ORIGINAL_ARTICLE
Modeling the Optimal Coalition Structure Using the Core Solution Concept
Coalition formation is an important step towards developing the social welfare by improving the performance. This is pursued in two main research streams: (i) algorithmic approaches to achieve the optimal coalition structure and (ii) cooperative game theory to distribute the coalition payoff based on fairness and stability criteria. The aim of this paper is to integrate the strengths of the two approaches in order to achieve an optimal and stable coalition structure. The main innovation of the paper is using mathematical modeling to incorporates stability condition in a set partitioning problem through core solution concept to overcome decentralized procedures of coalition formation and payoff distribution. A numerical example is used to investigate the performance of overlapping and non-overlapping optimal coalition structure models. The results show that cooperation leads to improve social welfare. This improvement has an ascending trend with a decreasing slope and does not change after increasing the upper limit of players allowed to join the coalition to the certain extent. This is due to several reasons which prevent players to form grand coalition and suggests that, to form large coalitions, one should compare achieved gains with the managerial complexities and the increased costs of coordination and communication between players.
https://jimp.sbu.ac.ir/article_94176_ecad3582d7d5ae740d998e3576065209.pdf
2021-03-21
9
32
10.52547/jimp.11.1.9
Coalition
Optimal Coalition Structure
Stability
Core
Cooperative Game Theory
Overlapping Coalition Structure
Mathematical Modeling
Mohammadreza
Mehregan
mehregan@ut.ac.ir
1
Professor, University of Tehran .
LEAD_AUTHOR
Ghahreman
Abdoli
abdoli@ut.ac.ir
2
Professor, University of Tehran.
AUTHOR
Elham
Razghandi
elham.razghandi@ut.ac.ir
3
Ph.D Student, University of Tehran.
AUTHOR
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61
ORIGINAL_ARTICLE
Modeling Steel Supply Chain and Estimating Its Consumption through ABM Methodology
The purpose of this study was to develop an agent based model that could simulate the steel supply chain and estimate its production and consumption, taking into account the key factors of the steel industry. The approach of the present study is mixed (quantitative and qualitative). In the first part of the research (qualitative), the agents of the steel chain consumption model were obtained through interviews with experts using thematic analysis method. In the second part of the research (quantitative), a questionnaire was used to survey the causal relationships of the factors extracted from the interviews and the thematic analysis method, and then the relationship model was tested by the DEMATEL method. Finally, by using AnyLogic software and coding in Java language, a model of steel supply chain and its consumption was designed throughan agent-based approach, and according to the opinion of steel industry experts, the model explanation process was also approved. The combination of agents identified in this study is consistent with the influence of factors on production, consumption, import and export of the steel chain in the proposed structural model.
https://jimp.sbu.ac.ir/article_87611_2e587e32629fdcc8efeaa87212189bc2.pdf
2021-03-21
33
52
10.52547/jimp.11.1.33
Supply Chain Management
Steel Chain Consumption
Thematic Analysis Method
Dematel Method
Agent Based Modeling
Adel
Azar
azara@modares.ac.ir
1
Professor, Tarbiat Modares University.
LEAD_AUTHOR
Mahdi
Mashayekhi
dr.mashayekhi@gmail.com
2
Ph.D Candidate, University of Tehran.
AUTHOR
Mojataba
Amiri
mamiry@ut.ac.ir
3
Associate Professor, University of Tehran.
AUTHOR
Hossein
Safari
hsafari@ut.ac.ir
4
Professor, University of Tehran.
AUTHOR
1. Aghaei, Milad, Fazli, zero. (2012). Applying the combined approach of DEMATEL and ANP to select the appropriate maintenance strategy (Case study: Work Vehicle Industry). Journal of Industrial Management Perspective, 2(2), 89-107. (In Persian)
1
2. Afshar Kazem, M.A., Makoei, A., Darman, Z. (2009). Developing the Supply Chain Strategy of Iran Steel Industry Using Systems Dynamics Analysis, Iranian journal of trade studies, 13(50), 201-224. (In Persian)
2
3. Azar,A, & Sadeghi A.(2015). Agent based modeling, a new approach in modeling complex ethical problems. Ethics in Science. & Technology, 7(1), 11-19.(In Persian)
3
4. Azar,A, Abedini Nayini, M. (2015). Designing a hybrid order planning model in the supply chain. Ministry of Science, Research and Technology - Tarbiat Modarres University.(In Persian)
4
5. Azimifard, A., Moosavirad, S. H., & Ariafar, S. (2018). Selecting sustainable supplier countries for Iran's steel industry at three levels by using AHP and TOPSIS methods. Resources Policy, 57, 30-44.
