2020-09-26T11:18:59Z
http://www.jise.ir/?_action=export&rf=summon&issue=645
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2011
4
4
An Efficient Algorithm to Solve Utilization-based Model for Cellular Manufacturing Systems
Kaveh
Fallahalipour
Iraj
Mahdavi
Ramin
Shamsi
Mohammad Mahdi
Paydar
The design of cellular manufacturing system (CMS) involves many structural and operational issues. One of the important CMS design steps is the formation of part families and machine cells which is called cell formation. In this paper, we propose an efficient algorithm to solve a new mathematical model for cell formation in cellular manufacturing systems based on cell utilization concept. The proposed model is to minimize the number of voids in cells to achieve higher cell utilization. The proposed model is a non-linear model which cannot be optimally solved. Thus, a linearization approach is used and the linearized model is then solved by linear optimization software. Even after linearization, the large-sized problems are still difficult to solve, therefore, a Simulated Annealing method is developed. To verify the quality and efficiency of the SA algorithm, a number of test problems with different sizes are solved and the results are compared with solutions obtained by Lingo 8 in terms of objective function values and computational time.
Cell formation
mathematical model
Cell utilization
Simulated Annealing
2011
02
01
209
223
http://www.jise.ir/article_4033_ca9b73afbf94b12ae3d4c31024bc5dc1.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2011
4
4
A Comprehensive Fuzzy Multiobjective Supplier Selection Model under Price Brakes and Using Interval Comparison Matrices
Mehdi
Seifbarghy
Ali
Pourebrahim Gilkalayeh
Mehran
Alidoost
The research on supplier selection is abundant and the works usually only consider the critical success factors in the buyer–supplier relationship. However, the negative aspects of the buyer–supplier relationship must also be considered simultaneously. In this paper we propose a comprehensive model for ranking an arbitrary number of suppliers, selecting a number of them and allocating a quota of an order to them considering three objective functions: minimizing the net cost, minimizing the net rejected items and minimizing the net late deliveries. The two-stage logarithmic goal programming method for generating weights from interval comparison matrices (Wang et al. 2005) is used for ranking and selecting the suppliers. It is assumed that the suppliers give price discounts. A fuzzy multiobjective model is formulated in such a way as to consider imprecision of information. A numerical example is given to explain how the model is applied.
Supplier selection
Interval Comparison Matrices
Fuzzy Multiobjective Model
Price Discounts
Supply chain
2011
02
01
224
244
http://www.jise.ir/article_4034_4a5a96a7db70d9782b94e5ed464c4d3e.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2011
4
4
Development of a Genetic Algorithm for Advertising Time Allocation Problems
Reza
Alaei
Farhad
Ghassemi-Tari
Commercial advertising is the main source of income for TV channels and allocation of advertising time slots for maximizing broadcasting revenues is the major problem faced by TV channel planners. In this paper, the problem of scheduling advertisements on prime-time of a TV channel is considered. The problem is formulated as a multi-unit combinatorial auction based mathematical model. This is an efficient mechanism for allocating the advertising time to advertisers in which the revenue of TV channel is maximized. However, still this problem is categorized as a NP-Complete problem. Therefore, a steady-state genetic algorithm is developed for finding a good or probably near-optimal solution, and is evaluated through a set of test problems for its robustness. Computational results reveal that the proposed algorithm is capable of obtaining high-quality solutions for the randomly generated real-sized test problems.
Combinatorial Optimization
TV Advertising Allocation Problem
Combinatorial Auctions
Genetic Algorithms
2011
02
01
245
255
http://www.jise.ir/article_4035_449d296da1de8b504edd056ae34ecd08.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2011
4
4
A New Acceptance Sampling Plan Based on Cumulative Sums of Conforming Run-Lengths
Mohammad Saber
Fallah Nezhad
Seyed Taghi
Akhavan Niaki
Mohammad Hossein
Abooie
In this article, a novel acceptance-sampling plan is proposed to decide whether to accept or reject a receiving batch of items. In this plan, the items in the receiving batch are inspected until a nonconforming item is found. When the sum of two consecutive values of the number of conforming items between two successive nonconforming items falls underneath of a lower control threshold, the batch is rejected. If this number falls above an upper control threshold, the batch is accepted, and if it falls within the upper and the lower thresholds then the process of inspecting items continues. The aim is to determine proper threshold values and a Markovian approach is used in this regard. The model can be applied in group- acceptance sampling plans, where simultaneous testing is not possible. A numerical example along a comparison study are presented to illustrate the applicability of the proposed methodology and to evaluate its performances in real-world quality control environments.
Acceptance Sampling
Quality Control
Inspection
Markov process
2011
02
01
256
264
http://www.jise.ir/article_4036_1f15901e89251874e58963f75bfdf1f4.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2011
4
4
Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Mehdi
Khashei
Mehdi
Bijari
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid models for time series forecasting. Several researches in the literature have been shown that these models can outperform single models. In this paper, the predictive capabilities of three different models in which the autoregressive integrated moving average (ARIMA) as linear model is combined to the multilayer perceptron (MLP) as nonlinear model, are compared together for time series forecasting. These models are including the Zhang’s hybrid ANNs/ARIMA, artificial neural network (p,d,q), and generalized hybrid ANNs/ARIMA models. The empirical results with three well-known real data sets indicate that all of these methodologies can be effective ways to improve forecasting accuracy achieved by either of components used separately. However, the generalized hybrid ANNs/ARIMA model is more accurate and performs significantly better than other aforementioned models.
Artificial neural networks (ANNs)
Auto-Regressive Integrated Moving Average (ARIMA)
Time series forecasting
Hybrid linear/nonlinear models
2011
02
01
265
285
http://www.jise.ir/article_4037_aa9ad9380cac2203195414d60c0b2da0.pdf