Optimization in science and engineering
zohre kiapasha; ali salmasnia
Abstract
Purpose: Cloud manufacturing is a service-oriented production model that centralizes production resources available in different geographical locations to respond to specific customer needs. One of the main issues in cloud manufacturing systems is the proper allocation of sub-tasks to enterprises and ...
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Purpose: Cloud manufacturing is a service-oriented production model that centralizes production resources available in different geographical locations to respond to specific customer needs. One of the main issues in cloud manufacturing systems is the proper allocation of sub-tasks to enterprises and their optimal scheduling. Most studies in the literature assume that all tasks have only one type of structure, although it is possible to have tasks with different structures in one order set. Furthermore, existing scheduling models in the cloud manufacturing literature tend to assume that all tasks are available at time zero and that the logistics time/cost among enterprises is negligible. Therefore, in this study, an optimization model with three objective functions of task completion time, the cost imposed on the cloud manufacturing system, and the quality of the selected services is developed in which to get closer to the real world, three features are included in it: 1) the possibility of tasks with two structures, series and parallel, 2) different arrival times of tasks in the cloud manufacturing system, and 3) time/cost of logistics between different enterprises.
Methodology: First, six examples with different numbers of tasks and subtasks are designed with both sequential and parallel structures. In order to accurately solve the proposed model and achieve the global optimum, the CPLEX solver is used in the GAMS software.
Findings: In order to verify the importance of the characteristics of the developed model, two comparative studies are carried out. In the first comparative study, the presented model is compared with a similar model in which it is assumed that all tasks are available at time zero. The second comparative study examines the effect of considering logistics time/costs between enterprises when allocating subtasks to services. The results of the comparative studies show the misleading level of the cloud manufacturing manager in the face of the reduced models.
Originality/Value: The output of this research is to present a model for the simultaneous scheduling of tasks with sequential and parallel structures, taking into account the different task arrival times and logistics in the cloud manufacturing system.
Optimization in science and engineering
Ali Sheykhani; Farshad Hosseinzadeh Lotfi; Arash Maghsoudi
Abstract
Worldwide, the rate of preterm births is increasing, so there will be significant health, development and economic problems. Premature birth is one of the leading causes of death and a significant cause for the loss of human potential among survivors around the world. Complications of preterm birth are ...
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Worldwide, the rate of preterm births is increasing, so there will be significant health, development and economic problems. Premature birth is one of the leading causes of death and a significant cause for the loss of human potential among survivors around the world. Complications of preterm birth are the single largest direct cause of neonatal death. Current methods for early detection of such labor are insufficient. One promising technique, recognized in monitoring uterine activity, is the use of advanced device learning algorithms and electrohistrography (EHG) induction. In this article, a learning machine is designed to diagnose different types of deliveries. Using deep learning algorithms, electrohistrographic signals have been used to detect preterm birth. The results were obtained using a data set that included 262 cases for women who had a preterm delivery and 38 cases for women who had a preterm delivery. Using the "cross" technique, 4 types of data sets were implemented in two ways, with training and without training. The results obtained in this study showed that the error on this set of data was one percent.
Optimization in science and engineering
Elham Nejati; Mahdi Yousefi Nejad Attari; Asgar Hajibadali
Abstract
Purpose: One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the ...
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Purpose: One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the organ transplant supply chain and other supply chains is the possibility of corruption of related products. Therefore, the time and also the location of organ transplant centers are of special importance. On the other hand, due to the rapid growth of the demand for organ transplantation and the lack of resources, the patient's waiting time to complete the transplantation process plays a vital role in the organ transplantation system.Methodology: This study presents a robust bi-objective mathematical model for the location problem of allocating organ transplant centers under uncertainty, which includes the total costs of the organ transplant system as well as the average patient waiting time for organ transplantation, which follows a G/G/m queuing system.Findings: To solve this model, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been used. Finally, the applicability of this model and the efficiency of the mentioned algorithm compared to the defined indicators have been shown through numerical experiments.Originality/Value: Since each organ can spend a certain amount of time outside the body and there is a possibility of corruption or a decrease in the quality of the transplant, the time between the organ leaving the body and the completion of the transplant operation plays an essential role in the transplant system.
Optimization in science and engineering
Zeynab Rashidi; Zahra Rashidi
Abstract
Purpose: The problem of allocating space to academic needs is one of the complex optimization issues that distributes a limited set of educational and research needs to a set of resources with a set of constraints. Due to the complexity of this problem, several techniques based on innovative methods ...
