ORIGINAL_ARTICLE
A New Robust Bootstrap Algorithm for the Assessment of Common Set of Weights in Performance Analysis
The performance of the units is defined as the ratio of the weighted sum of outputs to the weighted sum of inputs. These weights can be determined by data envelopment analysis (DEA) models. The inputs and outputs of the related (Decision Making Unit) DMU are assessed by a set of the weights obtained via DEA for each DMU. In addition, the weights are not generally common, but rather, they are very close to zero or they are even equal to zero. This means that some major criteria will not be considered. Another problem is the similarity of the efficiency scores of efficient DMUs. However, this is not the case in reality, and the performance of the DMUs should be completely ranked. Using common weights can solve these problems completely during measuring the performance of DMUs. There are some articles in the literature to determine common weight sets (CSWs), but none of them takes into account the bootstrap approach. This paper introduces a novel, empirical and robust algorithm based on bootstrapping technique to find CSWs.
https://ijms.ut.ac.ir/article_69590_92ff1e5f5d8c86f5140ae307d05c2df0.pdf
2019-04-01
175
189
10.22059/ijms.2019.254137.673058
Data Envelopment Analysis
Common set of weights
Performance evaluations
Bootstrapping
İhsan
Alp
ihsanalp@gazi.edu.tr
1
Department of Statistics, Faculty of Sciences, Gazi University, Ankara, Turkey
AUTHOR
Volkan Soner
Ozsoy
volkansoner@gazi.edu.tr
2
Department of Statistics, Faculty of Sciences, Gazi University, Ankara, Turkey
LEAD_AUTHOR
Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European journal of operational research, 140(2), 249-265.
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39
ORIGINAL_ARTICLE
A Competency-based Typology of Technology Entrepreneurs: A Systematic Review of the Empirical Studies
Due to the importance of technology entrepreneurs’ competencies in the creation and development of technological businesses, a distinct stream of research has been dedicated to this subject. However, given the nature of suchlike studies, it is difficult to reach a common understanding of the competencies. In fact, there is a need to provide some systematization to achieve advancements in the field. A review of 87 articles indexed on Scopus about technology entrepreneurs indicates that competencies of technology entrepreneurs can be classified under three groups of technological competencies, entrepreneurial competencies, and managerial competencies. This classification is used for two purposes, namely to categorize the technology entrepreneurs based on their competencies and to recommend new venues of research to study technology entrepreneurs. This paper can help technology entrepreneurs develop their competencies. It will also be useful in identifying entrepreneurs, and in training and developing the competency of academic entrepreneurs, technology incubators, and accelerators.
https://ijms.ut.ac.ir/article_70121_0bb52e377b510987965866875a85f7d0.pdf
2019-04-01
191
211
10.22059/ijms.2019.241656.672822
Technology entrepreneur
Start-up
Systematic review
Competency approach
Typology
Mohammad Sadegh
Baradaran
baradaran_64@ut.ac.ir
1
Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Jahangir
Yadollahi Farsi
jfarsi@ut.ac.ir
2
Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
AUTHOR
Seyed Reza
Hejazi
rehejazi@ut.ac.ir
3
Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
AUTHOR
Morteza
Akbari
mortezaakbari@ut.ac.ir
4
Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
AUTHOR
Agogué, M., Lundqvist, M., & Middleton, K. W. (2015). Mindful Deviation through Combining Causation and Effectuation: A Design Theory-Based Study of Technology Entrepreneurship. Creativity and Innovation Management, 24(4), 629-644.
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72
ORIGINAL_ARTICLE
A Novel Approach to Evaluate the Road Safety Index: A Case Study in the Roads of East Azerbaijan Province in Iran
Road safety index is an important indicator that has been recently introduced as a useful tool to measure the quality of life in many countries and cities. Road safety index is a complex index and it has at least three main components, including road user behavior, vehicle safety, and road infrastructure effects. Many researchers have selected studying road performance from road safety index perspective due to its feasibility and applicability. To calculate the road safety index, a novel approach was proposed using data envelopment analysis method. In this paper, the selected road safety indicators are classified into two groups, namely the desirable and undesirable indicators. The new approach was applied for a case study in the roads of East Azerbaijan Province in Iran. Inefficient roads were recognized applying the proposed method, and strategies were suggested to improve the efficiency of these roads.
