2017
24
2
2
135
http://scientiairanica.sharif.edu/4058.html
10.24200/sci.2017.4058
Research on the inventory control of the remanufacturing reverse logistics based on the quantitative examination
Research on the inventory control of the remanufacturing reverse logistics based on the quantitative examination
2
2
Based on the remanufacturing reverse logistics system, this paper studies the inventory control problem of the entire supply chain. Considering a variety of products and raw materials, we established amultiproduct multiecheloninventory control model of the remanufacturing reverse logisticsbased on the quantitative examination. And a numerical simulation is performed, which can prove that the model can reduce the inventory cost of remanufacturing reverse logistics, and can provide theoretical basis for determining the production batch and the processing batch for manufacturer and the recycling center. Then, using sensitivity analysis, it proves that the recovery rate and the remanufacturingrate of the recycled products have a great influence on the inventory cost of the reverse logistics, production and inventoryof the manufacturer and the recycling center.
2
Based on the remanufacturing reverse logistics system, this paper studies the inventory control problem of the entire supply chain. Considering a variety of products and raw materials, we established amultiproduct multiecheloninventory control model of the remanufacturing reverse logisticsbased on the quantitative examination. And a numerical simulation is performed, which can prove that the model can reduce the inventory cost of remanufacturing reverse logistics, and can provide theoretical basis for determining the production batch and the processing batch for manufacturer and the recycling center. Then, using sensitivity analysis, it proves that the recovery rate and the remanufacturingrate of the recycled products have a great influence on the inventory cost of the reverse logistics, production and inventoryof the manufacturer and the recycling center.
741
750
Weiqi
Zhou
Weiqi
Zhou
Iran
zwq@ujs.edu.cn
Long
Chen
Long
Chen
Iran
Reverse logistics
Inventory
remanufacturing
Quantitative examination
Multiechelon
http://scientiairanica.sharif.edu/4059.html
10.24200/sci.2017.4059
A queuing location–allocation model for a capacitated health care system
2
2
The aim of the present paper is to propose a location–allocation model, for a capacitated health care system. This paper develops a discrete modeling framework to determine the optimal number of facilities among candidates and optimal allocations of existing customers for operating health centers in a coverage distance, so that the total sum of customer and operating facility costs are minimized.Our goal is to create a model that is more practical in the real world. Therefore, setup costs of the hospitals are based on the costs of customers, fixed costs of establishing health centers and costs based on theavailable resources in each level of hospitals.In this paper, the idea of hierarchical structure has been used. There are two levels of service in hospitals including low and high levels and sections at different levels that provide different types of services. The patients are referred to the hospital’s different sections according to their requirements. To solve the model, two metaheuristic algorithms, including genetic algorithm, simulated annealing and their combination are proposed. To evaluate the performance of the three algorithms, some numerical examples are produced and analyzed using the statistical test in order to determine which algorithm works better.
2

751
764
Mahsa
Pouraliakbari
Mahsa
Pouraliakbari
Iran
Mohammad
Mohammadi
Mohammad
Mohammadi
Kharazmi University
Iran
mohammadi@khu.ac.ir
Abolfazl
Mirzazadeh
Abolfazl
Mirzazadeh
Iran
a.mirzazadeh@aut.ac.ir
Health care system
mixedinteger programming
queuing theory
Capacitated system
Genetic Algorithm
Simulated annealing algorithm
Hybrid algorithm
http://scientiairanica.sharif.edu/4060.html
10.24200/sci.2017.4060
A new mathematical model for a scheduling problem of dynamic machinetool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm
A new mathematical model for a scheduling problem of dynamic machinetool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm
2
2
Although a machinetool selection and operation allocation problem of a flexible manufacturing system (FMS) is known for its complexity, scheduling of these systems is more operative and more complex. This paper considers scheduling of an FMS with dynamic machinetool selection and operation allocation. In addition, due to the NPhard nature of this problem, a modified evolutionary algorithm (EA) considering an island model is proposed to solve the given problem. Its performance is tested on a number of randomly generated problems. Furthermore, the related results are compared with the results obtained by a branchandbound (B&B) method. It has been found that the modified EA with the island model gives good results in terms of the objective function values and CPU times.
