Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Investigation into skill leveled operators in a multi-period cellular manufacturing system with the existence of multi-functional machines
3219
3232
EN
M.
Rafiee
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
rafiee@sharif.edu
A.
Mohamaditalab
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
10.24200/sci.2019.21513
Many works published in the area of cellular manufacturing system are based on the assumption that machines are reliable in the whole production horizon without any break down. As such assumptions often are not realistic, to contribute to closing this gap to reality, the model has been modified by additionally including machine reliability, alternative process routings and workforce assignment in a dynamic environment. In this research to integrate this aspects, the modified problem has been defined and formulated and an extended mixed integer multi-period mathematical model has been proposed.<br />In order to evaluate the effectiveness and capability of the extended model, some hypothetical numerical instances are generated and computational experiment are carried out using Gams optimization package. Experimental results demonstrate that the demand value can affect the machine breakdown rate, and a machine with a minimum breakdown rate is implemented more often than others. Moreover, the observed trade-off between the workforce-related costs and the cell-formation costs indicates that workforce-related issues have a significant impact on the total efficiency of the system. The proposed model can be implemented in medium- and large-scale manufacturing companies.
multi-period cellular manufacturing system,machine reliability,workforce learning-forgetting effect,alternative process routing
http://scientiairanica.sharif.edu/article_21513.html
http://scientiairanica.sharif.edu/article_21513_0cb8b3180eb6e874de511ae629ccd74f.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Integration of machine learning techniques and control charts in multivariate processes
3233
3241
EN
D.
Demircioglu Diren
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
ddemircioglu@sakarya.edu.tr
S.
Boran
0000-0002-0532-937X
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
boran@sakarya.edu.tr
I.
Cil
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
icil@sakarya.edu.tr
10.24200/sci.2019.50377.1667
Using multivariate control chart instead of establishing univariate control chart for all variables in processes provides time and labor advantage. In addition, it is considered in the relations between variables. However, the statistical calculation of the measured values of all variables is seen as a single value in the control chart. Therefore, it is necessary to determine which variable(s) is the cause of the out of control signal. Effective corrective measures can only be developed when the causes of the fault(s) are determined correctly. The aim of the study is to determine the machine learning techniques that will accurately estimate the type of fault. With the Hotelling T2 chart, out of control signals are identified and the types of faults affected by the variables are defined. Various machine learning techniques are used to compare classification performances. The developed model was applied in the evaluation of the paint quality in a painting process. ANN was determined as the most successful techniques according to performance criteria. The novelty of the study is to classify the fault according to the types of faults, not the variables. Defining the faults according to its types will enable to take effective corrective actions quickly.
Multivariate Control Chart,Naive Bayes - Kernel,K - Nearest Neighbor,Decision Tree,artificial neural network,Multi-Layer Perpectron,Deep Learning
http://scientiairanica.sharif.edu/article_21503.html
http://scientiairanica.sharif.edu/article_21503_2df725c24f3b36bfb07bc4309a69a5af.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Weight determination and ranking priority in interval group MCDM
3242
3252
EN
S.
Saffarzadeh
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
s.saffarzadeh@gmail.com
A.
Hadi-Vencheh
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
abdh12345@yahoo.com
A.
Jamshidi
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
ali.jamshidi@khuisf.ac.ir
10.24200/sci.2019.51133.2022
In this study, we propose a method to determine the weight of decision makers (DMs) in group multiple criteria decision making (GMCDM) problems with interval data .Here, we obtain an interval weight for each DM and the relative closeness of each decision from the negative ideal solution (NIS) and the positive ideal solution (PIS) is then computed. In the proposed method, after weighting the decision matrix of each DM, the alternatives are ranked using interval arithmetic. A comparative example together with a real world problem on air quality assessment is given to illustrate our method. Our findings show that the proposed approach is a suitable tool to solve GMCDM problems.
Decision Analysis,GMCDM,TOPSIS,interval matrix,weight of criteria
http://scientiairanica.sharif.edu/article_21403.html
http://scientiairanica.sharif.edu/article_21403_5ff5619396a91a08759ddbb2449ee83f.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Extended economic production quantity models with preventive maintenance
3253
3264
EN
H.
Mokhtari
0000-0002-5297-5841
Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, P.O. Box 8731753153, Iran
mokhtari_ie@kashanu.ac.ir
J.
