18
2008-4889
Iran University of Science & Technology
766
Decision Analysis and Methods
A Nadir Compromise Programming for Supplier Selection Problem under Uncertainty
Ekhtiari
Mostafa
^{
b
}
Zandieh
Mostafa
^{
c
}
Alem-Tabriz
Akbar
^{
d
}
Rabieh
Masood
^{
e
}
^{
b
}Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
^{
c
}Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
^{
d
}Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
^{
e
}Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
1
3
2018
29
1
1
14
07
06
2017
27
01
2018
Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. Regarding the fact that decision makers (DMs) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. This paper develops a nadir compromise programming (NCP) model for decision-making under uncertainty on the selection of suppliers within the framework of binary programming. Depending on the condition of uncertainty, three statuses are taken into consideration and a solution approach is proposed for each status. A pure deterministic NCP model is presented for solving the problem in white condition (certainty of data) and a solution approach resulted from combination of NCP and stochastic programming is introduced to solve the model in black (uncertainty of data) situation. The paper also proposes a NCP model under certainty and uncertainty for solving problem under grey (a combination of certainty and uncertainty of data) conditions. The proposed approaches are illustrated for a real problem in steel industry with multiple objectives. Also, a simulation approach has been designed in order to examine the results obtained and also verifies capabilities of the proposed model.
689
Other Related Subject
Minimizing the Number of Tardy Jobs in the Single Machine Scheduling Problem under Bimodal Flexible and Periodic Availability Constraints
Moslehi
Ghasem
^{
f
}
Mashkani
Omolbanin
^{
g
}
^{
f
}Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
^{
g
}Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
1
3
2018
29
1
15
34
26
08
2016
02
01
2018
In single machine scheduling problems with availability constraints, machines are not available for one or more periods of time. In this paper, we consider a single machine scheduling problem with flexible and periodic availability constraints. In this problem, the maximum continuous working time for each machine increases in a stepwise manner with two different values allowed. Also, the duration of unavailability for each period depends on the maximum continuous working time of the machine in that same period, again with two different values allowed. The objective is to minimize the number of tardy jobs. In the first stage, the complexity of the problem is investigated and a binary integer programming model, a heuristic algorithm and a branch-and-bound algorithm are proposed in a second stage. Computational results of solving 1680 sample problems indicate that the branch-and-bound algorithm is capable of not only solving problems of up to 20 jobs but also of optimally solving 94.76% of the total number of problems. Based on numerical results obtained, a mean average error of 2% is obtained for the heuristic algorithm.
764
Logistic & Apply Chain
Supplier Selection and Order Allocation under Risk: Iranian Oil and Gas Drilling Companies
Torabi
S. Ali
^{
h
}
Boostani
Abtin
^{
i
}
^{
h
}School of Industrial Engineering, College of Engineering
^{
i
}School of Industrial Engineering, College of Engineering
1
3
2018
29
1
35
52
06
06
2017
26
12
2017
This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.
788
Manufacturing Process & Systems
Study of buffer effects on the grouping efficacy measure of stochastic cell formation problem
Esmailnezhad
bahman
^{
j
}
saidi-Mehrabad
mohammad
^{
k
}
^{
j
}Tehran Jalal AleAhmad Nasr P.O.Box: 14115-111
^{
k
}Iran University of Science and Technology
1
3
2018
29
1
53
63
26
09
2017
26
09
2017
This paper deals the stochastic cell formation problem (SCFP). The paper presents a new nonlinear integer programming model for the SCFP in which the effect of buffer size on the grouping efficacy of cells has been investigated. The objective function is the maximization of the grouping efficacy of cells. A chance constraint is applied to explore the effect of buffer on the SCFP. Processing time and arrival time of the part for each cell are considered stochastic and are following exponential probability distribution. To find out the optimal solution in a reasonable time, a heuristic approach is used to linearize the proposed nonlinear model. This problem has been known as an NP-hard problem. Therefore, two metaheuristic methods, namely; genetic algorithm and particle swarm optimization are employed to solve examples. The parameters of the algorithms are calibrated using Taguchi and full factorial methods, and the performances of the algorithms on the examples of various sizes are analyzed against global solutions obtained from Lingo software’s branch and bound (B&B) in terms of quality of solutions and computational time.