2
Although a machinetool selection and operation allocation problem of a flexible manufacturing system (FMS) is known for its complexity, scheduling of these systems is more operative and more complex. This paper considers scheduling of an FMS with dynamic machinetool selection and operation allocation. In addition, due to the NPhard nature of this problem, a modified evolutionary algorithm (EA) considering an island model is proposed to solve the given problem. Its performance is tested on a number of randomly generated problems. Furthermore, the related results are compared with the results obtained by a branchandbound (B&B) method. It has been found that the modified EA with the island model gives good results in terms of the objective function values and CPU times.
765
777


M.H.M.A.
Jahromi
Islamic Azad University
Iran
mhm_jahromi@yahoo.com


Reza
TavakkoliMoghaddam
University of Tehran
Iran
tavakoli@ut.ac.ir


Ahmad
Makui
Iran University of Science and Technology
Iran
amakui@iust.ac.ir


Abbas
Saghaei
Islamic Azad University
Iran
a.saghaei@srbiau.ac.ir
flexible manufacturing systems
mathematical model
Evolutionary algorithm
island model
http://scientiairanica.sharif.edu/4061.html
10.24200/sci.2017.4061
CredibilityBased Fuzzy Mathematical Programming for BiObjective Capacitated Partial Facility Interdiction with Fortification and Demand Outsourcing Model
CredibilityBased Fuzzy Mathematical Programming for BiObjective Capacitated Partial Facility Interdiction with Fortification and Demand Outsourcing Model
2
2
The concepts of fortification and partial interdiction have not been considered concurrently in previous studies. In this paper for the first time we added the fortification and partial interdiction concepts to interdiction problem, the reason is that in interdiction situations, defenders decide to protect some important facilities according to their budgets, and attackers like to destroy most unprotected facilities according to their resources and therefore to cripple the defenders systems. Also, we use the advantages of credibilitybased fuzzy mathematical programming and introduce an integrated model based on uncertainty contexts. In this biobjective model decision maker gives satisfaction degrees for constraints and then we use the interactive possibility model to solve the biobjective model with varying confidence levels. These confidence levels specify the knowledge of attacker and defender about themselves. Also, we propose genetic algorithm (GA) to solve the suggested model. In the experiments, we generate problem instances and solve them with multiobjective mixedinteger nonlinear programming (MOMINLP) and the proposed genetic algorithm for various settings.
2
The concepts of fortification and partial interdiction have not been considered concurrently in previous studies. In this paper for the first time we added the fortification and partial interdiction concepts to interdiction problem, the reason is that in interdiction situations, defenders decide to protect some important facilities according to their budgets, and attackers like to destroy most unprotected facilities according to their resources and therefore to cripple the defenders systems. Also, we use the advantages of credibilitybased fuzzy mathematical programming and introduce an integrated model based on uncertainty contexts. In this biobjective model decision maker gives satisfaction degrees for constraints and then we use the interactive possibility model to solve the biobjective model with varying confidence levels. These confidence levels specify the knowledge of attacker and defender about themselves. Also, we propose genetic algorithm (GA) to solve the suggested model. In the experiments, we generate problem instances and solve them with multiobjective mixedinteger nonlinear programming (MOMINLP) and the proposed genetic algorithm for various settings.
778
793
M. A.
Azadeh
M. A.
Azadeh
Industrial Eng.
Iran
aazadeh@ut.ac.ir
R.
Kokabi
Reza
Kokabi
University of Tehran
Iran
kokabi.reza@ut.ac.ir
D.
Hallaj
Diako
Hallaj
Iran University of Science and Technology
Iran
d_hallaj@ind.iust.ir
Facility interdiction
Fortification
Fuzzy mathematical programming
Chance constrained programming
Multiobjective mixedinteger nonlinear programming (MOMINLP)
Genetic Algorithm
http://scientiairanica.sharif.edu/4062.html
10.24200/sci.2017.4062
A Method for Multiattribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection
A Method for Multiattribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection
2
2
The trustworthy service selection is a typical multiattribute group decision making (MAGDM) problem. The aim of this paper is to develop a novel method for MAGDMwithtriangular intuitionistic fuzzy numbers (TIFNs) and apply to the trustworthy service selectionproblem.Firstly, we define the meanindex, varianceindex and standard deviation of TIFN. And a new distance measure of TIFNs is proposed and corresponding proofs are given. Based on these concepts of meanindex and standard deviation, a ranking method for TIFNs is developed considering the risk preferenceof decision maker (DM). Further, according to the crisp relative closeness coefficient matrix with respect to the normalized TIFNs decision matrix, we use entropy measure to obtain attribute weights. The DMs weights are calculated by the similarity between the individual decisions and the average decisions. Then, a decision procedure is described to solve the MAGDM under triangular intuitionistic fuzzy environment. Finally, a real trustworthy service selection example is analyzed to verify the practicality and effectiveness of the developed method.