5
6. Bafandeh, A. & Nemat Abad, N. (2015). Agent-Baesd modeling is the basis of a new approach for analyzing consumer preferences. 4th National Conference on Management, Economics and Accounting, Tabriz, East Azarbaijan Industrial Management Organization, Tabriz University.(In Persian)
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7. Bates, H., & Slack, N. (1998). What happens when the supply chain manages you?: A knowledge-based response. European Journal of Purchasing & Supply. Management, 4(1), 63-72.
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23. Kolyaei M, Azar A, Rajabzadeh ghatari A.(2015). Design of An Integrated Robust Optimization Model for Closed-Loop Supply Chain and supplier and remanufacturing subcontractor selection. Journal of Decision Engineering, 2(7), 7-40. (In Persian)
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27. Rezaei Pendari, Abbas, (2014). Designing a service supply chain performance evaluation model; Cognitive mapping approach (Case study: Insurance industry in Iran. Journal of Industrial Management Perspective, 16, 388-404. (In Persian)
26
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36
ORIGINAL_ARTICLE
The Application of Data Envelopment Analysis in Evaluating the Performance of Universities and Higher Education Institutions: A Systematic Review of the Literature
The efficiency of universities and higher education institutions is considered by many researchers because of their strategic role in the development and economy of each country, because the evaluation of the efficiency of universities helps to implement effective programs for the development of higher education. The literature on evaluating the efficiency of universities and higher education institutions has evolved over the past decades. However, the divergence of approaches, process areas, differences in output and input variables of previous studies unveils the importance of conducting a systematic review on the use of data envelopment analysis technique in evaluating the performance of universities and higher education institutions. The purpose of this study is conducting such a review and identifying future trends in this field of research using a combination of systematic literature review and citation network analysis. After determining the search protocol and article selection criteria, 165 articles were finally selected and analyzed. The results show that, in recent years, in addition to educational and research activities, the performance of universities has been evaluated in terms of the performance of entrepreneurship and university-industry relations, which can be considered in development and improvement programs.
https://jimp.sbu.ac.ir/article_94175_0da3d0aaf30f0daaf16dc27e93ea64c8.pdf
2021-03-21
53
80
10.52547/jimp.11.1.53
Effeciency Evaluation
university performance evaluation
Data Envelopment Analysis
Systematic Literature Review
citation network analysis
Sara
Majidi
sara74majidi@yahoo.com
1
MSc. Student, University of Mazandaran.
AUTHOR
Hamidreza
Fallah Lajimi
h.fallah@umz.ac.ir
2
Assistant Professor, University of Mazandaran.
LEAD_AUTHOR
Abdolhamid
Safaei ghadikolaei
ab.safaei@umz.ac.ir
3
Professor, University of Mazandaran.
AUTHOR
Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education review, 22(1), 89-97.
1
Abramo, G., Cicero, T., & D’Angelo, C. A. (2011). A field-standardized application of DEA to national-scale research assessment of universities. Journal of Informetrics, 5(4), 618-628.
2
Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: An analysis of research-related output efficiency. Research Evaluation, 21(1), 2-14.
3
Azar, A., Gholamrezaei, D., Danaei Fard, H., Khodadad Hosseini, H. (2013). Analysis of University-Industry Relation in Higher Education Policies of the Fifth Development Plan using System Dynamics. Journal of Industrial Management Perspective, 3(Issue 1), Spring 2013, 79-115.(in Persian)
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Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European journal of operational research, 17(1), 35-44.
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Banker, R. D. (1989). An introduction to DEA with some of its models and their uses. Research in Governmental and Nonprofit accounting, 5, 125-163.
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Batagelj, V. (2003). Efficient algorithms for citation network analysis. arXiv preprint cs/0309023.
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Berbegal-Mirabent, J., García, J. L. S., & Ribeiro-Soriano, D. E. (2015). University–industry partnerships for the provision of R&D services. Journal of Business Research, 68(7), 1407-1413.
8
Berbegal-Mirabent, J., Lafuente, E., & Solé, F. (2013). The pursuit of knowledge transfer activities: An efficiency analysis of Spanish universities. Journal of Business Research, 66(10), 2051-2059.
9
Calero-Medina, C., & Noyons, E. C. (2008). Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field. Journal of Informetrics, 2(4), 272-279.
10
Chang, T. Y., Chung, P. H., & Hsu, S. S. (2012). Two-stage performance model for evaluating the managerial efficiency of higher education: Application by the Taiwanese tourism and leisure department. Journal of Hospitality, Leisure, Sport & Tourism Education, 11(2), 168-177.
