Fatemeh Mojibian
Abstract
It’s more than one decade that industrial development based on the structure of industrial clusters as a new strategy has been planned and administrated by developed industrialized countries. Considering the importance of the role of industrial clusters in economic development programs, providing ...
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It’s more than one decade that industrial development based on the structure of industrial clusters as a new strategy has been planned and administrated by developed industrialized countries. Considering the importance of the role of industrial clusters in economic development programs, providing solutions to improve, progress and development of clusters has always been a concern for researchers and specialists.The aim of this study is to provide a mechanism for pricing process of the product in this industrial-economic phenomenon; So that the structure of the proposed model is defined based on mechanisms and activities of the components of industrial clusters. The proposed pricing process is presented based on the concept of Stackelberg game theory and tariff pricing strategy, and in order to solve the model in production level of cluster, a meta-heuristic genetic algorithm is used. Finally, the performance and efficiency of the proposed model is studied in the form of a numerical example, and using the parameter tuning Taguchi method the optimal value of the model variables are presented. Based on the obtained results, the optimal wholesale price of cluster’s products are determined and each manufacturer select the appropriate tariff based on its optimal demand.
Mohammad Nikzamir; vahid baradaran; Yunes Panahi
Abstract
Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that ...
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Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that must be treated beforedisposal or recycling. Hence, this paper seeks to develop a bi-objective mixedinteger programming model for the infectious waste management. In the proposedmodel, in addition to minimizing the chain costs, the reduction of risks for thepopulation exposed to the spread of contamination resulting from infectious wasteis also considered. For this purpose, a multi-echelon chain is proposed by takinginto account the green location-routing problem, which involves the location ofrecycling, disposal, and treatment centers through various treatment technologiesand routing of vehicles between treatment levels and the hospital. The routingproblem has been considered to be multi-depot wherein the criterion of reducingthe cost of fuel consumption of heterogeneous cars is used for green routing.Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developedand, following its validation, its function in solving large-scale problems has beeninvestigated. Results show that the proposed algorithm is effective and efficient.
Alireza Rashidi Komijan; Amin Gordani
Abstract
The problem of Airline planning has totally been divided into four sub-problems.These problems include Flight Scheduling, Fleet Assignment, Aircraft Routing, Maintenance, and Crew Scheduling. In this research, firstly, we defined basic concepts and common terminology about Airline Planning then early ...
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The problem of Airline planning has totally been divided into four sub-problems.These problems include Flight Scheduling, Fleet Assignment, Aircraft Routing, Maintenance, and Crew Scheduling. In this research, firstly, we defined basic concepts and common terminology about Airline Planning then early models and previous researchers were presenting investigated articles. Moreover, by identifying existing research gaps, an Integrated Mathematical model presented for Aircraft Routing and Crew Scheduling for Airlines with Multi Fleet and Multi Maintenance hub with considering the rules of the Airlines. The main purpose of the proposed model is to determine the flight chains for each aircraft and crew assignments to all aircrafts with the attention to the airlines rules and regulations for aircrafts and crew. In the integrated models by previous researcher in this field, usually the type of fleet is considered the same while in the model presented in this research, the type of fleet is considered different. Other innovations of this research consider several maintenance units for an airline. In addition, the minimizations of deadheading flights for crew and aircraft that can impose heavy costs to the airline is presented as a part of the objective function in the model presented. Finally, the problem has been solved into small dimensions by GAMS software and in order to solve it in the larger dimensions a meta-heuristic method is being used, such as genetics algorithm. At the end, we have presented the results, which came from meta-heuristic Algorithm and GAMS Software.
Arman Sajedinejad; Meysam Lotfi
Abstract
In this paper, a non-periodic preventive maintenance scheduling optimization model for multi-component systems is provided based on the maximum availability of system components. In addition to providing the required level of system reliability and satisfy other system constraints (maintenance activities ...
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In this paper, a non-periodic preventive maintenance scheduling optimization model for multi-component systems is provided based on the maximum availability of system components. In addition to providing the required level of system reliability and satisfy other system constraints (maintenance activities and available resources), total costs (direct and indirect) associated with minimal maintenance and, if necessary, one of the maintenance activities include in inspected and serviced simple, preventive repair and preventive replacement for each component, is proposed. Each of these activities uses various sources and regarding the position of the repairing component, effects differently on the reliability of the system. The costs considered include in direct costs (simple service, repair and replacement) as well as indirect costs (out of order and random failures). Since the proposed model has a complex structure, in order to solve the problem, the Genetic Algorithm (G.A) has been used and the results is presented. In the end, performance and use of this model, for a 10-part series - parallel is presented in the form of a case study.
