best answer made convergence faster and reduced the total running time for large instances.Findings: Evaluation of the proposed method and base algorithm on the Australian Post (AP) dataset showed that the improvements increased efficiency of the genetic algorithm in achieving optimal solutions for problems as large as 200 nodes from 2% to more than 85%.Originality/Value: This study showed that meta-heuristic algorithms and their improved versions are suitable methods for solving hub location problems in a short and limited time.
meta-heuristic algorithms
Hojatollah Rajabi Moshtaghi; Abbass Toloie-Eshlaghy; Mohammad Reza Motadel
Abstract
Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world ...
Read More
Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA). Methodology: Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.Findings: The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time". The results show the excellent performance of the proposed algorithm.Originality/Value: In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".
Decisions in new businesses
Elham Fazelli Veisari; mohamad javad Taghipourian; Reza Tavoli; Ghydar Ghanbarzade
Abstract
The purpose of this study is to identify the components and develop a model to provide rules for optimizing viral marketing in businesses. It is an applied research and in terms of method, it is mixed (quantitative and qualitative). The statistical population of the research in the qualitative part includes ...
Read More
The purpose of this study is to identify the components and develop a model to provide rules for optimizing viral marketing in businesses. It is an applied research and in terms of method, it is mixed (quantitative and qualitative). The statistical population of the research in the qualitative part includes 15 people in the three generations X, Y and Z (Millennium marketing generation) and in the quantitative part includes 460 online buyers. Data collection tools were used in the qualitative part of projection technique and in-depth interview. Interviews were analyzed and summarized using MAXQDA software, through which six components were identified, and then in a small part of 12 experts were used to determine the index of CVR, and then exploratory factor analysis was performed by SPSS software. Because selecting the most effective new components of viral marketing can have a huge impact on the accuracy of the viral marketing model in online businesses, To identify the most effective components, genetic metaheuristic algorithm was used, which is the software used in this section, WEKA and RAPIDMINER. Finally, the rules of viral marketing optimization were identified using the decision tree method. Findings in the qualitative section indicate that online persuasion, online trust, online support, online services, online attractiveness and online risk-taking are components of viral marketing. In the quantitative section and genetic algorithm, it was shown that the online risk component could not be used as an effective component for modeling and extracting viral marketing rules, so it was removed from the six components.