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De França, J. M. F., de Figueiredo, J. N., & dos Santos Lapa, J. (2010). A DEA methodology to evaluate the impact of information asymmetry on the efficiency of not-for-profit organizations with an application to higher education in Brazil. Annals of Operations Research, 173(1), 39-56.
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18
Edirisinghe, N. C. P., & Zhang, X. (2010). Input/output selection in DEA under expert information, with application to financial markets. European journal of operational research, 207(3), 1669-1678.
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Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
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Eva, M., Sagarra, M., & Agasisti, T. (2016). Assessing organizations’ efficiency adopting complementary perspectives: An empirical analysis through data envelopment analysis and multidimensional scaling, with an application to higher education. In Handbook of operations analytics using data envelopment analysis (pp. 145-166). Springer, Boston, MA.
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61
ORIGINAL_ARTICLE
Presenting a Robust Optimization Model to Design a Comprehensive Blood Supply Chain under Supply and Demand Uncertainties
Neglecting the supply chain management of perishable goods could create a lot of costs for organizations and companies. Blood is a perishable product in the healthcare supply chain, the shortage of which could prove quite problematic and disastrous. Any improvements in the blood supply chain management operations may increase service efficiency and decrease the cost of the healthcare system, saving the lives of lots of people. In this paper, a mixed-integer nonlinear programming model is proposed for comprehensive blood supply chain management, which includes gathering, processing and distributing blood and blood products by taking into account the demand lifetime and age. This model aims at decreasing supply chain costs and blood product deficiency. Robust optimization is utilized to take into account the inherent uncertainty and volatility of the demand and supply. The proposed model is first tested on a small-scale numerical example in GAMS software. Then a large-scale problem is solved using Whale and Imperialist Competition algorithms and the results are compared. In addition, a case study is presented to show the applicability of the proposed model.
https://jimp.sbu.ac.ir/article_94174_6fb2158c5d996e5278dfc0dbd08ec4cc.pdf
2021-03-21
81
116
10.52547/jimp.11.1.81
Blood supply chain
Mathematical Modeling
Robust Modeling
Whale Metaheuristic Algorithm
Imperialist Competitive Algorithm
Taher
Kouchaki Tajani
taher.kouchaki@yahoo.com
1
Ph.D. Student in Industrial Management, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AUTHOR
Ali
Mohtashami
mohtashami07@gmail.com
2
Associate Professor of Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
LEAD_AUTHOR
Maghsoud
Amiri
amiri@atu.ac.ir
3
Professor, Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran.
AUTHOR
Reza
Ehtesham Rasi
rezaehteshamrasi@gmail.com
4
Assistant Professor of Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AUTHOR
Arvan, M., R. (2015). Tavakkoli-Moghaddam, and M. Abdollahi, Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 3(1), 57-68.
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Bozorgi Amiri, A., S. Mansoori, and M.S. Pishvaee, Multi-objective Relief Chain Network Design for Earthquake Response under Uncertainties. Journal of Industrial Management Perspective, 2017. 7(Issue 1, Spring 2017): p. 9-36. (In Persian).
5
Daneshvar, A., Homayounfar, M., & Farahmandnejad, A. (2020). Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE. Journal of Industrial Management Perspective, 9(Issue 4), 41-61. (In Persian)
6
Derikvand, H., et al., (2020). A robust stochastic bi objective model for blood inventory-distribution management in a blood supply chain. European Journal of Industrial Engineering, 14(3), 369-403. (In Persian)
7
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8
Doodman, M., & Bozorgi Amiri, A. (2020). Integrate Blood Supply Chain Network Design with Considering Lateral Transshipment under Uncertainty. Journal of Industrial Management Perspective, 9(Issue 4), 9-40. (In Persian)
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16
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Hosseini-Motlagh, S.-M., M.R.G. Samani, & Homaei, S. (2020). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1085-1104.