2
The trustworthy service selection is a typical multiattribute group decision making (MAGDM) problem. The aim of this paper is to develop a novel method for MAGDMwithtriangular intuitionistic fuzzy numbers (TIFNs) and apply to the trustworthy service selectionproblem.Firstly, we define the meanindex, varianceindex and standard deviation of TIFN. And a new distance measure of TIFNs is proposed and corresponding proofs are given. Based on these concepts of meanindex and standard deviation, a ranking method for TIFNs is developed considering the risk preferenceof decision maker (DM). Further, according to the crisp relative closeness coefficient matrix with respect to the normalized TIFNs decision matrix, we use entropy measure to obtain attribute weights. The DMs weights are calculated by the similarity between the individual decisions and the average decisions. Then, a decision procedure is described to solve the MAGDM under triangular intuitionistic fuzzy environment. Finally, a real trustworthy service selection example is analyzed to verify the practicality and effectiveness of the developed method.
794
807
shuping
wan
shuping
wan
College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China
Iran
shupingwan@163.com
jun
xu
jun
xu
Iran
xujun1028@126.com
Trustworthy service selection
multiattribute group decision making
triangular intuitionistic fuzzy numbers
meanindex
varianceindex
relative closeness coefficient
http://scientiairanica.sharif.edu/4063.html
10.24200/sci.2017.4063
Reliability analysis of a warm standby repairable system with two cases of imperfect switching mechanism
Reliability analysis of a warm standby repairable system with two cases of imperfect switching mechanism
2
2
This paper studies a warm standby repairable system including two dissimilar units, one repairman and imperfect switching mechanism. Times to failure and times to repair of active and standby units are assumed to be exponentially distributed. Two cases of unreliable switching mechanism are considered. In case one, the failed active unit will be replaced by the available warm standby unit with coverage probability. However, in case two, the switching mechanism is repairable and its failure time and repair time are also exponentially distributed. Using Markov process and Laplace transforms, the explicit expressions of the mean time to failure, MTTF, and the steady state availability of the two systems are derived analytically. Finally, by solving a numerical example, comparison of the two systems are made based on various reliability and availability characteristics. Moreover, sensitivity analyses of the reliability and availability indexes with respect to the model parameters are accomplished.
2
This paper studies a warm standby repairable system including two dissimilar units, one repairman and imperfect switching mechanism. Times to failure and times to repair of active and standby units are assumed to be exponentially distributed. Two cases of unreliable switching mechanism are considered. In case one, the failed active unit will be replaced by the available warm standby unit with coverage probability. However, in case two, the switching mechanism is repairable and its failure time and repair time are also exponentially distributed. Using Markov process and Laplace transforms, the explicit expressions of the mean time to failure, MTTF, and the steady state availability of the two systems are derived analytically. Finally, by solving a numerical example, comparison of the two systems are made based on various reliability and availability characteristics. Moreover, sensitivity analyses of the reliability and availability indexes with respect to the model parameters are accomplished.
808
822
Meisam
Sadeghi
Meisam
Sadeghi
Department of Industrial Engineering, K. N. Toosi University of Technology
Iran
meisam.sadeghi.m@gmail.com
Emad
Roghanian
Emad
Roghanian
KNTU
Iran
e_roghanian@kntu.ac.ir
Markov Process
Switching mechanisms
Reliability
MTTF
Steady state availability
http://scientiairanica.sharif.edu/4064.html
10.24200/sci.2017.4064
A Benders decomposition algorithm for multifactory scheduling problem with batch delivery
A Benders decomposition algorithm for multifactory scheduling problem with batch delivery
2
2
The multifactory supply chain problem is investigated to determine the production and transportation scheduling of jobs which are allowed to be transported by batches. This is a mixedinteger optimization problem, which could be challenging to solve. The problem incorporates two parts: (1) assigning jobs to appropriate batch, and (2) scheduling jobs of batches for production and transportation. Based on the problem structure and because of its NPhardness characteristics, Benders decomposition is recognized as a suitable approach. This approach decomposes the problem into assignment master problem and scheduling subproblem. This would facilitate the solution procedure. By comparing performance of the proposed algorithm with an exact approach: Branch and Bound, It is achieved that it is able to find the near optimal solution in smaller computational times than the Branch and Bound.