https://ijms.ut.ac.ir/article_70122_ce897c3a85b3d3fde2409ffe4b924ccd.pdf
2019-04-01
213
233
10.22059/ijms.2019.271035.673431
Road Safety Index
Data Envelopment Analysis
Road safety performance assessment
Undesirable indices
Kazem
Teimourzadeh
k.teimour@gmail.com
1
Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran
AUTHOR
Jafar
Pourmahmoud
pourmahmoud@azaruniv.ac.ir
2
Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran
LEAD_AUTHOR
Sohrab
Kordrostami
kordrostami@liau.ac.ir
3
Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran
AUTHOR
Al-Haji, G. (2007). Road safety development index: Theory, philosophy and practice. Doctoral dissertation, Linköping University Electronic Press.
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3
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4
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7
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8
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11
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27
Schlögl, M., & Stütz, R. (2017). Methodological considerations with data uncertainty in road safety analysis. Accident Analysis & Prevention, In Press, https://doi.org/10.1016/j.aap.2017.02.001.
28
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.
29
Shen, Y., Hermans, E., Bao, Q., Brijs, T., & Wets, G. (2013). Road safety development in Europe: A decade of changes (2001–2010). Accident Analysis & Prevention, 60, 85-94.
30
Shen, Y., Hermans, E., Bao, Q., Brijs, T., Wets, G., & Wang, W. (2015). Inter-national
31
A Novel Approach to Evaluate the Road Safety Index: A Case Study in the Roads … 59
32
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33
Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. (2012). Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accident Analysis & Prevention, 48, 430-441.
34
Shen, Y., Hermans, E., Ruan, D., Wets, G., Brijs, T., & Vanhoof, K. (2011). A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation. Expert systems with applications, 38(12), 15262-15272.
35
Tatari, O., Egilmez, G., & Kurmapu, D. (2016). Socio-eco-efficiency analysis of highways: A data envelopment analysis. Journal of Civil Engineering and Management, 22(6), 747-757.
36
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38
Xie, F., Gladhill, K., Dixon, K., & Monsere, C. (2011). Calibration of highway safety manual predictive models for Oregon state highways. Journal of the Transportation Research Board, 2241(1), 19-28.
39
ORIGINAL_ARTICLE
A Simulation-Optimization Model For Capacity Coordination In Make To Stock/Make To Order Production Environments
Capacity coordination, as the tactical level of hierarchical production planning in hybrid MTS/MTO systems, includes numerous important decisions. In this paper, two of these decisions i.e. finding the best strategy for the acceptance/rejection of incoming orders and determining orders’ due dates – are investigated. Also a simulation model is proposed to evaluate the efficiency of the presented mixed integer model. Finally, an industrial case study is considered in a food processing plant to evaluate the proposed framework and conduct suitable sensitivity analysis.
https://ijms.ut.ac.ir/article_69922_14088e4c7fba1c0b89f7aaf418a56306.pdf
2019-04-01
235
253
10.22059/ijms.2019.255382.673087
Production Planning
Make to stock
Make to order
Order Acceptance
Simulation Optimization
Helia
Yousefnejad
helia.yousefnejad@gmail.com
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Masoud
Rabbani
mrabani@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Neda
Manavizadeh
n.manavi@khatam.ac.ir
3
Department of Industrial Engineering, Khatam University, Tehran, Iran
AUTHOR
Adan, I. J., & Van der Wal, J. (1998). Combining make to order and make to stock. Operations-Research-Spektrum, 20(2), 73-81.
1
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Chang, S. H., Pai, P. F., Yuan, K. J., Wang, B. C., & Li, R. K. (2003). Heuristic PAC model for hybrid MTO and MTS production environment. International Journal of Production Economics, 85(3), 347-358.
3
Dellaert, N. P., & Melo, M. T. (1996). Production strategies for a stochastic lot-sizing problem with constant capacity. European Journal of Operational Research, 92(2), 281-301.
4
Easton, F. F., & Moodie, D. R. (1999). Pricing and lead time decisions for make-to-order firms with contingent orders. European Journal of operational research, 116(2), 305-318.