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24
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31
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32
ORIGINAL_ARTICLE
Cash flow Optimization in Medicine Supply Chain: A Supply Risk Approa
Despite the increasing importance of cash flow management in the financial supply chain, limited works have been conducted in this field. This research optimizes the flow of money in the medical supply chain from the viewpoint of a distribution company. In this context, the focal company receives the medical supplies from the upstream suppliers and sells them to the downstream retailers and makes payments to suppliers with earned money from retailers. The imbalance between the cash inflow and outflow causes the imposition of a penalty for late-payments and supply risk as a result of the poor reputation in the market. In this context, the question is which payment sequence will minimize the total monetary outflows and the risk of supply. To answer this question, a bi-objective 0-1 linear programming model was developed. Solving the model by genetic algorithm determined the best sequence of payments and minimized the cash outflow as well as the risk of violation of the due date for the invoices.
https://jimp.sbu.ac.ir/article_87592_79be86e1772f012618167833b0f545fa.pdf
2021-03-21
117
145
10.52547/jimp.11.1.117
Genetic algorithm
Cash Flow
medicine supply chain
financial supply chain
mathematical model building
Rahim
Foukerdi
r.foukerdi@gmail.com
1
Assistant Professor, University of Qom.
LEAD_AUTHOR
Zenab
Talavari
zenab.talavari@yahoo.com
2
Master, University of Qom.
AUTHOR
Aljazzar, S. M., Jaber, M. Y., & Moussawi-Haidar, L. (2016). Coordination of a three-level supply chain (supplier–manufacturer–retailer) with permissible delay in payments. Applied Mathematical Modelling, 40(21-22), 9594-9614.
1
Badell, M., Romero, J., & Puigjaner, L. (2005). Optimal budget and cash flows during retrofitting periods in batch chemical process industries. International Journal of Production Economics, 95(3), 359-372.
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Bertel, S., Fenies, P., & Roux, O. (2008). Optimal cash flow and operational planning in a company supply chain. International Journal of Computer Integrated Manufacturing, 21(4), 440-454.
4
Blackman, I. D., Holland, C. P., & Westcott, T. (2013). Motorola's global financial supply chain strategy. Supply Chain Management: An International Journal, 18(2), 132-147.
5
Brigham, E. F., & Houston, J. F. (2012). Fundamentals of financial management. Cengage Learning.
6
Collette, Y., & Siarry, P. (2013). Multiobjective optimization: principles and case studies. Springer Science & Business Media.
7
Comelli, M., Féniès, P., & Tchernev, N. (2008). A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain. International Journal of Production Economics, 112(1), 77-95.
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Cooper, R. & Kaplan, R. (1998). How cost accounting distorts product cost. Management Accounting, 69(10), 20-27.
9
10. Deb, K., & Srivastava, S. (2012). A genetic algorithm based augmented Lagrangian method for constrained optimization. Computational optimization and Applications, 53(3), 869-902.
10
11. El-kholy, A. M. (2014). A multi-objective fuzzy linear programming model for cash flow management. Engineering Research and Applications, 4(8), 152-163.
11
12. Faraji, M. A., & Behnamian, J. (2020). A simulation-based Genetic algorithm to solve the workshop flow scheduling problem by considering the energy cost under uncertainty conditions. Journal of industrial management perspective, 10(2), 9-32. (in Persian)
12
13. Farris, T. M. & Hutchison, D. P. (2002). Cash-to-cash: the new supply chain management metric. International Journal of Physical Distribution and Logistics Management, 32(4), 288-298.
13
14. Gardner, D. L. (2004). Supply chain vector: Methods for linking the execution of global business models with financial performance. J. Ross Publishing.
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15. Garey, M. R., & Johnson, D. S. (1979). A Guide to the Theory of NP-Completeness. WH Freemann, New York, 70.
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16. Gitman, L. J., & Zutter, C. J. (2015). Principles of Managerial Finance. Prentice Hall. Pearson.
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17. Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935.
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19. Gupta, S., & Dutta, K. (2011). Modeling of financial supply chain. European Journal of Operational Research, 211(1), 47-56.
19
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21. John, A. O. (2014). Effect of cash management on profitability of Nigerian manufacturing firms. International Journal of Marketing and Technology, 4(1), 129-140.
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22. Kardan B., Vadiei, M. H., & Rostami, A. (2016). Using fuzzy regression to explain the relationship between supply chain management and financial performance. Journal of industrial management perspective. 5(4), 119-141. (in Persian)
22
23. Khakbiz, M., Rezaei Pandari, A., & Dehghan Niri, M. (2017). Designing a mathematical model for stock portfolio diversification and solving it using genetic algorithms. Journal of industrial management perspective, 7(1), 173-196. (in Persian)
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26. Kroes, J. R., & Manikas, A. S. (2014). Cash flow management and manufacturing firm financial performance: A longitudinal perspective. International Journal of Production Economics, 148, 37-50.
26
27. Krumrey, L., Moeini, M., & Wendt, O. (2017, June). A Cash-Flow-Based Optimization Model for Corporate Cash Management: A Monte-Carlo Simulation Approach. In International Conference on Computer Science, Applied Mathematics and Applications (pp. 34-46). Springer, Cham.
