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.
Optimization in science and engineering
Amir Parnianifard; Hamidreza Izadbakhsh
Abstract
Up to now, different tools have been introduced for analyses of process capability such as process capability indexes of Cp, Cpk and Cpm that developed for analyzing the capability of process and adapting the quality specifications of product. Most studies in the literature only consider the process ...
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Up to now, different tools have been introduced for analyses of process capability such as process capability indexes of Cp, Cpk and Cpm that developed for analyzing the capability of process and adapting the quality specifications of product. Most studies in the literature only consider the process with single capability variable, while there is a lack of studies that consider multi-variable processes. Cpm index has been defined with Taguchi overview over robust design. In this research, the metric Lp model has introduced to investigate the optimum decision variables by considering nominal is better quality specification and reparation Cpm index. We also expand the proposed model for such a processes with considering overall cost as well as process quality. At the end of research, numerical example has been presented to exhibit usage of proposed model for obtaining the best levels of process decision variables.
Optimization in science and engineering
Amir Parnianifard
Abstract
The quality loss function is conventional techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by uncontrollable external factors that cause output of process to be far from ideal points with ...
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The quality loss function is conventional techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by uncontrollable external factors that cause output of process to be far from ideal points with variability around its exact value. In this research, the common Taguchi quality loss function is applied to propose a new robust optimization model that able to choose optimal results of input variables. In this model, the quality loss function is expanded and a nonlinear optimization model is introduced in order to minimize the effect of environmental noise variables. In the end, a numerical example is presented to show the applicability of the proposed model for investigating the best levels of input variables in the noisy process.
meta-heuristic algorithms
Vida Karbasi bonab; Mahdi Yousefi Nejad Attari; Ensiyeh Neishabouri
Abstract
Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated ...
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Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated for a supply chain problem with multiple vendors and retailers, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. The vendor confronts two constraints: number of orders and available budget and minimizing the total inventory cost and optimizing the warehouse space are the two objectives of the model. Since the proposed model is formulated ino a bi-objective integer nonlinear programming (INLP) problem, an non-dominated Sorting genetic algorithm-II (NSGA-II) has been developed to find Pareto front solution. To improve the performance of algorithm has been calibrated using Taguchi method. Finally, conclusions are made and future research works are recommended.