Mohammad Mohammadi; Kamran Forghani
Abstract
The cell formation problem and the group layout problem, both are two important problems in designing a cellular manufacturing system. The cell formation problem is consist of grouping parts into part families and machines into production cells. In addition, the group layout problem is to find the arrangement ...
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The cell formation problem and the group layout problem, both are two important problems in designing a cellular manufacturing system. The cell formation problem is consist of grouping parts into part families and machines into production cells. In addition, the group layout problem is to find the arrangement of machines within the cells as well as the layout of cells.In this paper, an integrated approach is presented to solve the cell formation, group layout and routing problems. By Considering the dimension of machines, the width of the aisles, and the maximum permissible length of the plant site, a new framework, called spiral layout, is suggested for the layout of cellular manufacturing systems. To extend the applicability of the problem, parameters such as part demands, operation sequences, processing times and machine capacities are considered in the problem formulation. The problem is formulated as a bi-objective integer programming model, in which the first objective is to minimize the total material handling cost and the second one is to maximize the total similarity between machines. As the problem is NP-hard, three metaheuristic algorithms, based on Genetic Algorithm and Simulated Annealing are proposed to solve it. To enhance the performance of the algorithms, a Dynamic Programming algorithm is embedded within them. The performance of the algorithms is evaluated by solving numerical examples from the related literature. Finally, a comparison is carried out between the proposed spiral layout and the linear multi-row layout which has recently presented in the literature
Mehdi Seifbarghy; Shima Zangeneh
Abstract
In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. ...
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In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. In this paper, a mixed integer programing (MIP) model presented in order to determine how to serve the customers at the time of disruption in distribution centers in a two-echelon supply chain, including distribution centers and customers. This model selects potential places that minimize traditionally supply chain costs and also the transportation cost after distribution centers disruptions. In fact, the model tries to choose the distribution centers facilities with lowest cost and highest reliability and also allocate them to customers. The problem divided into two sub-problems using Lagrangian relaxation approach. By examining sub-problems optimal conditions, a heuristic solution is used for the first sub-problem and a genetic algorithm is used for the second sub-problem to solve large-scale problems. Finally, numerical examples are presented to examine the performance and efficiency of the proposed model and approach
Mohammad saeed Company; Parham Azimi
Abstract
In this study, the use of simulation technique in bi-objective optimization of assembly line balancing problem has been studied. The aim of this paper is to determine optimal allocation of human resource and equipment to parallel workstations in order to minimize the cost of adding equipment and manpower ...
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In this study, the use of simulation technique in bi-objective optimization of assembly line balancing problem has been studied. The aim of this paper is to determine optimal allocation of human resource and equipment to parallel workstations in order to minimize the cost of adding equipment and manpower among the stations and maximize the production output. In other words, with optimal use of resources, production output is maximized and therefore productivity become maximum. To this end, with optimization via simulation, the production line process is simulated in the form of a simulation model in the ED software. After validating the simulation model using design of experiment, various scenarios designed and run in the simulation model. Possible results for human resource and equipment variables, obtained by genetic algorithm are shown in a Pareto chart and have compared with the production line current situation
Laya Olfat; Maghsod Amiri; Ahmad Jafarian
Abstract
Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock ...
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Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock costs. This paper provides a model for minimizing total distance traveled by vehicles in the external environment of a cross-dock. In this paper, Vehicles routes was modeled with capacitated vehicle routing problem (CVRP) and genetic algorithm (GA) was used to solve the model. To validate responses obtained by GA, simulated annealing (SA) was used. Also, to evaluate the efficacy of two algorithms (SA & GA) in different CVRP problems in cross-dock, 10 problems with different dimensions are evaluated. The results show that in problems with smaller size GA is more efficient, whereas in large size problems SA is more efficient
Akdar Alemtabriz; Ashkan Ayough; Mahdie Baniasadi
Abstract
During the recent years, extensive research has been done on the field ofproject scheduling. There is always uncertainty in the area of projectscheduling that causes a deviation in the real plan from the scheduled plan.One of the solutions to deal with this uncertainty is using the critical chainmethod ...
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During the recent years, extensive research has been done on the field ofproject scheduling. There is always uncertainty in the area of projectscheduling that causes a deviation in the real plan from the scheduled plan.One of the solutions to deal with this uncertainty is using the critical chainmethod (CCM) in project scheduling. This method which is derived from thetheory of constraints (TOC) is a new method in project control which was firstproposed by Goldartt in 1997.In this research we attempt to use the principalsof critical chain in resource-constrained project scheduling problem. The maininnovation in this research is presentation of critical chain project schedulingproblem model with consideration of feeding buffer and using float as asupplement for feeding buffer. For this matter, the project scheduling underresources constraints with critical chain approach was first written and itsreliability was evaluated using the Lingo software. In the next step thesolution algorithm of this model was developed using the genetic algorithmand finally different sample issues were investigated. The results of thisresearch show the efficiency of the presented genetic algorithm
Hamidreza Shahabifard; Behrouz Afshar-nadjafi
Abstract
In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems ...