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29. Mashayekhi, E. N. & Alam Tabriz, A. (2017). The effect of upstream and downstream supply chain integration on performance and quality program, Journal of industrial management perspective. 6(4), 37-57. (In Persian)
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30
31. Moussawi-Haidar, L., & Jaber, M. Y. (2013). A joint model for cash and inventory management for a retailer under delay in payments. Computers & Industrial Engineering, 66(4), 758-767.
31
32. Peng, J., & Zhou, Z. (2019). Working capital optimization in a supply chain perspective. European Journal of Operational Research, 277(3), 846-856.
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34. Radfar, A. & Mohammaditabar, D. (2019). A vendor-based two-objective inventory optimization in a green multilevel supply chain. Journal of industrial management perspective, 9(3), 109-134. (In Persian)
34
35. Reider, R., & Heyler, P. B. (2003). Managing cash flow: An operational focus. John Wiley & Sons.
35
36. Righetto, G. M., Morabito, R., & Alem, D. (2016). A robust optimization approach for cash flow management in stationery companies. Computers & Industrial Engineering, 99, 137-152.
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38
39. Rostami, M. (2020). An optimal model for the closed-loop supply chain scheduling problem. Journal of industrial management perspective, 10(3), 29-52. (In Persian)
39
40. Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2018). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529.
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43
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44
ORIGINAL_ARTICLE
Developing a Project Planning Model Considering the Executive Methods and the Rework Activity
In this research, a model with three objective functions is presented to solve the problem of time, cost and quality trade-off in project planning. What distinguishes this model is that, in addition to considering different executive methods for each activity, rework activity is defined for some activities in order to prevent a decrease in quality. Other features of this model include covering various costs including incentive cost and tardiness cost. Because of the NP-Hardness of such large-scale problems, genetic algorithm is used to solve the proposed model.The results obtained from solving a real problem in screen filter production indicate that considering different executive methods for activities as well as different costs and defining rework activity can lead to better results towards the final goal by presenting a comprehensive model.If more accurate and detailed information is used for time, cost and quality in the model, it can achieve more rational results, similar to those of the real world more confidently. Under such conditions the least time and cost and most quality are achieved for successful implementation of project.
https://jimp.sbu.ac.ir/article_94172_04f931d3541aaf2158ad246799aeecd5.pdf
2021-03-21
147
173
10.52547/jimp.11.1.147
Genetic algorithm
Project Planning
Time-Cost and Quality trade-off
Incentive Cost
tardiness cost
Javad
Ahmadi Moghadam
javad.ahmadi@mail.um.ac.ir
1
MSc. Student, Ferdowsi University Mashhad.
AUTHOR
Nasser
Motahari Farimani
n.motahari@um.ac.ir
2
Assistant Professor, Ferdowsi University Mashhad.
LEAD_AUTHOR
Mostafa
Kazemi
kazemi@um.ac.ir
3
Professor, Ferdowsi University Mashhad.
AUTHOR
Abdelsalam, H. M., & Gad, M. M. (2009). Cost of quality in Dubai: An analytical case study of residential construction projects. International journal of project management, 27(5), 501-511.
1
Afruzi, E. N., Najafi, A. A., Roghanian, E., & Mazinani, M. (2014). A multi-objective imperialist competitive algorithm for solving discrete time, cost and quality trade-off problems with mode-identity and resource-constrained situations. Computers & Operations Research, 50, 80-96.
2
Afshar, A., Kaveh, A., & Shoghli, O. R. (2007). Multi-objective optimization of time-cost-quality using multi-colony ant algorithm. Asian Journal of Civil Engineering (Building and Housing), 8(2), 854-1563.
3
Akhundy, A. M.; Keshavarz, T. (2017). Provide a multi-objective genetic algorithm to solve the multi-objective problem of cost-time-quality balance of the project, taking into account the predictive relationships and failure of activities in case of lack of resources. Second International Conference on Management and Accounting, Tehran, Salehan Higher Education Institute (In Persian).
4
Amouzad Mahdirji, H., Mokhtarzadeh, N., & Radmand, S. (2017). Model gray fuzzy ideal planning to balance time, cost, risk and project quality. Journal of Industrial Management Perspective, 7(3), 47-80(In Persian).
5
Ataei, Y., & Shirviehzad, H. (2012). Time-cost balance in the project of constructing a gas network in Sadra city of Shiraz with the assumption of minimum cost. First National Conference on Industrial Engineering and Systems. Najafabad, Islamic Azad University (In Persian).
