eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
741
750
10.24200/sci.2017.4058
4058
Research on the inventory control of the remanufacturing reverse logistics based on the quantitative examination
Weiqi Zhou
zwq@ujs.edu.cn
1
Long Chen
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 amulti-product multi-echeloninventory 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.
http://scientiairanica.sharif.edu/article_4058_87928287577c8c1f69f1beecbae25d3b.pdf
Reverse logistics
Inventory
remanufacturing
Quantitative examination
Multi-echelon
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
751
764
10.24200/sci.2017.4059
4059
A queuing location–allocation model for a capacitated health care system
Mahsa Pouraliakbari
1
Mohammad Mohammadi
mohammadi@khu.ac.ir
2
Abolfazl Mirzazadeh
a.mirzazadeh@aut.ac.ir
3
Kharazmi University
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 meta-heuristic 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.
http://scientiairanica.sharif.edu/article_4059_c579a19058be532869dd97efbd269825.pdf
Health care system
Mixed-integer programming
queuing theory
Capacitated system
genetic algorithm
Simulated annealing algorithm
Hybrid algorithm
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
765
777
10.24200/sci.2017.4060
4060
A new mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm
M.H.M.A. Jahromi
mhm_jahromi@yahoo.com
1
Reza Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
2
Ahmad Makui
amakui@iust.ac.ir
3
Abbas Saghaei
a.saghaei@srbiau.ac.ir
4
Islamic Azad University
University of Tehran
Iran University of Science and Technology
Islamic Azad University
Although a machine-tool 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 machine-tool selection and operation allocation. In addition, due to the NP-hard 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 branch-and-bound (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.
http://scientiairanica.sharif.edu/article_4060_61051342f49fe8dd1867af93d0780947.pdf
flexible manufacturing systems
mathematical model
Evolutionary algorithm
island model
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
778
793
10.24200/sci.2017.4061
4061
Credibility-Based Fuzzy Mathematical Programming for Bi-Objective Capacitated Partial Facility Interdiction with Fortification and Demand Outsourcing Model
M. A. Azadeh
aazadeh@ut.ac.ir
1
Reza Kokabi
kokabi.reza@ut.ac.ir
2
Diako Hallaj
d_hallaj@ind.iust.ir
3
Industrial Eng.
University of Tehran
Iran University of Science and Technology
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 credibility-based fuzzy mathematical programming and introduce an integrated model based on uncertainty contexts. In this bi-objective model decision maker gives satisfaction degrees for constraints and then we use the interactive possibility model to solve the bi-objective 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 multi-objective mixed-integer non-linear programming (MOMINLP) and the proposed genetic algorithm for various settings.
http://scientiairanica.sharif.edu/article_4061_29b5989256b1748ae05e435b777d3c79.pdf
Facility interdiction
Fortification
Fuzzy mathematical programming
Chance constrained programming
Multi-objective mixed-integer non-linear programming (MOMINLP)
genetic algorithm
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
794
807
10.24200/sci.2017.4062
4062
A Method for Multi-attribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection
shuping wan
shupingwan@163.com
1
jun xu
xujun1028@126.com
2
College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China
The trustworthy service selection is a typical multi-attribute 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 mean-index, variance-index and standard deviation of TIFN. And a new distance measure of TIFNs is proposed and corresponding proofs are given. Based on these concepts of mean-index 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.
http://scientiairanica.sharif.edu/article_4062_12dc718eba1b24415135c88e6232c2e5.pdf
Trustworthy service selection
multi-attribute group decision making
triangular intuitionistic fuzzy numbers
mean-index
variance-index
relative closeness coefficient
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
808
822
10.24200/sci.2017.4063
4063
Reliability analysis of a warm standby repairable system with two cases of imperfect switching mechanism
Meisam Sadeghi
meisam.sadeghi.m@gmail.com
1
Emad Roghanian
e_roghanian@kntu.ac.ir
2
Department of Industrial Engineering, K. N. Toosi University of Technology
KNTU
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.
http://scientiairanica.sharif.edu/article_4063_a64b93e554b7426c8bf392c83ed7454b.pdf
Markov Process
Switching mechanisms
Reliability
MTTF
Steady state availability
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
823
833
10.24200/sci.2017.4064
4064
A Benders decomposition algorithm for multi-factory scheduling problem with batch delivery
N. Karimi
n.karimi@aut.ac.ir
1
H. Davoudpour
h.davoudpour@hotmail.com
2
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 15916-34311, Iran
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 15916-34311, Iran
The multi-factory 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 mixed-integer 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 NP-hardness characteristics, Benders decomposition is recognized as a suitable approach. This approach decomposes the problem into assignment master problem and scheduling sub-problem. 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.
http://scientiairanica.sharif.edu/article_4064_aae5048c38d57ac4e2b9311cd1d1af24.pdf
Multi-factory scheduling
Batch delivery
Benders Decomposition
Mixed-integer programming
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
834
846
10.24200/sci.2017.4065
4065
A new model to optimize the knowledge exchange in industrial cluster: A case study of Semnan plaster production industrial cluster
Hamid Reza Dezfoulian
hrdezfoolian@yahoo.com
1
Abbas Afrazeh
afrazeh.abas@gmail.com
2
Behrooz Karimi
b.karim@aut.ac.ir
3
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.
http://scientiairanica.sharif.edu/article_4065_00e595332522b5a7391a4ab6030013a0.pdf
Knowledge exchange
optimization
Industrial cluster
New mathematical model
Organizational social relationships
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
847
855
10.24200/sci.2017.4066
4066
Single machine scheduling problem with convex multi-resource dependent processing times and job deadlines
M. Ziaee
ziaee@iust.ac.ir
1
Department of Industrial Engineering University of Bojnord, 94531-55111 Bojnord, Iran
In this paper, the single machine scheduling problem with convex multi-resource 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 two-variable unconstrainted optimization problem which can be easily solved by a simple grid search.
http://scientiairanica.sharif.edu/article_4066_1fc3a3b851c4aaefca1617ce0ce04538.pdf
Scheduling
Single machine scheduling problem
Convex multi-resource dependent processing times
Job deadlines
Posynomial geometric programming
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
856
867
10.24200/sci.2017.4067
4067
Model and optimization of the multi-objective single-buyer multi-vendor integrated inventory problem with multiple quantity discounts
Amir Kamali
amir_kamali@aut.ac.ir
1
S.M.T. Fatemi Ghomi
fatemi@aut.ac.ir
2
F. Jolai
fjolai@ut.ac.ir
3
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
This paper deals with a multi-objective integrated inventory model to coordinate a two-stage 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.
http://scientiairanica.sharif.edu/article_4067_93bc50ff44eeb7f0613276c222b080e0.pdf
Integrated inventory
Multi-vendor
Multiple discounts
Multi-objective
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-04-01
24
2
868
875
10.24200/sci.2017.4068
4068
A new optimization algorithm for parameter optimization of nano-finishing processes
R. Venkata Rao
1
Dhiraj P. Rai
2
J. Balic
3
Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat 395007, India
Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, Gujarat 395007, India
Faculty of Mechanical Engineering, University of Maribor, Slovenia
Material removal rate and surface roughness are the most important performance measures in nano-finishing processes and these are largely influenced by the process parameters. The optimum combination of process parameters for nano-finishing 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 nano-finishing processes. The results obtained by the Jaya algorithm are useful for the real production systems.
http://scientiairanica.sharif.edu/article_4068_71fff3c8e948409b0cb2f424d07dfa2c.pdf
Nano-finishing processes
Parameters optimization
Jaya algorithm