5
Ebadian, M., Rabbani, M., Torabi, S. A., & Jolai, F. (2009). Hierarchical production planning and scheduling in make-to-order environments: reaching short and reliable delivery dates. International Journal of Production Research, 47(20), 5761-5789.
6
Ghalehkhondabi, I., Ardjmand, E., & Weckman, G. (2017). Integrated decision making model for pricing and locating the customer order decoupling point of a newsvendor supply chain. Opsearch, 54(2), 417-439.
7
Ghalehkhondabi, I., & Suer, G. (2018). Production line performance analysis within a MTS/MTO manufacturing framework: a queuing theory approach. Production, 28 (0). http://dx.doi.org/10.1590/0103-6513.20180024
8
Gharehgozli, A. H., Rabbani, M., Zaerpour, N., & Razmi, J. (2008). A comprehensive decision-making structure for acceptance/rejection of incoming orders in make-to-order environments. The International Journal of Advanced Manufacturing Technology, 39(9-10), 1016-1032.
9
Halawa, F., Lee, I. G., Shen, W., Khan, M. E., & Nagarur, N. (2017). The Implementation of Hybrid MTSMTO as a Promoter to Lean-Agile: A Simulation Case Study for Miba Sinter Slovakia. In IIE Annual Conference. Proceedings (pp. 1006-1011). Institute of Industrial and Systems Engineers (IISE).
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14
Kalantari, M., Rabbani, M., & Ebadian, M. (2011). A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems. Applied Mathematical Modelling, 35(3), 1363-1377.
15
Kingsman, B., & Hendry, L. (2002). The relative contributions of input and output controls on the performance of a workload control system in make-to-order companies. Production Planning & Control, 13(7), 579-590.
16
Makui, A., Heydari, M., Aazami, A., & Dehghani, E. (2016). Accelerating Benders decomposition approach for robust aggregate production planning of products with a very limited expiration date. Computers & Industrial Engineering, 100, 34-51.
17
Manavizadeh, N., Goodarzi, A. H., Rabbani, M., & Jolai, F. (2013). Order acceptance/rejection policies in determining the sequence in mixed model assembly lines. Applied Mathematical Modelling, 37(4), 2531-2551.
18
Mu, Y. (2001). Design of hybrid Make-to-Stock (MTS)-Make-to-Order (MTO) manufacturing system (Doctoral dissertation, M. Sc. Thesis, The University of Minnesota).
19
Olhager, J. (2003). Strategic positioning of the order penetration point. International Journal of Production Economics, 85(3), 319-329.
20
Rabbani, M., Yousefnejad, H., & Rafiei, H. (2014). Presenting a new approach toward locating optimal decoupling point in supply chains. International Journal of Research in Industrial Engineering, 3(1), 49.
21
Rabbani, M., Haghighi, S. M., Farrokhi-Asl, H., & Manavizadeh, N. (2017). Capacity coordination in hybrid make-to-stock/make-to-order contexts using an enhanced multi-stage model. Brazilian Journal of Operations & Production Management, 14(3), 396-413.
22
Rafiei, H., & Rabbani, M. (2011). Order partitioning and order penetration point location in hybrid make-to-stock/make-to-order production contexts. Computers & Industrial Engineering, 61(3), 550-560.
23
Rafiei, H., & Rabbani, M. (2012). Capacity coordination in hybrid make-to-stock/make-to-order production environments. International Journal of Production Research, 50(3), 773-789.
24
Soman, C. A., Van Donk, D. P., & Gaalman, G. (2004). Combined make-to-order and make-to-stock in a food production system. International Journal of Production Economics, 90(2), 223-235.
25
Soman, C. A., van Donk, D. P., & Gaalman, G. (2006). Comparison of dynamic scheduling policies for hybrid make-to-order and make-to-stock production systems with stochastic demand. International Journal of Production Economics, 104(2), 441-453.
26
Van Dam, P., Gaalman, G. J., & Sierksma, G. (1998). Designing scheduling systems for packaging in process industries: A tobacco company case. International journal of production economics, 56, 649-659.
27
Van Donk, D. P. (2001). Make to stock or make to order: The decoupling point in the food processing industries. International Journal of Production Economics, 69(3), 297-306.