6
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7
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39
ORIGINAL_ARTICLE
Intelligent Design of a Dynamic Facility Layout in the Stochastic Environment of Flexible Manufacturing Systems Considering Routing Flexibility
This paper aims at proposing a novel quadratic assignment-based mathematical model for designing an optimal facility layout in each period of the stochastic dynamic facility layout problem (SDFLP). Considering routing flexibility is the main assumption of this problem so that parts can pass through multiple routes. It is also assumed that product demands are independent, normally distributed random variables with known expected value and variance changing from period to period at random. In addition, to solve the proposed model, a new hybrid meta-heuristic algorithm is developed by combining simulated annealing (SA) and the CRAFT approaches. Finally, the proposed model and the hybrid algorithm are verified and validated using design of experiment, real case study and sensitivity analysis methods as well as solving some numerical examples.The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time perspectives. Moreover, the proposed model can be used to design the layout of facilities in both of the stochastic and deterministic environments of traditional and modern manufacturing systems.
https://jimp.sbu.ac.ir/article_100733_1f3a3ec26514404a761e7d8058b390ac.pdf
2021-03-21
175
209
10.52547/jimp.11.1.175
Stochastic dynamic facility layout problem
Flexible Manufacturing Systems
Routing flexibility
Simulated Annealing
CRAFT
Gorbanali
Moslemipour
ghmoslemipour@pnu.ac.ir
1
Assistant Proffesor, Payame Noor University.
LEAD_AUTHOR
Seyed Mohammad
Ghadirpour
ghadirpour.iust@gmail.com
2
M.s, Payame Noor University.
AUTHOR
Azadeh, S., Haghighi, S. M., & Asadzadeh, S. M. (2014). A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times. Journal of Manufacturing Systems, 33, 287–302.
1
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5
Benjaafar, S., & Sheikhzadeh, S. (2000). Design of flexible plant layouts. IIE Transactions, 32, 309-322.
6
Braglia, M., Zanoni, S., & Zavanella, L. (2003). Layout design in dynamic environments: analytical issues. International Transition in Operation Research, 12, 1-19.
7
Derakhshan Asl, A., & Kuan, K. Y. (2015). Solving unequal-area static and dynamic facility layout problems using modified particle swarm optimization. Journal of Intelligent Manufacturing, 28, 1317–1336.
8
Drira, A., Pierreval, H., & Hajri-Gabouj, S. (2007). Facility layout problems: A survey. Annual Reviews in Control, 31, 255–267.
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10. Eslaminia, A., & Azimi, P. (2020). Solving the Electric Vehicle Routing Problem Considering the Vehicle Volume Limitation Using a Simulated Annealing Algorithm. Journal of Industrial Management Perspective, 36, 165-188. (In Persian).
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11. Fazlelahi, F. Z., Pournader, M., Gharakhani, M., & Sadjadi. (2015). A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm. Journal of Engineering Manufacture, 230(12), 2264-2274.
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12. Forghani, K., Mohammadi, M., & Ghezavati, V. (2013). Designing robust layout in cellular manufacturing systems with uncertain demands. International Journal of Industrial Engineering Computations, 4(2), 215-226.
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13. Groover, M. P. (2008). Automation, production systems, and Computer-Integrated manufacturing. New Jersey: Pearson Education Inc.
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14. Ghadirpour, S. M., Rahmani, D., Moslemipour, G. (2020). Routing flexibility for unequal–area stochastic dynamic facility layout problem in flexible manufacturing systems. International Journal of Industrial Engineering & Production Research, 31(2), 269-285.
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15. Jithavech, I., & Krishnan, K. (2010). A simulation-based approach for risk assessment of facility layout designs under stochastic product demands. International Journal of Advanced Manufaturing Technology, 49, 27-40.
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16. Krishnan, K. K., Cheraghi, S. H., & Nayak, C. N. (2006). Dynamic From-Between Chart: a new tool for solving dynamic facility layout problems. International Journal of Industrial and Systems Engineering, 1(1/2), 182-200.
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17. Krishnan, K. K., Cheraghi, S., & Nayak, C. (2008). Dynamic facility layout design for multiple production scenarios in a dynamic environment. International Journal of Industrial and Systems Engineering, 3(2), 105-133.
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20. Lee, T. S., Moslemipour, G., Ting, T. O., & Rilling, D. (2012). A Novel Hybrid ACO/SA Approach to Solve Stochastic Dynamic Facility Layout Problem (SDFLP). Communication in Computer and Information Science, special issue: Emerging Intelligent Computing Technology and Applications, 304, 100-108.