Asadkhani
Department of Management and Entrepreneurship, Faculty of Humanities, University of Kashan, Kashan, P.O. Box 1471835191,
Iran
javad.asadkhani@gmail.com
10.24200/sci.2019.51199.2055
This paper generalizes the standard economic production quantity (EPQ) model in process manufacturing industry by incorporating regular preventive maintenance (PM) activities into classic EPQ model. The PM program improves the condition of the production to an acceptable level, and avoids potential stoppages and disruptions, hence, it is a vital task in every production process. However, the standard EPQ model does not consider PM activities and then is not applicable to real-world situations. We consider manufacturer which produces a product under EPQ setting with a defective production process, in which every production cycle involves a number of sub-production cycles. Two models are proposed, based on the disposal time of defective items, to determine optimal number of sub-production cycles. In model I, the disposal of defective items is performed once per cycle at the end of each production cycle, while in model II, the disposal of defective items is performed multiple times per cycle, at the end of each sub-production cycle. The total cost functions are derived for each model separately, and then simple solution algorithms are designed. A numerical example is presented and discussed to evaluate proposed models. The results illustrate that model II is more cost effective than model I.
Preventive maintenance,Production-Inventory System,Defective Process,Manufacturing Planning,Defective items
http://scientiairanica.sharif.edu/article_21394.html
http://scientiairanica.sharif.edu/article_21394_3f60af9af08b638edc11e412d69af2dc.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Investigating the effect of learning on setup cost in imperfect production systems using two-way inspection plan for rework under screening constraints
3265
3288
EN
A.
Kishore
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
kishore.aakanksha@gmail.com
P.
Gautam
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
prerna3080@gmail.com
A.
Khanna
0000000160304071
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
dr.aditikhanna.or@gmail.com
C.K.
Jaggi
0000-0001-6179-8376
Department of Operational Research,
Faculty of Mathematical Sciences,
University of Delhi,
India
ckjaggi@yahoo.com
10.24200/sci.2019.51333.2120
In the modern industrial environment, there is a continuous need for the advancement and improvement of the organization’s operations. Learning is an inherent property which is time-dependent and comes with experience. In view of this, the present framework considers the process of learning for an imperfect production system which aids in reducing the setup cost with the level of maturity gained, hence, providing positive results for the organization. Because of machine disturbances/ malfunctions, defectives are manufactured with a known probability density function. To satisfy the demand with good products only, the manufacturer invests in a two-way inspection process with multiple screening constraints. The first inspection misclassifies some of the items and delivers inaccuracies, viz., Type-I and Type–II. The loss due to inspection at the first stage is managed efficiently through a second inspection which is presumed to be free from errors. The study mutually optimizes the production and backordering quantities in order to maximize the expected total profit per unit time. Numerical analysis and detailed sensitivity analysis is carried out to validate the hypothesis and further cater to some valuable implications.
Inventory,Imperfect-production,Two-way inspection,Sales-returns,Learning,Screening-constraints
http://scientiairanica.sharif.edu/article_21444.html
http://scientiairanica.sharif.edu/article_21444_36f4e5aea20605232e6564d458757fcb.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
A game theoretic approach to coordination of pricing, ordering, and co-op advertising in supply chains with stochastic demand
3289
3304
EN
H.
Ghashghaei
Department of Industrial Engineering, Electronic Branch, Islamic Azad University, Tehran, P.O. Box 14335-499, Iran.
hghashghae@gmail.com
M.
Mozafari
Department of Industrial Engineering, Electronic Branch, Islamic Azad University, Tehran, P.O. Box 14335-499, Iran.
m_mozafari@iauec.ac.ir
10.24200/sci.2019.51588.2264
This paper combines the newsboy problem with the cooperative advertisement problem in the presence of uncertain demand which depends on retail price as well as both local and national advertising expenditures to coordinate pricing, ordering, and advertising decisions in a manufacturer-retailer supply chain. A game theoretic approach is adopted to determine the equilibrium values of the decisions. Three different game scenarios based on the newsboy problem model are developed and analyzed: 1) Stackelberg manufacturer game in which manufacturer as the dominant power plays the role of leader in the market and the follower retailer makes its own best decisions after observing the leader decisions, 2) Nash game wherein both manufacturer and retailer have equal power in the market and make their decisions simultaneously to find their own best strategies and 3) centralized scenario in which retailer and manufacturer make the best decisions by information sharing and joint cooperation. The equilibrium decisions are obtained exactly in the three scenarios. Some corollaries are also presented and theoretically proved to show the relationships among the variables in centralized vs. decentralized supply chain. Finally, some numerical examples are randomly generated and a sensitivity analysis is carried out to show capabilities of the proposed models.
supply chain coordination,newsboy problem,Pricing,cooperative advertising,ordering,uncertainty,Game theory
http://scientiairanica.sharif.edu/article_21404.html
http://scientiairanica.sharif.edu/article_21404_4c3778f3329a454a32efcf6892e44959.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Bi-objective optimization of non-periodic preventive maintenance strategy by considering time value of money
3305
3321
EN
A.
Salmasnia
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
a.salmasnia@qom.ac.ir
A.
Shahidian
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
shahidian.ali@gmail.com
M.
Seivandian
Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
mesbah.sivandian@ut.ac.ir
B.
