سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,500
تعداد مشاهده مقاله124,084,079
تعداد دریافت فایل اصل مقاله97,188,540
Ostadi, Bakhtiar, Ghorbani, Abolfazl, Mokhtarian, Reza. (1397). Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry). , 53(1), 509-521. doi: 10.22059/jieng.2019.246488.1484
Bakhtiar Ostadi; Abolfazl Ghorbani; Reza Mokhtarian. "Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry)". , 53, 1, 1397, 509-521. doi: 10.22059/jieng.2019.246488.1484
Ostadi, Bakhtiar, Ghorbani, Abolfazl, Mokhtarian, Reza. (1397). 'Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry)', , 53(1), pp. 509-521. doi: 10.22059/jieng.2019.246488.1484
Ostadi, Bakhtiar, Ghorbani, Abolfazl, Mokhtarian, Reza. Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry). , 1397; 53(1): 509-521. doi: 10.22059/jieng.2019.246488.1484

Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry)

Advances in Industrial Engineering
مقاله 4، دوره 53، شماره 1، فروردین 2019، صفحه 509-521    اصل مقاله (994 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2019.246488.1484
نویسندگان
Bakhtiar Ostadi* 1؛ Abolfazl Ghorbani2؛ Reza Mokhtarian3
1مهندسی صنایع و سیستم ها، دانشگاه تربیت مدرس تهران، ایران
2مهندسی صنایع، دانشگاه جامع امام حسین، تهران، ایران
3مهندسی صنایع، دانشگاه تربیت مدرس، تهران، ایران
چکیده
The cost of design and building industrial systems is greatly affected by determining the exact number of machineries and human resources, consequently allowing to achieve a higher level of efficiency and productivity. Different methods have been presented to estimate the number of required resources for operational processes. The reliability of the results from these methods is highly dependent on the estimation of the input data which, under uncertain conditions, might have a vague nature and convey incorrect information. Therefore, this study aimed to propose a novel framework based on the fuzzy logic to determine the optimal number of machineries and human resources. The fuzzy set theory was used to determine the percentage of wastes and the time required to complete operational processes. Moreover, to prove the practicality of the proposed model and given the significance of improving the productivity of Textile industry in Iran, the proposed model was employed in a case study of the textile industry and the results were compared with the standard method. The results suggest a significant difference between the number of machineries and human resources estimated by the proposed method and that by the standard method. These differences may negatively affect the performance and optimal usage of the available capacity. The results obtained from the proposed model offer more accurate and comprehensive information under uncertain conditions, allowing us to make appropriate decisions to revise the unused capacity, reduce the cost of idle resources, and increase the efficiency and productivity of the industry.
کلیدواژه‌ها
Number of required equipment؛ Textile industry؛ Fuzzy logic؛ Uncertainty
مراجع
  1. Sobhani, H., and Mohammadloo, H. A. (2008). “Comparative Analysis of the Factor Productivity in Iran's Large Manufacturing”, Journal of Economic Research (Tahghighat- E- Eghtesadi), Vol. 43, No, 1: PP. 94-126.
  2. Kohansal, M. R., and Hayatgheibi, F. (2015). “Comparison of Regional Differences in the Productivities of Intermediate Factors”, Quarterly Journal of Economic Research, Research Institute of Economics, Vol. 15, No. 1, PP. 159-184.
  3. Sule, D. R. (2008). Manufacturing Facilities: Location, Planning, and Design, Chapter 6, 3 Ed: CRC Press.
  4. Koopmans, T. C., and Beckmann, M. (1957). “Assignment Problems and the Location of Economic Activities”, Econometrica: Journal of the Econometric Society, Vol. 25, No. 1, PP. 53-76.
  5. Azadivar, F., and Wang, J. (2000). “Facility Layout Optimization Using Simulation and Genetic Algorithms”, International Journal of Production Research, Vol. 38, No. 17, PP. 4369-4383.
  6. Shayan, E., and Chittilappilly, A. (2004). “Genetic Algorithm for Facilities Layout Problems Based on Slicing Tree Structure”, International Journal of Production Research, Vol. 42, No. 19, PP. 4055-4067.
  7. Toloei Ashlaghi, A., and Mojrian, M. (2010). “Developing a Facility Layout Optimization Method Using Mathematical Modeling (Case Study: Pooya Khodro Shargh)”, Scientific Journal Management System, Vol. 21, No. 87, PP. 81-94.
  8. Fattahi, P., Samouei, P., and Zandiyeh, M. (2016). “An Integrated Approach for Product-Mix Determination, Two-Sided Assembly Line Balancing and Worker Assignment, Based on the Bottlenecks of System”, Journal of Industrial Engineering, Vol. 50, No. 3, PP. 451-460.
  9. Monga, R., and Khurana, V. (2015), “Facility Layout Planning: A Review”, International Journal of
     Innovative Research in Science, Engineering and Technology, Vol. 4, No, 03, PP. 976-980.