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21. Lee, T., Moslemipour, G., (2012). Intelligent design of a flexible cell layout with maximum stability in a stochastic dynamic situation. Trends in intelligent robotics, automation, and manufacturing. Springer, 398-405.
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26. Moslemipour, G., Lee, T. S. (2012). Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems. International Journal of Flexibility Manufacturing System, 1849-1860.
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27. Moslemipour, G., Lee, T. S., & Rilling, D. (2012). A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. International Journal of Advanced Manufaturing Technology, 60, 11-27.
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37. Tayal, A., & Singh, S. (2014). Chaotic Simulated Annealing for Solving Stochastic Dynamic Facility Layout Problem. Journal of International Management Studies, 14(2), 67-74.
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40. Vitayasak, S., Pongcharoen, P., & Chris Hicks, C. (2016). A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. International Journal of Production Economics, 190, 146-157.
40
ORIGINAL_ARTICLE
Integration of MDM and Fuzzy Inference System in Designing, Creating, and Developing TRM for Systems
Roadmap and especially technology roadmap is a management tool and technique to achieve future goals in order to link business to technology. With the increasing trend and the need to use different types of roadmaps, new tools are needed to analyze the complex relationships between layers and roadmap elements. The purpose of this study is to focus on a new tool for analyzing the relationships between roadmap layers with a combined method with a proposed framework. Previous research has addressed relationships between layers only with tools such as quality function deployment (QFD) and the linking grid. Although DSM and TRM have been extensively studied independently so far, this study, therefore, proposes an integrated six-step framework combining a multi-domain matrix and design structure matrix and fuzzy set theory in designing, creating, and developing technology roadmaps for Suggest systems to support decision making and case studies. In this study, multi-domain MDM matrix and fuzzy inference, and network theory were used in designing the technology roadmap. The advantage of using a multi-domain matrix is the simultaneous analysis of each domain specifically in the DSM format as well as the entire domain in the MDM format. The results of the present study indicate the provision of detailed instructions for managers to prepare a suitable roadmap.
https://jimp.sbu.ac.ir/article_101051_534254461055e63b41df10b8aeb3dd67.pdf
2021-03-21
211
245
10.52547/jimp.11.1.211
Technology roadmap
Design structure matrix
Relationship and DependencyAnalysis
Multi-Domain Matrix
Fuzzy Inference
Seyed Mohammad
Sajadiyan
sajadiyan@pnu.ac.ir
1
Ph.D student, Malek Ashtar University of Technology.
AUTHOR
Reza
Hosnavi
hosnavi@mut.ac.ir
2
Professor, Malek Ashtar University of Technology.
LEAD_AUTHOR
Morteza
Abbasi
moryabott@gmail.com
3
Assistant Professor, Malek Ashtar University of Technology.
AUTHOR
Mehdi
Karbasian
mkarbasian@yahoo.com
4
Associate Professor, Malek Ashtar University of Technology.
AUTHOR
Mohammad Hossein
Karimi Gavareshki
mhkarimigg@yahoo.com
5
Assistant Professor, Malek Ashtar University of Technology.
AUTHOR
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58
ORIGINAL_ARTICLE
A Multi-Objective Mathematical Formulation for the Airline Crew Scheduling Problem: MODE and NSGA-II Solution Approaches
In this research, a multi-objective mathematical model is proposed for the airline multi-skilled crew scheduling problem. The multi-skilled crew can be assigned to flights and airplanes according to their skills. The objective functions of the proposed model are: (1) Maximizing the number of leave days planned according to the days announced by the flight crew, and (2) Minimizing the penalty costs associated with violation of minimum and maximum working hours. Several test problems have been designed based on the data acquired by the airline studied in this research. Due to the NP-hard essence of the model, we have employed two meta-heuristics, namely the multi-objective differential evolution (MODE) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). These algorithms are calibrated using the Taguchi method. The algorithms have been compared based on several multi-objective performance measures. Each algorithm has been more successful in terms of some metrics. The comparisons between algorithms and sensitivity analysis show that the proposed model and algorithms can produce appropriate schedules for the airline crew scheduling problem.
https://jimp.sbu.ac.ir/article_101030_ca524913cbf39f9e701f607b4e6bd171.pdf
2021-03-21
247
269
10.52547/jimp.11.1.247
Flight planning
Crew scheduling
Multi-Objective Optimization
Meta-heuristic algorithms
Vahid
Baradaran
v_baradaran@iau-tnb.ac.ir
1
Associate Professor, Islamic Azad University, Tehran North Branch
LEAD_AUTHOR
Amir Hossein
Hosseinian
ah_hosseinian@iau-tnb.ac.ir
2
Ph.D, Industrial Engineering, Islamic Azad University, Tehran North Branch.