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Purpose: The problem of allocating space to academic needs is one of the complex optimization issues that distributes a limited set of educational and research needs to a set of resources with a set of constraints. Due to the complexity of this problem, several techniques based on innovative methods have been proposed. In this paper, a mathematical model of integer programming is presented to formulate this problem.Methodology: To solve the model, the gradient descent method is used and its parameters are adjusted. To evaluate the proposed model and solution, the data and facilities of one of the fledgling faculties at Allameh Tabatabai University in Tehran are tested. There are 11 requirements and 18 allocable spaces in this faculty and therefore there are 198 binary decision variables, in the model. In experiments, several scenarios are created and the results of each scenario are compared.Findings: The proposed model and solution is a general method and can be used for other faculties and universities that face space constraints.Originality/Value: In this article, a mathematical model was presented to formulate the problem of allocating space, which is one of the important decision-making issues for organizations and research educational institutions.
Optimization in science and engineering
Nazila Nikdel
Abstract
Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing ...
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Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing appropriate methods to ensure the successful performance of industrial operations. Therefore, this article pursues two main objectives: 1) controlling the robotic system by presenting a method based on fractional-order calculus so that it can control the system despite its complexity and non-linearity, 2) presenting the meta-heuristic algorithm "Improved Grey Wolf" to optimize the system response.Methodology: First, the mathematical model of the robot is presented based on Lagrange rules, and then the fractional-order calculus is used to design the controller. In addition, the efficiency of the grey wolf algorithm is increased with the introduction of an improved method.Findings: Different cost functions based on the main performance criteria of the robotic system are introduced, and an improved algorithm is applied to them. The comparison results of the proposed algorithm and other algorithms, indicate its satisfying performance. In addition, the efficiency of the fractional-order controller is compared with its integer-order counterpart, and the results show a significant improvement in system performance.Originality/Value: The proposed controller can control the system well despite its complexity and non-linearity. In addition, inspired by the Grey Wolf algorithm, an improved optimization method is proposed that can increase the efficiency of the controlled system. Numerical results show the satisfying performances of the proposed controller and the improved optimization algorithm.
Optimization in science and engineering
Nazila Adabavazeh; Mehrdad Nikbakht; Reza Tavakkoli-Moghaddam
Abstract
Purpose: Extent of an application and importance of a welding industry and economic opportunities of this industry need to develop appropriate strategies to pave the way for the economic growth as a strategic industry with sustainable competitive advantage. The high cost of the gas tungsten arc welding ...
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Purpose: Extent of an application and importance of a welding industry and economic opportunities of this industry need to develop appropriate strategies to pave the way for the economic growth as a strategic industry with sustainable competitive advantage. The high cost of the gas tungsten arc welding (GTAW) process in a welding industry due to the advantages of excellent welding quality and low complexity of this process requires management. Therefore, careful study and evaluation in the correct use of the GTAW process with financial resources seems to be very necessary. Modeling leads to efficient decision making along with competitive advantage in strategic planning. Today, by planning, sustainability goals and considerations can also be achieved in addition to achieving economic goals.Methodology: In this study, a linear programming model for the problem of minimizing the cost of the GTAW process according to different automatic or manual conditions is presented by a skilled welding operator. GAMS software is used to solve the problem and validate the proposed model. Finally, to evaluate the applicability of the model, four scenarios of the case study are solved and explained as well as sensitivity analysis.Findings: The results show that from an economic point of view, the proposed model can reduce costs and increase efficiency and customer satisfaction. The proposed approach leads to the improvement of the shielded tungsten arc welding process and the increase of management insight.Originality/Value: Mathematical cost modeling can provide a comprehensive analysis of management decisions. The cost minimization model helps managers to understand the cost structure and behavior. Considering the scope of welding application, minimizing the costs of this activity will lead to a reduction in the total cost of an industry
Optimization in science and engineering
Leila Hasan-Beigi Dashtbayaz; Isa Nakhai Kamalabadi; Ali Husseinzadeh Kashan; Sakine Beigi
Abstract
Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is ...
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Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is generally determined by assuming a certain lifetime for CNC machine tools to minimize production costs. However, minimizing costs usually results in increased machining time and lower output rates.Methodology: In this research, the optimal machining time is determined using a bi-objective model including minimizing the cost and total production time of a robotic cell with a CNC machine and a material handling robot. It has assumed that identical productions are produced in this robotic cell. Using the Epsilon constraint method, the proposed model is coded in GAMS software and its results are reported.Findings: In this research, the lifespan of the CNC machine tools can be considered as a determined or probable value. The results showed that decreasing the operation time at different speeds does not necessarily impose the same cost on the system. Therefore, it is necessary to be more careful in choosing the appropriate machining time for different tools and parts. Paying attention to the rate of suddenly tool breakdowns is also important in choosing the appropriate time for machining. Using a set of non-dominated solutions, it is possible to determine the appropriate machining time in different parts to achieve a suitable level of problem objectives.Originality/Value: In this research, for the first time, the failure rate of the tool as one of the cost factors in the robotic cell has been added to the cost function of a production cycle and its effect on determining the appropriate machining time has been investigated.
Optimization in science and engineering
Saeid Rezaie moghadam; Aslan Doosti
Abstract
Purpose: The present study concludes that by designing and presenting a mathematical model of multi-objective cogeneration production planning, multi-stage products for several periods in the reverse supply chain under uncertainty conditions were presented using a genetic meta-heuristic algorithm.Methodology: ...
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Purpose: The present study concludes that by designing and presenting a mathematical model of multi-objective cogeneration production planning, multi-stage products for several periods in the reverse supply chain under uncertainty conditions were presented using a genetic meta-heuristic algorithm.Methodology: The first objective function of the model is to minimize costs, the second objective function is to maximize the quality of products in the supply chain, and the third and fourth objective functions are to minimize the total weight of the maximum shortage among customers and to maximize the total weight of the minimum supply. Goods from suppliers. In this model, the first and second objective functions are designed in the case of uncertainty- possible fuzzy stability by Malvey method based on scenario writing.Findings: The results of solving the proposed applied mathematical model developed by coding in MATLAB software were approved by the officials of Borujen Strong Stream Concrete Company, which are given in Tables (16) and (17).Originality/Value: What is important in designing this model, which is formulated in a non-linear programming and has not been observed in similar studies, is the existence of a reconstruction center and a maintenance center and considering target functions for customer and supplier satisfaction and also paying attention to product quality. Suppliers and the product produced by the manufacturer.
Optimization in science and engineering
Mohammad Namakshenas; Mohammad Mahdavi Mazdeh
Abstract
Purpose: The chemical attributes of Technetium-99m have made it popular for most medical imaging procedures. However, in recent years, the decay product of molybdenum-99, i.e., technetium-99m, has become expensive, and its routine availability can no longer be taken for granted. We proposed scenarios ...
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Purpose: The chemical attributes of Technetium-99m have made it popular for most medical imaging procedures. However, in recent years, the decay product of molybdenum-99, i.e., technetium-99m, has become expensive, and its routine availability can no longer be taken for granted. We proposed scenarios to maximize the throughput of Technetium-99m which is used to produce radiopharmaceuticals in a medical imaging center.Methodology: We proved a recursive function to imitate the decay dynamics of Technetium-99m, which is used in 80 percent of medical imaging. Then, we proved necessary and sufficient optimality analysis for this function.Findings: We found optimal scenarios for distributing the radiopharmaceuticals into elusion periods according to clinical considerations.Originality/Value: We developed a rigorous mathematical model based to maximize the throughput of radiopharmaceuticals in a molecular imaging center.
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
Fatemeh Babakordi
Abstract
Since the problems of everyday life are relative , so far various tools such as fuzzy sets, intuitive fuzzy sets, etc. have been expressed to express these ambiguities in mathematical modeling. In 2009, Torra introduced a new horizon for the discussion of hesitant fuzzy sets to discuss issues that are ...
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Since the problems of everyday life are relative , so far various tools such as fuzzy sets, intuitive fuzzy sets, etc. have been expressed to express these ambiguities in mathematical modeling. In 2009, Torra introduced a new horizon for the discussion of hesitant fuzzy sets to discuss issues that are uncertain about decision making. In the course of his work, the quantitative and qualitative expansion of uncertain fuzzy sets is discussed. In this article, for the purpose of introducing more researchers to hesitant fuzzy sets, we review the types of hesitant fuzzy sets such as dual uncertain fuzzy sets, generalized hesitant fuzzy sets, and so on.
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.