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In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems list, so a genetic based meta-heuristic algorithm isproposed to tackle the developed model. In the proposed algorithm besidecommon operators of genetic algorithms such as crossover & mutation, someintelligent operators are utilized for local search in computed resources andshifting the activities with negative cash flows. The key parameters of thealgorithm are calibrated using Taguchi method to accelerate convergence ofthe proposed algorithm. Then, the algorithm is used to solve 90 testproblems consisting 30 small-scale, 30 middle-scale and 30 large scaleproblems to examine the algorithm’s performance. It is observed that, insmall problems, the obtained solutions from the proposed genetic algorithmhave been comparably better than the local optimum solutions stemmedfrom Lingo software. On the other hand, for the middle and large sizeproblems which there is no local optimum available within the limited CPUtime, robustness of the proposed algorithm is appropriate
Roozbeh . Azizmohammadi; Maghsoud .Amiri; Reza Tavakkoli- Moghadam; Hamid Reza. Mashatzadegan
Volume 14, Issue 42 , October 2016, , Pages 103-121
Abstract
A redundancy allocation problem is a well-known NP-hard problem thatinvolves the selection of elements and redundancy levels to maximize thesystem reliability under various system-level constraints. In many practicaldesign situations, reliability apportionment is complicated because of thepresence of ...
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A redundancy allocation problem is a well-known NP-hard problem thatinvolves the selection of elements and redundancy levels to maximize thesystem reliability under various system-level constraints. In many practicaldesign situations, reliability apportionment is complicated because of thepresence of several conflicting objectives that cannot be combined into asingle-objective function. A stele communications, manufacturing and powersystems are becoming more and more complex, while requiring shortdevelopments schedules and very high reliability, it is becoming increasinglyimportant to develop efficient solutions to the RAP. In this paper, a newhybrid multi-objective competition algorithm (HMOCA)based oncompetitive algorithm (CA) and genetic algorithm (GA) is proposed for thefirst time in multi-objective redundancy allocation problems. In the multiobjectiveformulation, the system reliability is maximized while the cost andvolume of the system are minimized simultaneously. Additionally, ay RSMis employed to tune the CA parameters. The proposed HMOCA is validatedby some examples with analytical solutions. It shows its superiorperformance compared to a NSGA-II and PAES algorithms. Finally, theconclusion is given
Mojgan Khorasani; Abolfazl Kazemi
Abstract
This paper investigates a supplier evaluation and selection model which considers features of leagile supply chain and allocates orders to the selected suppliers. In the proposed model, potential suppliers are evaluated by applying a multi-objective model with respect to the key features of leagile supply ...
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This paper investigates a supplier evaluation and selection model which considers features of leagile supply chain and allocates orders to the selected suppliers. In the proposed model, potential suppliers are evaluated by applying a multi-objective model with respect to the key features of leagile supply chain, including the ability to respond to demands, reducing delay time and costs (which are the winning factors for being the winner of market in this supply chain), as well as considering the capacities and limitations of organizations and suppliers. The proposed model allows buyer to select several suppliers. In addition, the model is multi-products and multi-periods. Due to long time and inefficiency of exact methods for large-sized problems, in addition to Lingo software, Genetic Algorithm is used to achieve the optimum solution
Masoud Rabbani,; Neda Manavizadeh; Amir Farshbaf-Geranmayeh
Volume 13, Issue 37 , July 2015, , Pages 5-35
Abstract
In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness ...
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In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness and maximizing reliability of suppliers are considered as objective functions in the proposed mathematic model. In order to have a more reliable model in case of disruption, the robustness measure is used in the model. Moreover, because of the lack of information, the economic factors such as tax rate, interest rate, and inflation are considered as uncertain factors in the model. An interactive possibilistic programming approach is applied for solving the multi-objective model. To solve larger size instances, genetic algorithm is proposed. Finally numerical examples are presented to show how the model works in practice
Ali Mohtashami; Ali Fallahian-Najafabadi
Volume 11, Issue 31 , January 2014, , Pages 55-84
Abstract
In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing ...
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In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified.
Hadi Hematiyan; Meysam Sarreshtehdar; Hassan Hadipour
Volume 9, Issue 23 , December 2011, , Pages 163-186
Abstract
The present study aims at optimizing the vehicle brake drum assembling process. Considering the crucial role of the Axle unit, especially its vehicle brake drum which is related to the safety of the passengers, the study of the producing and assembling processes and conducting the quality control experiments ...