767
Intelligent Systems
Goal programming-based post-disaster decision making for allocation and scheduling the rescue units in natural disaster with time-window
Nayeri
Sina
^{
l
}
Asadi-Gangraj
Ebrahim
^{
m
}
Emami
Saeed
^{
n
}
^{
l
}Department of industrial engineering; Babol Noshirvani University of Technology
^{
m
}Department of industrial engineering; Babol Noshirvani University of Technology
^{
n
}Department of industrial engineering; Babol Noshirvani University of Technology
1
3
2018
29
1
65
78
07
06
2017
05
02
2018
Natural disasters, such as earthquakes, tsunamis, and hurricanes cause enormous harm during each year. To reduce casualties and economic losses in the response phase, rescue units must be allocated and scheduled efficiently, such that it is a key issues in emergency response. In this paper, a multi-objective mix integer nonlinear programming model (MOMINLP) is proposed to minimize sum of weighted completion times of relief operations as first objective function and makespan as second objective with considering time-window for incidents. The rescue units also have different capability and each incident just can be allocated to a rescue unit that has the ability to do it. By assuming the incidents and rescue units as jobs and machine, respectively, the research problem can be formulated as a parallel-machine scheduling problem with unrelated machines. Multi-Choice Goal programming (MCGP) is applied to solve research problem as single objective problem. The experimental results shows the superiority of the proposed approach to allocate and schedule the rescue units in the natural disasters.
778
Reliability and Maintenance
Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models
farughi
hiwa
^{
o
}
hakimi
ahmad
^{
p
}
kamranrad
reza
^{
}
^{
o
}University of Kurdistan
^{
p
}University of Kurdistan
^{
}University of Science and Culture
1
3
2018
29
1
79
90
05
08
2017
26
12
2017
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods.
779
Information Processing and Engineering
Detecting frauds using customer behavior trend analysis and known scenarios
Eshghi
Abdollah
^{
}
Kargari
Mehrdad
^{
}
^{
}Tehran Jalal AleAhmad Nasr P.O.Box: 14115-111
^{
}Tehran Jalal AleAhmad Nasr P.O.Box: 14115-111
1
3
2018
29
1
91
101
18
08
2017
04
03
2018
In this paper a fraud detection method is proposed which user behaviors are modeled using two main components namely the un-normal trend analysis component and scenario based component. The extent of deviation of a transaction from his/her normal behavior is estimated using fuzzy membership functions. The results of applying all membership functions on a transaction will then be infused and a final risk is gained which is the basis for decision making in order to block the arrived transaction or not. An optimized threshold for the value of the final risk is estimated in order to make a balance between the fraud detection rate and alarm rate. Although the assessment of such problems are complicated, we show that this method can be useful in application according to several measures and metrics.
795
Operations Managment
Random gravitational emulation search algorithm (RGES (in scheduling traveling salesman problem
Sheibat Alhamdi
Ahmad
^{
}
Hosseinzadeh Kashani
Alireza
^{
}
^{
}correspond
^{
}Azad University of Tehran(North-Tehran)
1
3
2018
29
1
103
112
24
10
2017
03
03
2018
this article proposes a new algorithm for finding a good approximate set of non-dominated solutions for solving generalized traveling salesman problem. Random gravitational emulation search algorithm (RGES (is presented for solving traveling salesman problem. The algorithm based on random search concepts, and uses two parameters, speed and force of gravity in physics. The proposed algorithm is compared with genetic algorithm and experimental results show that the proposed algorithm has better performance and less runtime to be answered.