28
Williams, T. M. (1984). Special products and uncertainty in production/inventory systems. European Journal of Operational Research, 15(1), 46-54.
29
Zaerpour, N., Rabbani, M., Gharehgozli, A. H., & Tavakkoli-Moghaddam, R. (2008). Make-to-order or make-to-stock decision by a novel hybrid approach, Advanced Engineering Informatics, 22(2), 186-201.
30
Zaerpour, N., Rabbani, M., Gharehgozli, A. H., & Tavakkoli-Moghaddam, R. (2009). A comprehensive decision making structure for partitioning of make-to-order, make-to-stock and hybrid products. Soft Computing, 13(11), 1035-1054.
31
ORIGINAL_ARTICLE
A Unique Mathematical Framework for Optimizing Patient Satisfaction in Emergency Departments
In healthcare systems, emergency departments (EDs) are the most vitalelements, in that they provide critical and immediate healthcare services to thepatients 24 hours a day. Patient satisfaction is a crucial concept and a practicaltool for evaluating the performance of the EDs. This study presents a uniqueframework for the performance optimization of an emergency department in abig general hospital in Iran based on the standard patient satisfaction indicators.Standard questionnaire is designed and used in a large and busy emergencydepartment. The reliability and validity of the questionnaires are obtained byCronbach’s alpha and parametric and non-parametric analysis of variance(ANOVA), respectively. Afterwards, the most efficient data envelopmentanalysis (DEA) model is selected and employed to assess the performance of theemergency department based on the selected indicators. Results show thatcertain indicators such as quality of equipment, performance of physicians andtreatment time have the greatest impact (weight) on overall patient satisfaction.The framework of this study is a practical approach for all types of emergencydepartments in the process of the improvement and optimization of patientsatisfaction
https://ijms.ut.ac.ir/article_70833_becef4f8225c4250e6d17ece3b360785.pdf
2019-04-01
255
279
10.22059/ijms.2019.263086.673239
Emergency department
Patient satisfaction
Data Envelopment Analysis (DEA)
Analysis of variance (ANOVA)
Sensitivity analysis
Hamidreza
Farzaneh Kholghabad
hamidreza.farzane@ut.ac.ir
1
Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
Negin
Alisoltani
n.alisoltani@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Salman
Nazari-Shirkouhi
snnazari@ut.ac.ir
3
Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran
LEAD_AUTHOR
Mohammadali
Azadeh
aazadeh@ut.ac.ir
4
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Saeed
Moosakhani
s.moosakhani@ut.ac.ir
5
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Abo-Hamad, W., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research, 224(1), 154-166.
1
Al-Refaie, A., Fouad, R. H., Li, M.-H., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59-72.
2
Athanassopoulos, A., & Gounaris, C. (2001). Assessing the technical and allocative efficiency of hospital operations in Greece and its resource allocation implications. European Journal of Operational Research, 133(2), 416-431.
3
Azadeh, A., Ghaderi, S., Anvari, M., Izadbakhsh, H., Rezaee, M. J., & Raoofi, Z. (2013). An integrated decision support system for performance assessment and optimization of decision-making units. The International Journal of Advanced Manufacturing Technology, 66(5-8), 1031-1045.
4
Azadeh, A., Saberi, M., Moghaddam, R. T., & Javanmardi, L. (2011). An integrated data envelopment analysis–artificial neural network–rough set algorithm for assessment of personnel efficiency. Expert Systems with Applications, 38(3), 1364-1373.
5
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
6
Boudreaux, E. D., d'Autremont, S., Wood, K., & Jones, G. N. (2004). Predictors of emergency department patient satisfaction: stability over 17 months. Academic Emergency Medicine, 11(1), 51-58.
7
Boudreaux, E. D., & O'Hea, E. L. (2004). Patient satisfaction in the emergency department: A review of the literature and implications for practice. The Journal of emergency medicine, 26(1), 13-26.
8
Charnes, A., Cooper, W. W., & Rhodes, E. (1979). Measuring the efficiency of decision-making units. European Journal of Operational Research, 3(4), 339-338.
9
Draper, M., Cohen, P., & Buchan, H. (2001). Seeking consumer views: What use are results of hospital patient satisfaction surveys? International journal for quality in health care, 13(6), 463-468.
10
Fiallos, J., Patrick, J., Michalowski, W., & Farion, K. (2017). Using data envelopment analysis for assessing the performance of pediatric emergency department physicians. Health care management science, 20(1), 129-140.
11
Gibbons, C., Singh, S., Gibbons, B., Clark, C., Torres, J., Cheng, M. Y., . . . Armstrong, A. W. (2018). Using qualitative methods to understand factors contributing to patient satisfaction among dermatology patients: A systematic review. Journal of Dermatological Treatment, 29(3), 290-294.
12
Grosskopf, S., & Valdmanis, V. (1993). Evaluating hospital performance with case-mix-adjusted outputs. Medical Care, 31(6), 525-532.
13
Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European Journal of Industrial Engineering, 10(2), 196-223.
14
Gupta, D., Rodeghier, M., & Lis, C. G. (2013). Patient satisfaction with service quality in an oncology setting: Implications for prognosis in non-small cell lung cancer. International journal for quality in health care, 25(6), 696-703.
15
Halkos, G. E., & Tzeremes, N. G. (2011). A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures. Health policy, 103(1), 73-82.
16
Hall, M. F., & Press, I. (1996). Keys to patient satisfaction in the emergency department: Results of a multiple facility study. Journal of Healthcare Management, 41(4), 515.
17
Harrison, J. P., Coppola, M. N., & Wakefield, M. (2004). Efficiency of federal hospitals in the United States. Journal of Medical Systems, 28(5), 411-422.
18
Heiberger, R. M., & Neuwirth, E. (2009). R through Excel: A spreadsheet interface for statistics, data analysis, and graphics. New York: Springer, 323-330.
19
Hollingsworth, B. (2003). Non-parametric and parametric applications measuring efficiency in health care. Health care management science, 6(4), 203-218.
20
Hussey, P. S., De Vries, H., Romley, J., Wang, M. C., Chen, S. S., Shekelle, P. G., & McGlynn, E. A. (2009). A systematic review of health care efficiency measures. Health services research, 44(3), 784-805.
21
Kol, E., Arıkan, F., İlaslan, E., Akıncı, M. A., & Koçak, M. C. (2018). A quality indicator for the evaluation of nursing care: Determination of patient satisfaction and related factors at a university hospital in the Mediterranean Region in Turkey. Collegian, 25(1), 51-56.
22
Kounetas, K., & Papathanassopoulos, F. (2013). How efficient are Greek hospitals? A case study using a double bootstrap DEA approach. The European Journal of Health Economics, 14(6), 979-994.
23
Lertworasirikul, S., Fang, S.-C., Joines, J. A., & Nuttle, H. L. (2003). Fuzzy data envelopment analysis (DEA): A possibility approach. Fuzzy Sets and Systems, 139(2), 379-394.
24
Li, J., Wang, P., Kong, X., Liang, H., Zhang, X., & Shi, L. (2016). Patient satisfaction between primary care providers and hospitals: A cross-sectional survey in Jilin province, China. International journal for quality in health care, 28(3), 346-354.
25
Luscombe, R., & Kozan, E. (2016). Dynamic resource allocation to improve emergency department efficiency in real time. European Journal of Operational Research, 255(2), 593-603.
26
Mitropoulos, P., Mitropoulos, I., & Sissouras, A. (2013). Managing for efficiency in health care: The case of Greek public hospitals. The European Journal of Health Economics, 14(6), 929-938.
27
Nazari-Shirkouhi, S., & Keramati, A. (2017). Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm. Applied Mathematical Modelling, 50, 755-771.
28
Newcomb, P., Wilson, M., Baine, R., McCarthy, T., Penny, N., Nixon, C., & Orren, J. (2017). Influences on Patient Satisfaction Among Patients Who Use Emergency Departments Frequently for Pain-Related Complaints. Journal of Emergency Nursing, 43(6), 553-559.
29
O’Neill, L., Rauner, M., Heidenberger, K., & Kraus, M. (2008). A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Socio-Economic Planning Sciences, 42(3), 158-189.
30
Osman, I. H., Berbary, L. N., Sidani, Y., Al-Ayoubi, B., & Emrouznejad, A. (2011). Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit. Journal of Medical Systems, 35(5), 1039-1062.
31
Özpeynirci, Ö., & Köksalan, M. (2007). Performance evaluation using data envelopment analysis in the presence of time lags. Journal of Productivity Analysis, 27(3), 221-229.
32
Pink, G. H., Murray, M., & McKillop, I. (2003). Hospital efficiency and patient satisfaction. Health Services Management Research, 16(1), 24-38.
33
Popescu, C., Asandului, L., & Fatulescu, P. (2014). A Data Envelopment Analysis for Evaluating Romania’s Health System. Procedia-Social and Behavioral Sciences, 109, 1185-1189.
34
Schoenfelder, T., Klewer, J., & Kugler, J. (2011). Determinants of patient satisfaction: A study among 39 hospitals in an in-patient setting in Germany. International journal for quality in health care, 23(5), 503-509.
35
Sharma, S., & Sharma, S. (1996). Applied multivariate techniques.
36
Soares, A. M., & Farhangmehr, M. (2015). Understanding patient satisfaction in a hospital emergency department. International Review on Public and Nonprofit Marketing, 12(1), 1-15.
37
Taylor, D., Kennedy, M. P., Virtue, E., & McDonald, G. (2006). A multifaceted intervention improves patient satisfaction and perceptions of emergency department care. International journal for quality in health care, 18(3), 238-245.
38
Rezaie, K., Dalfard, V. M., Hatami-Shirkouhi, L., & Nazari-Shirkouhi, S. (2013). Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange. Neural Computing and Applications, 23(1), 1-17.
39
Valdmanis, V. (1992). Sensitivity analysis for DEA models: An empirical example using public vs. NFP hospitals. Journal of Public Economics, 48(2), 185-205.
40
Watson, W. T., Marshall, E. S., & Fosbinder, D. (1999). Elderly patients' perceptions of care in the emergency department. Journal of Emergency Nursing, 25(2), 88-92.
41
Welch, S. J. (2010). Twenty years of patient satisfaction research applied to the emergency department: A qualitative review. American Journal of Medical Quality, 25(1), 64-72.
42
Zavras, A. I., Tsakos, G., Economou, C., & Kyriopoulos, J. (2002). Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network. Journal of Medical Systems, 26(4), 285-292.
43
ORIGINAL_ARTICLE
A Genetic Algorithm Developed for a Supply Chain Scheduling Problem
This paper concentrates on the minimization of total tardiness and earliness of orders in an integrated production and transportation scheduling problem in a two-stage supply chain. Moreover, several constraints are also considered, including time windows due dates, and suppliers and vehicles availability times. After presenting the mathematical model of the problem, a developed version of GA called Time Travel to History (TTH) algorithm, inspired from the idea of traveling through history, is proposed to solve the problem. In order to validate the performance of the proposed algorithm, the results of TTH algorithm are compared with two other genetic algorithms in the literature. The comparison results show the better performance of the proposed algorithm. Moreover, the results of implementing the sensitivity analysis to the main parameters of the algorithm show the behavior of the objective functions when the parameters are changed.
https://ijms.ut.ac.ir/article_69461_41d014f5fa61e7d09c6c06ef85e5b80a.pdf
2019-04-01
281
306
10.22059/ijms.2019.254633.673069
Genetic algorithm
Meta-heuristic
Supply Chain
Scheduling
Logistic
Seyed Mohammad Reza
Taheri
smr.taheri@alum.semnan.ac.ir
1
Department of Industrial Engineering, University of Semnan, Semnan, Iran
AUTHOR
Mohammad Ali
Beheshtinia
beheshtinia@semnan.ac.ir
2
Department of Industrial Engineering, University of Semnan, Semnan, Iran
LEAD_AUTHOR
Beheshtinia, M. A., & Ghasemi, A. (2017). A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system. Engineering Optimization, 50(9), 1415-1433.
1
Beheshtinia, M. A., Ghasemi, A., & Farokhnia, M. (2017). Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company). Journal of Modelling in Management, 13(1), 27-49.
2
Borumand, A., & Beheshtinia, M. A. (2018). A developed genetic algorithm for solving the multi-objective supply chain scheduling problem. Kybernetes, 47(7), 1401-1419.
3
Chang, Y. C., & Lee, C. Y. (2004). Machine scheduling with job delivery coordination. European Journal of Operational Research, 158(2), 470-487.
4
Chang, Y.C., Chang, K.H., & Kang, T.C. (2015). Applied variable neighborhood search-based approach to solve two-stage supply chain scheduling problems. Journal of Testing and Evaluation, 44(3), 1337-1349
5
Fahimnia, B., Luong, L., & Marian, R. (2012). Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains. International Journal of Production Research, 50(1), 81-96.
6
Han, B., & Zhang, W. J. (2015). On-line Supply Chain Scheduling Problem with Capacity Limited Vehicles. IFAC-PapersOnLine, 48(3), 1539-1544.
7
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
8
Karaoğlan, İ., & Kesen, S. E. (2017). The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm. International Journal of Production Research, 55(2), 536-557.
9
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69-85.
10
Low, C., Chang, C. M., & Gao, B. Y. (2017). Integration of production scheduling and delivery in two echelon supply chain. International Journal of Systems Science: Operations & Logistics, 4(2), 122-134.
11
Moon, C., Lee, Y. H., Jeong, C. S., & Yun, Y. (2008). Integrated process planning and scheduling in a supply chain. Computers & Industrial Engineering, 54(4), 1048-1061.
12
Selvarajah, E., & Zhang, R. (2014). Supply chain scheduling at the manufacturer to minimize inventory holding and delivery costs. International Journal of Production Economics, 147, 117-124.
13
Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.
14
Xu, S., Liu, Y., & Chen, M. (2017). Optimisation of partial collaborative transportation scheduling in supply chain management with 3PL using ACO. Expert Systems with Applications, 71, 173-191.
15
Yeung, W. K., Choi, T. M., & Cheng, T. C. E. (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132(2), 223-229.
16
Yimer, A. D., & Demirli, K. (2010). A genetic approach to two-phase optimization of dynamic supply chain scheduling. Computers & Industrial Engineering, 58(3), 411-422.
17
Yin, P. Y., Lyu, S. R., & Chuang, Y. L. (2016). Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering. Engineering Applications of Artificial Intelligence, 52, 40-53.
18
Zegordi, S. H., & Beheshti Nia, M. A. (2009). Integrating production and transportation scheduling in a two-stage supply chain considering order assignment. The International Journal of Advanced Manufacturing Technology, 44(9), 928-939.
19
Zegordi, S. H., & BeheshtiNia, M. A. (2009). Integrating production andtransportation scheduling in a two-stage supply chain considering order assignment. The International Journal of Advanced Manufacturing Technology, 44(9-10), 928-939.
20
ORIGINAL_ARTICLE
Integrated Process Planning and Active Scheduling in a Supply Chain-A Learnable Architecture Approach
Through the lens of supply chain management, integrating process planning decisions and scheduling plans becomes an issue of great challenge and importance. Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too. To deal with this integration, the modeling approach to problem with MIP structure is common in the literature. These models take precedence constraints into consideration to select machines and to determine sequences. In order to obtain viable sequences, we employed a proposed transformation matrix (TM). We also took advantage of an evolutionary search, called Learnable genetic Architecture (LEGA). Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA). Our approach was evaluated with problems with various sizes. The experimental results show that our proposed architecture outperforms prior approaches, or it performs, at least, as efficiently as they do.
https://ijms.ut.ac.ir/article_70834_58ee99de1ae3848dd3f2aeb08469ab44.pdf
2019-04-01
307
333
10.22059/ijms.2019.255363.673086
Supply Chain Management
Process planning
Scheduling
Transformation matrix
Evolutionary search
Esmaeel
Moradi
esmaeel@ostatemail.okstate.edu
1
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, USA
AUTHOR
Ashkan
Ayough
a_ayough@sbu.ac.ir
2
Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran
AUTHOR
Mostafa
Zandieh
m_zandieh@sbu.ac.ir
3
Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Ausaf, M. F., Gao, L., & Li, X. (2015). Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm. Frontiers of Mechanical Engineering, 10(4), 392–404.
1
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