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The present study aims at optimizing the vehicle brake drum assembling process. Considering the crucial role of the Axle unit, especially its vehicle brake drum which is related to the safety of the passengers, the study of the producing and assembling processes and conducting the quality control experiments during these stages is of great importance. With regard to the great significance of three main factors, namely, seal-oil spindle diameter, seal-oil internal diameter, and nut lock torque as independent variables, the present research attempts to optimize the rotatory torque of the automobile brake drum getting help from the discussions in the RSM and the unsteady of the automobile brake drum getting help from fuzzy regression using least absolute deviation estimators. Finally optimal solution perused by nonlinear programming model and Genetic Algorithm using one of multi-objective existing methods (LP-metric). Comparing the two optimization methods is shown that the GA technique has better performance rather than nonlinear programming model.
Payam Chiniforooshan; Behrooz Pourghannad; Narges Shahraki
Volume 9, Issue 23 , December 2011, , Pages 209-231
Abstract
In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and ...
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In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and practical constraints. In order to increase the flexibility provided by the multiplicity of routings, the model distributes production volume of each part among alternative routes. Also, a constraint enforcing work load balancing among machines is included in the model. Due to the complexity and combinatorial nature of this model, an enhanced algorithm comprised of a genetic algorithm and a linear programming is proposed for solving the model. The proposed algorithm is tested by a range of test problems and compared with two algorithms from the literature .The computational results show that the proposed algorithm is effective and the proposed approach offers better solution.
Hasan Shavandi; Mehdi Mardane Khameneh
Volume 8, Issue 20 , March 2011, , Pages 27-48
Abstract
On the networks existing servers and customers, each node indicates a customer demand and demand rate is estimated for them. The edges of the network indicate connective ways among the nods which is usually shown with the distance of two nods or the time of travelling. In the covering location problems, ...
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On the networks existing servers and customers, each node indicates a customer demand and demand rate is estimated for them. The edges of the network indicate connective ways among the nods which is usually shown with the distance of two nods or the time of travelling. In the covering location problems, the objective is locating some of the servers on the network in a way that the customers' demand supported by the maximum covering of the servers and optimized objective criterion. In this research the location model with Probability Structure, which the probability of choosing servers by customer is estimated based on their distance, is developed. In the presented model, supposing there is a competitive market, lost demand is considered, too. And according to the mentioned matter the objective of the model is to minimize the cost of losing demands or to maximize the earned profits of responding to the demands. Then, we propose a genetic algorithm (GA) to solve this model. In addition, we employ design of experiments and response surface methodology to both tune the GA parameters and to evaluate the performance of the proposed method in 45 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is very well.
Mehdi Seifbarghy; Razieh Forghani; Zarifeh Rathi
Volume 8, Issue 18 , September 2010, , Pages 1-13
Abstract
Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints ...
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Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints in the mode; for example, location of a limited number of servers in such a way as to maximize the covering considering the constraint regarding to the queue length. In this paper, we extend the proposed model by Correa and Lorena [3] which maximizes the covering. We consider a more objective function in such a way as to minimize the total distance between the servers and demand points. A genetic algorithm based heuristic is proposed to solve the model and results are compared with that of given by CPLEX as a standard solver to estimate the performance of the given algorithm.
M.J. Tarokh; K. Sharifiyan
Volume 6, Issue 17 , September 2007, , Pages 153-181
Abstract
Financial corporation and banks are sort of organization that due to specialty of their work, are very needy to customer management process ; and data mining is one of the best available tools for them to asses definition and behavior forecast of their customers.
Data mining is improving very fast and ...
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Financial corporation and banks are sort of organization that due to specialty of their work, are very needy to customer management process ; and data mining is one of the best available tools for them to asses definition and behavior forecast of their customers.
Data mining is improving very fast and due to presence of vide range of data using computer is essential. Nets & powerful algorithms are used to emplace of manual analysis to derive knowledge & information from data.
In this paper: “Mellat Bank” and its information bank of different division has been evaluated after data extraction from information bank and noise distortion , k means algorithm and fuzzy - k - means algorithm standard test of cluster's compression were used for customer clustering in groups. Determination of optimum number of clusters is done by applying cluster quality assay function. Afterward was used to determine the quality of gained clusters. Then the value of each cluster was determined through FRM model. At the end of project for clusters analysis and define appropriate strategy for each cluster; the pyramid of customer value was used.
Jamshid Salehi Sadaghiani; Seyed Amir Reza Abtahi
Volume 4, Issue 13 , June 2006, , Pages 89-122
Abstract
The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the ...
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The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.