  1. Vitayasak, S., and Pongcharoen, P. (2015). Genetic Algorithm Based Robust Layout Design by Considering Various Demand Variations, In International Conference in Swarm Intelligence.
  2. Rabani, M., Hosseini Aghozi, N., and Manavizadeh, N. (2013). “Robust Optimization Approach in Production Planning Problem Considering Rework, Backlogging and Breakdown Under Conditions of Uncertainty: An Evolutionary Approach”, Journal of Industrial Engineering, Vol. 47, No. 1, PP. 25-37.
  3. Zaree Mehrjerdi, Y., and Heidari Meybodi, M. (2017). “A Robust Optimization Model of Facility Location-Reliable Network Design in Competitive Environment Under Uncertainty”, Journal of Industrial Engineering, Vol. 51, No. 3, PP. 325-337.
  4. Zadeh, L. A. (1965). Fuzzy Sets, Information and Control, Vol. 8, No. 3, PP. 338-353.
  5. Ostadi, B., Mokhtarian Daloie, R., and Sepehri, M. M. (2018). “A Combined Modelling of Fuzzy Logic and Time-Driven Activity-Based Costing (TDABC) for Hospital Services Costing Under Uncertainty”, Journal of Biomedical Informatics, Vol. 89, No. 1, PP. 11-28.
  6. Roztocki, N., and Weistroffer, H. R. (2005). Evaluating Information Technology Investments: A Fuzzy Activity-Based Costing Approach.
  7. Wang, M. J., and Liang, G. S. (1995). “Benefit/Cost Analysis Using Fuzzy Concept”, The Engineering Economist, Vol. 40, No. 4, PP. 359-376.
  8. Maravas, A., and Pantouvakis, J. P. (2012). “Project Cash Flow Analysis in the Presence of Uncertainty in Activity Duration and Cost”, International Journal of Project Management, Vol. 30, No. 3, PP. 374-384.
  9. Kahraman, C., Ruan, D., and Tolga, E. (2002). “Capital Budgeting Techniques Using Discounted Fuzzy Versus Probabilistic Cash Flows”, Information Sciences, Vol. 142, No. 1, PP. 57-76.
  10. Kuchta, D. (2000). “Fuzzy Capital Budgeting”, Fuzzy Sets and Systems, Vol. 111, No. 3, PP. 367-385.
  11. Peidro, D. et al. (2009). “Fuzzy Optimization for Supply Chain Planning Under Supply, Demand and Process Uncertainties”, Fuzzy Sets and Systems, Vol. 160, No. 18, PP. 2640-2657.
  12. Cheng. C. H., and Lin, Y. (2002). “Evaluating the Best Main Battle Tank Using Fuzzy Decision Theory with Linguistic Criteria Evaluation”, European Journal of Operational Research, Vol. 142, No. 1, PP. 174-186.
  13. Liou, T. S., and Wang, M. J. J. (1992). Ranking Fuzzy Numbers with Integral Value, Fuzzy Sets and Systems.
آمار
تعداد مشاهده مقاله: 667
تعداد دریافت فایل اصل مقاله: 507


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب