ORIGINAL_ARTICLE
A new probability density function in earthquake occurrences
Although knowing the time of the occurrence of the earthquakes is vital and helpful, unfortunately it is still unpredictable. By the way there is an urgent need to find a method to foresee this catastrophic event. There are a lot of methods for forecasting the time of earthquake occurrence. Another method for predicting that is to know probability density function of time interval between earthquakes. In this paper a new probability density function (PDF) for the time interval between earthquakes is found out. The parameters of the PDF will be estimated, and ultimately, the PDF will be tested by the earthquakes data about Iran.
http://jiei.azad.ac.ir/article_511069_acea5c2c401a239095cc38aae7362314.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
1
6
forecasting
Probability Density Function (PDF)
Distribution function
Earthquake
S
Sadeghian
true
1
Ph.D. Student, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Ph.D. Student, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Ph.D. Student, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
G.R
Jalali-Naini
true
2
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran
AUTHOR
ORIGINAL_ARTICLE
A goal programming model for vehicle routing problem with backhauls and soft time windows
The vehicle routing problem with backhauls (VRPB) as an extension of the classical vehicle routing prob-lem (VRP) attempts to define a set of routes which services both linehaul customers whom product are to be delivered and backhaul customers whom goods need to be collected. A primary objective for the problem usually is minimizing the total distribution cost. Most real-life problems have other objectives addition to this common primary objective. This paper describes a multi-objective model for VRPB with time windows (VRPBTW) and some new assumptions. We present a goal programming approach and a heuristic algorithm to solve the problem. Computational experiments are carried out and performance of developed methods is discussed.
http://jiei.azad.ac.ir/article_511070_6e9a024719289de4db2af35961cc219e.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
7
18
Vehicle Routing Problem
Backhaul
Soft time windows
Goal programming
Heuristic
M
Aghdaghi
true
1
M.Sc. Graduated Student, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
M.Sc. Graduated Student, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
M.Sc. Graduated Student, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
AUTHOR
F
Jolai
true
2
Associate Professor, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
Associate Professor, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
Associate Professor, Dep. of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Quality function deployment: Integrating comprehensive matrix and SWOT analysis for effective decision making
In this ever-changing business scenario, the manufacturing product industries have to be in position to rec-ognize the ever changing pulse and demands of the market. Customer satisfaction and quality management has become a strategic issue for companies in the new millennium. Quality Function Deployment (QFD) lit-erature suggests that building up the House of Quality (HoQ) is not a difficult task, however to analyze and in-terpret the information available is replete with a lot of uncertainty and presents less than optimal solutions. This paper attempts to address these twin issues of the Post-HoQ analysis and its interpretation through SWOT. The development and mechanics of QFD model is presumed to be known to the followers and the paper deals specifically with post-HoQ model through a well-defined and structured approach to comprehen-sive matrix analysis. The paper contributes a method for evaluating and analyzing the customer data and tech-nical data in QFD as a function of the generation of useful information resulting in a better decision making process. The outcome of the study is a comprehensive solution which discusses post-matrix analysis through underlying concepts; requisite steps; information needed; and the computations involved. The applicability of the proposed model is demonstrated with an illustrative hypothetical example of a medical-care product - dis-posable syringe and needle.
http://jiei.azad.ac.ir/article_511071_cd18e57b94046eb9f367d40ad8d55bcd.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
19
31
QFD
HoQ
SWOT
prioritization
decision making
J.R
Sharma
true
1
Assistant Professor, Institute of Management Technology (IMT), Nagpur, India
Assistant Professor, Institute of Management Technology (IMT), Nagpur, India
Assistant Professor, Institute of Management Technology (IMT), Nagpur, India
AUTHOR
A,M
Rawani
true
2
Professor, Head of Mechanical Engineering Department, National Institute of Technology, Raipur, India
Professor, Head of Mechanical Engineering Department, National Institute of Technology, Raipur, India
Professor, Head of Mechanical Engineering Department, National Institute of Technology, Raipur, India
AUTHOR
ORIGINAL_ARTICLE
Ensemble strategies to build neural network to facilitate decision making
There are three major strategies to form neural network ensembles. The simplest one is the Cross Validation strategy in which all members are trained with the same training data. Bagging and boosting strategies pro-duce perturbed sample from training data. This paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based upon these factors, it compares the results of the trained single model with the cross validation one in a case which uses the presidential election data in US. The trained single model is called single best model. In this experience, the comparison shows that the cross validation ensemble leads to lower generalization error.
http://jiei.azad.ac.ir/article_511072_a77a294fc09a6e271a558fddf16f8a5e.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
32
38
Ensemble strategy
Neural Networks
P
Hanafizadeh
true
1
Assistant Professor, Dep. of Industrial Management, Allameh Tabataba' i University, Tehran, Iran
Assistant Professor, Dep. of Industrial Management, Allameh Tabataba' i University, Tehran, Iran
Assistant Professor, Dep. of Industrial Management, Allameh Tabataba' i University, Tehran, Iran
LEAD_AUTHOR
E
Salahi Parvin
true
2
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
AUTHOR
P
Asadolahi
true
3
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
AUTHOR
N
Gholami
true
4
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
M.Sc. Student in Information Technology Management, Allameh Tabataba' i University, Tehran, Iran
AUTHOR
ORIGINAL_ARTICLE
A generalized implicit enumeration algorithm for a class of integer nonlinear programming problems
Presented here is a generalization of the implicit enumeration algorithm that can be applied when the objec-tive function is being maximized and can be rewritten as the difference of two non-decreasing functions. Also developed is a computational algorithm, named linear speedup, to use whatever explicit linear constraints are present to speedup the search for a solution. The method is easy to understand and implement, yet very effec-tive in dealing with many integer programming problems, including knapsack problems, reliability optimiza-tion, and spare allocation problems. To see some application of the generalized algorithm, we notice that the branch-and-bound is the popular method to solve integer linear programming problems. But branch-and-bound cannot efficiently solve all integer linear programming problems. For example, De Loera et al. in their 2005 paper discuss some knapsack problems that CPLEX cannot solve in hours. We use our generalized algo-rithm to find a global or near global optimal solutions for those problems, in less than 100 seconds. The algo-rithm is based on function values only; it does not require continuity or differentiability of the problem func-tions. This allows its use on problems whose functions cannot be expressed in closed algebraic form. The re-liability and efficiency of the proposed algorithm has been demonstrated on some integer optimization prob-lems taken from the literature.