2
The multifactory supply chain problem is investigated to determine the production and transportation scheduling of jobs which are allowed to be transported by batches. This is a mixedinteger optimization problem, which could be challenging to solve. The problem incorporates two parts: (1) assigning jobs to appropriate batch, and (2) scheduling jobs of batches for production and transportation. Based on the problem structure and because of its NPhardness characteristics, Benders decomposition is recognized as a suitable approach. This approach decomposes the problem into assignment master problem and scheduling subproblem. This would facilitate the solution procedure. By comparing performance of the proposed algorithm with an exact approach: Branch and Bound, It is achieved that it is able to find the near optimal solution in smaller computational times than the Branch and Bound.
823
833
Neda
Karimi
N.
Karimi
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 1591634311, Iran
Iran
n.karimi@aut.ac.ir
Hamid
Davoudpour
H.
Davoudpour
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 1591634311, Iran
Iran
h.davoudpour@hotmail.com
Multifactory scheduling
Batch delivery
Benders Decomposition
mixedinteger programming
http://scientiairanica.sharif.edu/4065.html
10.24200/sci.2017.4065
A new model to optimize the knowledge exchange in industrial cluster: A case study of Semnan plaster production industrial cluster
A new model to optimize the knowledge exchange in industrial cluster: A case study of Semnan plaster production industrial cluster
2
2
Industrial clusters bring member firms the opportunities and advantages to save resources and increase competitiveness through cooperation and joint activities. One of these opportunities is knowledge exchange, using shared resources. If cluster firms want to create knowledge directly or acquire it from out of cluster, it is necessary to spend much money and time. The aim is to maximize knowledge transfer between firms of a cluster regarding the limitation of budget and time, using existing knowledge flow networks. This problem is formulated with a new model of mixed integer programming and solved by the CPLEX solver for Semnan plaster production industrial clusters. The results of sensitivity analysis show that knowledge transfer is much more influenced by budget than time constraints. The results help cluster managers to have a better understanding, regarding the available resources and business conditions, to maximize the results obtained from knowledge transfer process in industrial cluster members.
2
Industrial clusters bring member firms the opportunities and advantages to save resources and increase competitiveness through cooperation and joint activities. One of these opportunities is knowledge exchange, using shared resources. If cluster firms want to create knowledge directly or acquire it from out of cluster, it is necessary to spend much money and time. The aim is to maximize knowledge transfer between firms of a cluster regarding the limitation of budget and time, using existing knowledge flow networks. This problem is formulated with a new model of mixed integer programming and solved by the CPLEX solver for Semnan plaster production industrial clusters. The results of sensitivity analysis show that knowledge transfer is much more influenced by budget than time constraints. The results help cluster managers to have a better understanding, regarding the available resources and business conditions, to maximize the results obtained from knowledge transfer process in industrial cluster members.
834
846
Hamid Reza
Dezfoulian
Hamid Reza
Dezfoulian
Iran
hrdezfoolian@yahoo.com
Abbas
Afrazeh
Abbas
Afrazeh
Iran
afrazeh.abas@gmail.com
Behrooz
Karimi
Behrooz
Karimi
Iran
b.karim@aut.ac.ir
Knowledge exchange
optimization
Industrial cluster
New mathematical model
Organizational social relationships
http://scientiairanica.sharif.edu/4066.html
10.24200/sci.2017.4066
Single machine scheduling problem with convex multiresource dependent processing times and job deadlines
2
2
In this paper, the single machine scheduling problem with convex multiresource dependent processing times, subject to meeting job deadlines is considered. The objective is to minimize the total cost, including the resource allocation costs and the fixed costs. We assume that the actual processing time of each job (task) is a function of the amount of resources allocated. Therefore, the decision variables of the model are: 1) resources allocated to the jobs, 2) total consumed resources, 3) processing times of the jobs, and 4) start/completion times of the jobs. We reformulate and solve the problem using a posynomial geometric programming model. In the proposed exact solution method based on the geometric programming, the original problem of any size is reduced to a twovariable unconstrainted optimization problem which can be easily solved by a simple grid search.