AUTHOR
1. Alinezhad, A., Sabet, S. & Ekhtiari, M. (2014). Solving Fuzzy Multiple Objective Dynamic Cellular Manufacturing System Problem using a Hybrid Algorithm of NSGA-II and Progressive Simulated Annealing. Journal of Industrial Management Perspective, 4(3), 131-156.
1
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2
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3
4. Azmat, C.S., & Widmer, M. (2004). A case study of single shift planning and scheduling under annualized hours: A simple three-step approach. European Journal of Operational Research, 153(1), 148-175.
4
5. Chien C.F., Tseng, F.P., & Chen, C.H. (2008). An evolutionary approach to rehabilitation patient scheduling: A case study. European Journal of Operational Research, 189(3), 1234-1253.
5
6. Deb, K., Pratap, A., Agrawal, S., & Meyarivan, T. (2000). A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on evolutionary computation, 6(2), 182-197.
6
7. Deveci, M., & Demirel, N.C. (2018). A Survey of the literature on airline crew scheduling. Engineering Applications of Artificial Intelligence, 74, 54-69.
7
8. Ding, S., Chen, C., Xin, B., & Pardalos, P.M. (2018). A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches. Applied soft computing, 63, 249-267.
8
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10
11. Fan, Q., & Yan, X. (2015). Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective p-xylene oxidation process. Journal of Intelligent Manufacturing, 29(1), 35-49.
11
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12
13. Gamache, M., Hertz, A., & Ouellet, J.O. (2007). A graph coloring model for a feasibility problem in monthly crew scheduling with preferential bidding. Computers & Operations Research, 34(8), 2384-2395.
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15. Ho, S.C., & Leung, J.M.Y. (2010). Solving a manpower scheduling problem for airline catering using metaheuristics. European Journal of Operational Research, 202(3), 903-921.
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17. Imani Imanlu, M. & Atighehchian, A. (2017). Daily Operating Rooms Scheduling under Uncertainty using Simulation based Optimization Approach. Journal of Industrial Management Perspective, 7(2), 53-82.
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23. Masri, H., Krichen, S., & Guitouni, A. (2015). A multi-start variable neighborhood search for solving the single path multicommodity flow problem. Applied Mathematics and Computation, 251, 132-142.
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24. Mercier A., & Soumis, F. (2007). An integrated aircraft routing, crew scheduling and flight retiming model. Computers & Operations Research, 34(8), 2251-2265.
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40
ORIGINAL_ARTICLE
Identifying the Factors That Affect the Customer Experience and Customer Satisfaction Impact on Repurchasing Behaviors in Online Retailers in IRAN
After the success of Digikala as the largest online retailer in Iran, many online retailers emerged to achieve their appropriate market share. It seems that examining customer behavior in online retailers needs to be further explored and enhanced to achieve competitive power. The purpose of this study is to investigate the impact of pre-buying, buying, and post-buying processes on customer experience and the effects of customer experience on customer satisfaction, then customer satisfaction and customer experience on Repurchase behavior in online retailers. The current research is descriptive and applied. The community of this study is all those who have experience in buying from Digikala retailers. To determine the sample size, the Cochran formula was applied, and because of the infinite statistical population, a sample size of 384 people was selected. A researcher-made questionnaire was created, and a combination was used to collect data. In order to analyze the data obtained from the questionnaire, the structural equation modeling method was used. The hypothetical test was performed using descriptive and inferential statistics. The results suggest that pre-buying, buying, and post-buying processes affect the customer experience in Digikala online retail. Customer experience also affects customer satisfaction and re-purchasing from Digikala online retailers.
https://jimp.sbu.ac.ir/article_101154_8200f492b65191cdbc00c1ad1ab15828.pdf
2021-03-21
271
293
10.52547/jimp.11.1.271
Customer Experience
Customer Satisfaction
Repurchase
Online Retailer
Buying Processes
Mohammad Ali
Tousi
alitosi99@gmail.com
1
Master Student, Kharazmi University.
AUTHOR
Seyed Mahdi
Sadat Rasoul
msadatrasoul@khu.ac.ir
2
Assistant Professor, Kharazmi University.
LEAD_AUTHOR
Sepideh
Shafia
shafia931@atu.ac.ir
3
Assistant Professor, Kharazmi University.
AUTHOR
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