http://jiei.azad.ac.ir/article_511073_e31ca6284dea1119a2d5cda925fd2c30.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
39
50
Algebraic form
Function values
Generalized implicit enumeration
Integer programming
Linear speedup
M.S
Sabbagh
true
1
Assistant Professor, Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Assistant Professor, Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Assistant Professor, Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
M
Roshanjooy
true
2
M.Sc., Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
M.Sc., Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
M.Sc., Dep. of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
ORIGINAL_ARTICLE
Determining a common set of weights in DEA by solving a linear programming
In models of Data Envelopment Analysis (DEA), an optimal set of input and output weights is generally as-sumed to represent the assessed Decision Making Unit (DMU) in the best light in comparison to all the other DMUs. These sets of weights are, typically, different for each of the participating DMUs. Thus, it is important to find a Common Set of Weights (CSW) across the set of DMUs. In this paper, a procedure is suggested to find a CSW in DEA. In the proposed procedure by solving just one linear programming a CSW is achieved. To demonstrate the concept, a numerical example is solved
http://jiei.azad.ac.ir/article_511074_4524fd423e552e7ba8ec5394db639248.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
51
56
Data envelopment analysis
Weight Restriction
Common Set of Weights
Linear programming
S
Saati
true
1
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Tehran-North Branch, Tehran, Iran
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Tehran-North Branch, Tehran, Iran
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Tehran-North Branch, Tehran, Iran
AUTHOR
ORIGINAL_ARTICLE
Practical common weights MOLP approach for efficiency analysis
A characteristic of data envelopment analysis (DEA) is to allow individual decision making units (DMUs) to select the factor weights that are the most advantages for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes to use a multiple objective linear programming (MOLP) approach for generating common set of weights under the DEA framework. This is an advantage of the proposed approach against general approaches in the literature which are based on multiple objective nonlinear programming.
http://jiei.azad.ac.ir/article_511075_9072921be44b3fa7c552eba8d7ed24cd.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
57
63
MOLP
Maximin method
DEA
Efficiency
Ranking
Weight restrictions
A
Makui
true
1
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Assistant Professor, Dep. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
A
Alinezhad
true
2
Ph.D. Student, Dep. of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Ph.D. Student, Dep. of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Ph.D. Student, Dep. of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
AUTHOR
M
Zohrehbandian
true
3
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran
Assistant Professor, Dep. of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran
AUTHOR
ORIGINAL_ARTICLE
Technical Note: An opportunity cost maintenance scheduling framework for a fleet of ships: A case study
The conventional method towards deriving schedule for a fleet of ships to minimize cost alone has the short-coming of not addressing the problem of operation revenue losses associated with delays during maintenance at ships dockyards. In this paper, a preventive maintenance schedule for a fleet of ships that incorporates op-portunity cost is presented. The idea is to assign a penalty cost to all idle periods that the ship spends at the dockyard. A version of the scheduling problem was defined as a transportation model of minimizing mainte-nance costs. Fixed maintenance duration and dockyard capacity were the two constraints of the formulation. Relevant data from a shipping firm owing 8 ships and a dockyard in Lagos with a maintenance capacity of three ships per month were collected over a 24-month period. The maintenance cost function was then formu-lated with the parameters estimated and the transportation tableau set up. The considered eight ships arrived at the dockyard between the 1st and 20th month, and were expected to spend between 2 to 5 months for preven-tive maintenance. The optimal schedule of the cost function resulted in ships 1 to 8 being idle for 74 months. The results of the study showed that to reduce the cost and delays, decisions for scheduling preventive main-tenance of a fleet of ships should be based on opportunity cost.
http://jiei.azad.ac.ir/article_511076_3ab78c2d353e3875ab7d651c21929662.pdf
2008-06-01T11:23:20
2021-04-16T11:23:20
64
77
Preventive maintenance scheduling
Maintenance cost
Opportunity cost
Fleet of ships scheduling
O.E
Charles-Owaba
true
1
Reader, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Reader, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Reader, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
AUTHOR
A.E
Oluleye
true
2
Professor, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Professor, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Professor, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
AUTHOR
F.A
Oyawale
true
3
Lecturer, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Lecturer, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
Lecturer, Dep. of Industrial and Production Engineering, University of Ibadan, Nigeria
AUTHOR
S.K
Oke
true
4
Lecturer, Dep. of Mechanical Engineering, University of Lagos, Nigeria
Lecturer, Dep. of Mechanical Engineering, University of Lagos, Nigeria
Lecturer, Dep. of Mechanical Engineering, University of Lagos, Nigeria
LEAD_AUTHOR