Location Modeling
Alireza Roshani; Mohammad Reza Gholamian; Mahsa Arabi
Abstract
Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting ...
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Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary.Methodology: In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.Originality/Value: This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.
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 ...
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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.
Location Modeling
Sepideh Taghikhani; Fahimeh Baroughi; Behrooz Alizadeh
Abstract
In this paper, the -product and t-state uncapacitated facility location problem is investigated. To be more precise, it is assume that each customer can request different products in a -state network. First, the mathematical formulation for the -product and t-state uncapacitated facility location ...
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In this paper, the -product and t-state uncapacitated facility location problem is investigated. To be more precise, it is assume that each customer can request different products in a -state network. First, the mathematical formulation for the -product and t-state uncapacitated facility location problem with certain costs is proposed. Also, it is shown that this paoblem is NP-hard. Since in most real-world problems the input of data are often ambiguous and uncertain, we study the -product and -state uncapacitated facility location problem in which the facility set-up costs and customer service costs are fuzzy random variables. Using three criteria, probability-possibility, probability-necessity and probability-credibility, the -product and -state uncapacitated facility location problem is formulated as a quadratic programming. Finally, a practical example is given to illustrate the efficiency of the proposed approaches.
Location Modeling
Ali Naimi-Sadigh; Amir Emami; Marzieh Mozafari
Abstract
Total covering problem is one of the most commonly used issues of locating facilities. In this context, the goal of determining the P service center is to cover at least the cost of deploying all demand points. These issues have a wide range of nature and scope, each of which is optimized by taking into ...
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Total covering problem is one of the most commonly used issues of locating facilities. In this context, the goal of determining the P service center is to cover at least the cost of deploying all demand points. These issues have a wide range of nature and scope, each of which is optimized by taking into account certain conditions in order to find the answer. One of these conditions can be a situation in which, in addition to full coverage of demand, the dispersion of facilities is also considered. Facility dispersion means maximizing the distance between facilities with respect to existing limits. This research seeks to provide a suitable model considering the predictable limits of the real world and the use of an appropriate method for solving the cover-dispersion model. Accordingly, the full coverage of the solution space and the choice of the optimal location of the facility with maximum dispersion, taking into account the minimum number of facilities and the lowest cost of deployment, due to the limited capacity of facilities and the minimization of transportation costs are the goals of this research. Due to the NP-HARD nature of the coating and literature models, solving these models, an algorithm is designed based on the genetic method for solving the model. In order to improve the quality of the algorithm's parameters, the parameters of the algorithm are set by the Taguchi experimental design method. The results show that the algorithm is suitable for the model.
Location Modeling
Meisam Jafari Eskandari; Hamed Nozari; merdad mokhtari saghinsara
Abstract
In this research, a supply chain network has been designed to address social and corrupt situations. To evaluate the model, a small dimensional example was first designed and the model was solved with 3 decision methods (utility function, comprehensive criteria, and Goal programming). To compare the ...
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In this research, a supply chain network has been designed to address social and corrupt situations. To evaluate the model, a small dimensional example was first designed and the model was solved with 3 decision methods (utility function, comprehensive criteria, and Goal programming). To compare the results of target functions and the effective responses obtained from the two-objective model, we compared the efficiency response indicators (averages of the target functions, the number of efficient responses, the most exponential index, the gap index, the distance index from the ideal point and the computational time). The decision method is a comprehensive criterion for acquiring average indices of the first objective function, the distance indicator from the ideal point, and the computational time more efficient than other methods. The ideal planning method has also proved to be effective in obtaining average indices of the second objective function, the number of effective responses, the most exponential index, and the efficiency gap index. Finally, the utility function method has also been more efficient in obtaining the problem solving index in less time. Finally, for comparing and choosing the most efficient solving method from solvency solving methods from topsis, the method of the comprehensive method is the most efficient method among existing methods.
Location Modeling
Reza Hasan Zadeh; Shirin Alizade
Abstract
Crises are the inevitable realities of human life; which is an accident that occurs naturally or suddenly or increasingly by human and to address it, there is a need for urgent and fundamental measures.When the crisis occurs, pre-determined storage locations will play an important role in relief; therefore, ...
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Crises are the inevitable realities of human life; which is an accident that occurs naturally or suddenly or increasingly by human and to address it, there is a need for urgent and fundamental measures.When the crisis occurs, pre-determined storage locations will play an important role in relief; therefore, the selection of suitable places for warehouses is one of our main goals in this research. In this research, a bi-objective linear programing model with integer variables is developed. The proposed model attempt to minimize total cost along with maximizing the minimum weight of open shelter areas while deciding on the location of shelter areas, the assigned population points to each open shelter area and controls the utilization of open shelter areas. In order to solve proposed model, some of well-known multi-objective, exact methods includes a weighted sum method, LP-metric method, and goal programing approach are employed.Finally, the best open shelter areas with considering the minimum cost is obtained, which these results can be useful for crisis organizations.