2

847
855
M.
Ziaee
M.
Ziaee
Department of Industrial Engineering University of Bojnord, 9453155111 Bojnord, Iran
Iran
ziaee@iust.ac.ir
Scheduling
Single machine scheduling problem
Convex multiresource dependent processing times
Job deadlines
Posynomial geometric programming
http://scientiairanica.sharif.edu/4067.html
10.24200/sci.2017.4067
Model and optimization of the multiobjective singlebuyer multivendor integrated inventory problem with multiple quantity discounts
Model and optimization of the multiobjective singlebuyer multivendor integrated inventory problem with multiple quantity discounts
2
2
This paper deals with a multiobjective integrated inventory model to coordinate a twostage supply chain including a single buyer and multiple vendors. The earlier work on the problem is limited to consider only one type of discount. This paper extends the problem under the multiple quantity discount environment. We try to minimize the system cost, the number of defective items, the number of late delivered items and maximize the total purchasing value. Numerical examples are presented to provide some insights about the proposed model and different discount schemes. Results obtained from sensitivity analysis show that changes in unit prices have a relatively large effect on the objective function and as the upper bounds of discount intervals are reduced, the value of objective function decreases. It also seen that the order quantity from the suppliers increases as the number of suppliers offering all unit quantity discount increases. In addition, we use a solution approach that is not used by previous studies on this problem and the obtained results show that the DE algorithm, proposed in this study, outperforms the PSO proposed by Kamali et al. [1] in both solution quality and computational time.
2
This paper deals with a multiobjective integrated inventory model to coordinate a twostage supply chain including a single buyer and multiple vendors. The earlier work on the problem is limited to consider only one type of discount. This paper extends the problem under the multiple quantity discount environment. We try to minimize the system cost, the number of defective items, the number of late delivered items and maximize the total purchasing value. Numerical examples are presented to provide some insights about the proposed model and different discount schemes. Results obtained from sensitivity analysis show that changes in unit prices have a relatively large effect on the objective function and as the upper bounds of discount intervals are reduced, the value of objective function decreases. It also seen that the order quantity from the suppliers increases as the number of suppliers offering all unit quantity discount increases. In addition, we use a solution approach that is not used by previous studies on this problem and the obtained results show that the DE algorithm, proposed in this study, outperforms the PSO proposed by Kamali et al. [1] in both solution quality and computational time.
856
867
Amir
Kamali
Amir
Kamali
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran
Iran
amir_kamali@aut.ac.ir
S.M.T.
Fatemi Ghomi
S.M.T.
Fatemi Ghomi
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran
Iran
fatemi@aut.ac.ir
F.
Jolai
F.
Jolai
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
fjolai@ut.ac.ir
Integrated inventory
Multivendor
Multiple discounts
Multiobjective
http://scientiairanica.sharif.edu/4068.html
10.24200/sci.2017.4068
A new optimization algorithm for parameter optimization of nanofinishing processes
A new optimization algorithm for parameter optimization of nanofinishing processes
2
2
Material removal rate and surface roughness are the most important performance measures in nanofinishing processes and these are largely influenced by the process parameters. The optimum combination of process parameters for nanofinishing processes is determined in this paper using a recently proposed optimization algorithm, named as Jaya algorithm. The results show the better performance of the Jaya algorithm over the other approaches attempted by the previous researchers such as genetic algorithm and desirability function approach for the same nanofinishing processes. The results obtained by the Jaya algorithm are useful for the real production systems.
2
Material removal rate and surface roughness are the most important performance measures in nanofinishing processes and these are largely influenced by the process parameters. The optimum combination of process parameters for nanofinishing processes is determined in this paper using a recently proposed optimization algorithm, named as Jaya algorithm. The results show the better performance of the Jaya algorithm over the other approaches attempted by the previous researchers such as genetic algorithm and desirability function approach for the same nanofinishing processes. The results obtained by the Jaya algorithm are useful for the real production systems.
868
875
R. Venkata
Rao
R. Venkata
Rao
Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat 395007, India
Iran
Dhiraj P.
Rai
Dhiraj P.
Rai
Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat 395007, India
Iran
J.
Balic
J.
Balic
Faculty of Mechanical Engineering, University of Maribor, Slovenia
Iran
Nanofinishing processes
Parameters optimization
Jaya algorithm