Abdzadeh
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
behnamabdzadeh@gmail.com
10.24200/sci.2019.51791.2365
Recently, design of preventive maintenance (PM) policies during the warranty period has attracted the attention of researchers. The methods mainly design warranty servicing strategies in a way that reduce the cost imposed on the manufacturer without considering the impact of customer dissatisfaction. While dissatisfaction with a product is an important issue which may result in the loss of potential buyers and switching existing buyers to competitors. Therefore, this study develops a bi-objective model which simultaneously minimizes the manufacturer and the buyer cost under a Non-homogeneous Poisson Process framework. Also, a non-periodic preventive maintenance strategy is presented in which PM actions are performed at discrete time instants in a way that the expected number of failures remains a constant value over all PM intervals. Furthermore, it is a known fact that the value of money reduces over time due to different reasons and has a significant impact on long-term contracts. Since PMs and repairs are conducted at different times, the time value of money is considered to estimate the cost more accurately. A comparative study is conducted to support this claim that the presented non-periodic reliability-based PM policy has a better performance in comparison with a periodic PM policy.
warranty,non-periodic preventive maintenance,time value of money,bi-objective optimization
http://scientiairanica.sharif.edu/article_21384.html
http://scientiairanica.sharif.edu/article_21384_0bb84f78cffebf6a3477529b80d578c9.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
Technology valuation of NTBFs in the field of cleaner production in terms of investors' exibility and uncertainty in public policy
3322
3337
EN
K.
Fattahi
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
komeil_fattahi@yahoo.com
A.
Bonyadi Naeini
0000-0003-3119-551X
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
bonyadi@iust.ac.ir
S.J.
Sadjadi
0000-0002-5151-8315
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
sjsadjadi@iust.ac.ir
10.24200/sci.2019.52078.2523
Technology valuation, especially in the early stages of new technology-based firms (NTBFs) growth is one of the most critical challenges, which most often hinders the investor and entrepreneur's deals during the venture capital (VC) financing process. It is clear that uncertainties arising from the likelihood of implementing public policies could significantly affect the volatility of NTBFs cash flows in the field of cleaner production. Commonly, these kinds of technologies require public supportive policies for achieving success. Consequently, their technology valuation is more challenging and traditional valuation methods are not suitable anymore because of the definitive assumption of cash flow and ignoring the investors’ flexibilities and uncertainties. Therefore, this paper proposes a method by introducing a framework based on the decision tree and the real options analysis which is tailored to meet the technology valuation of such firms during all stages of their growth. Furthermore, unlike previous papers that have utilized the compound options, option to choose has been used to apply investors’ flexibilities. Then, the proposed framework is supported by a case study, which has been conducted to verify and validate it. Finally, the conclusion section discusses the contributions and limitations of the study and provides directions for future research.
Technology valuation,New technology-based firms (NTBFs),Real options analysis (ROA),Option to choose,Decision tree analysis (DTA),Cleaner production
http://scientiairanica.sharif.edu/article_21365.html
http://scientiairanica.sharif.edu/article_21365_f5efe19dd6c26590990426ae6751719a.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
27
6
2020
12
01
An evaluation of inventory systems via an evidence theory for deteriorating items under uncertain conditions and advanced payment
3338
3351
EN
M.
Soleimani Amiri
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
mth.soleimany@gmail.com
A.
Mirzazadeh
0000-0003-0546-849x
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Ira
ind.eng.res@gmail.com
M.M.
Seyed Esfahani
Department of Industrial Engineering and Management Systems, Amirkabir University, Tehran, Iran.
msesfahani@aut.ac.ir
Sh.
S.
Ghasemi
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
shiva.s.ghasemi@gmail.com
10.24200/sci.2019.52116.2543
The inventory model for deteriorating items, which is developed by The Evidential Reasoning Algorithm (ERA) and the imprecise inventory costs, is one of the most important factors in complex systems which plays a vital role in Payment. The ERA is able to strengthen the precision of the model and give the perfect interval-valued utility. In this model, during lead-time and reorder level two different cases can be happened which the mathematical model turns into an imposed nonlinear mixed integer problem with interval objective for each case. Placement of an order, which is overlooked by many researchers till now, is normally connected with the advance payment (AP) in business. Specifying the optimal profit and the optimal number of cycles in the finite time horizon and lot-sizing in each cycle, are our goals so. In order to solve this model, we apply the real-coded genetic algorithm (RCGA) with ranking selection. By the model, we represent some numerical examples and also a sensitivity analysis with the variation of different inventory parameters.
Evidence theory,Inventory,advance payment,Deterioration,Genetic Algorithm,interval order relations
http://scientiairanica.sharif.edu/article_21478.html
http://scientiairanica.sharif.edu/article_21478_b87332cdc38fb15baa1573b7e329e32d.pdf