[1] Kalir, A., Zorea, Y., Pridor, A., & Bregman, L. (2013). On the complexity of short-term production planning and the near-optimality of a sequential assignment problem heuristic approach. Computers & industrial engineering, 65(4), 537-543. https://doi.org/10.1016/j.cie.2013.05.005
[2] Nishi, T., Konishi, M., & Hasebe, S. (2005). An autonomous decentralized supply chain planning system for multi-stage production processes. Journal of intelligent manufacturing, 16, 259-275. https://doi.org/10.1007/s10845-005-7022-7
[3] Nishi, T., Sekiya, E., & Yin, S. (2012). Distributed optimization of energy portfolio and production planning for multiple companies under resource constraints. Procedia CIRP, 3, 275-280. https://doi.org/10.1016/j.procir.2012.07.048.
[4] S. Auer, W. Mayrhofer, and W. Sihn, (2012). "Implementation of a comprehensive production planning approach in special purpose vehicle production". Procedia CIRP, 3, 43-48, https://doi.org/10.1016/j.procir.2012.07.009.
[5] Lai, Y. J., Hwang, C. L., Lai, Y. J., & Hwang, C. L. (1992). Fuzzy mathematical programming (pp. 74-186). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48753-8_3
[6] Kiran, D. R. (2019). Production planning and control: A comprehensive approach. Butterworth-Heinemann. https://www.amazon.com/Production-Planning-Control-Comprehensive-Approach/dp/0128183640
[7] Nam, S. J., & Logendran, R. (1992). Aggregate production planning—a survey of models and methodologies. European journal of operational research, 61(3), 255-272. https://doi.org/10.1016/0377-2217(92)90356-E
[8] Wang, R. C., & Fang, H. H. (2001). Aggregate production planning with multiple objectives in a fuzzy environment. European journal of operational research, 133(3), 521-536. https://doi.org/10.1016/S0377-2217(00)00196-X
[9] Baykasoglu, A., & Gocken, T. (2010). Multi-objective aggregate production planning with fuzzy parameters. Advances in engineering software, 41(9), 1124-1131. https://doi.org/10.1016/j.advengsoft.2010.07.002
[10] Mirzapour Al-E-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International journal of production economics, 134(1), 28-42. https://doi.org/10.1016/j.ijpe.2011.01.027
[11] Lefta, F., Gozali, L., & Marie, I. A. (2020, July). Aggregate and disaggregate production planning, material requirement, and capacity requirement in PT. XYZ. IOP Conference series: Materials science and engineering (Vol. 852, No. 1, p. 012123). IOP Publishing. https://doi.org/10.1088/1757-899X/852/1/012123.
[12] Barman, S., & Tersine, R. J. (1991). Sensitivity of cost coefficient errors in aggregate production planning. Omega, 19(1), 31-36. https://doi.org/10.1016/0305-0483(91)90031-N
[13] A. Goli, E. B. Tirkolaee, B. Malmir, G. B. Bian, and A. K. Sangaiah. (2019). "A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand," Computing, 101, 499-529, https://doi.org/10.1007/s00607-018-00692-2.
[14] Kourentzes, N., Rostami-Tabar, B., & Barrow, D. K. (2017). Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. Journal of business research, 78, 1-9. https://www.sciencedirect.com/science/article/pii/S0148296317301376
[15] Cheraghalikhani, A., Khoshalhan, F., & Mokhtari, H. (2019). Aggregate production planning: A literature review and future research directions. International journal of industrial engineering computations, 10(2), 309-330. https://doi.org/10.5267/j.ijiec.2018.6.002.
[16] Cheng, C., & Shafir, E. (2019). Aggregate production planning for engineer-to-order products. Department of supply chain management,1-56. https://B2n.ir/mx5579
[17] Girkes, F., Reimche, M., Bergmann, J. P., Töpfer-Kerst, C. B., & Berghof, S. (2022, October). Aggregated production planning for engineer-to-order products using reference curves. Congress of the German academic association for production technology (pp. 642-651). Springer International Publishing. https://doi.org/10.1007/978-3-031-18318-8_64
[18] Holt, C. C., Modigliani, F., & Simon, H. A. (1955). A linear decision rule for production and employment scheduling. Management science, 2(1), 1-30. https://doi.org/10.1287/mnsc.2.1.1
[19] Saad, G. H. (1982). An overview of production planning models: Structural classification and empirical assessment. The international journal of production research, 20(1), 105-114. https://doi.org/10.1080/00207548208947752
[20] Kendall, K. E., & Schniederjans, M. J. (1985). Multi-product production planning: A goal programming approach. European journal of operational research, 20(1), 83-91. https://www.sciencedirect.com/science/article/pii/0377221785902863
[21] Mortezaei, N., Zulkifli, N., & Nilashi, M. (2015). Trade-off analysis for multi-objective aggregate production planning. Journal of soft computing and decision support systems, 2(2), 1-4. http://www.jscdss.com/index.php/files/article/view/29
[22] Da Silva, C. G., Figueira, J., Lisboa, J., & Barman, S. (2006). An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming. Omega, 34(2), 167-177. https://www.sciencedirect.com/science/article/pii/S0305048304001409
[23] Leung, S. C., & Chan, S. S. (2009). A goal programming model for aggregate production planning with resource utilization constraint. Computers & industrial engineering, 56(3), 1053-1064. https://www.sciencedirect.com/science/article/pii/S0360835208002301
[24] Safari, M., Mahdavi, I., Rezaeian, J., & Shirazi, B. (2025). Crowdsourcing-based material requirement optimization in the automotive parts production network with online demand coverage. Journal of decisions and operations research, 10(1), 169-193. https://doi.org/10.22105/dmor.2025.482155.1875.
[25] Wang, S. C., & Yeh, M. F. (2014). A modified particle swarm optimization for aggregate production planning. Expert systems with applications, 41(6), 3069-3077. https://doi.org/10.1016/j.eswa.2013.10.038
[26] Warfield, J. N. (1974). Structuring complex systems. Battelle Memorial Institute, Columbus, OH. https://www.amazon.com/Structuring-Complex-Systems-John-Warfield/dp/B002DIJNV4
[27] Charan, P., Shankar, R., & Baisya, R. K. (2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Business process management journal, 14(4), 512-529. https://doi.org/10.1108/14637150810888055/full/html
[28] Govindan, K., Kannan, D., Mathiyazhagan, K., Jabbour, A. B. L. D. S., & Jabbour, C. J. C. (2013). Analysing green supply chain management practices in Brazil’s electrical/electronics industry using interpretive structural modelling. International journal of environmental studies, 70(4), 477-493. https://doi.org/10.1080/00207233.2013.798494
[29] Eslamian Koupaei, M. R., & Shirouyehzad, H. (2023). Identification and evaluate the critical success factors of sustainable supply chain using interpretive structural modeling (Case study: Golnoor Co.). Modern research in performance evaluation, 1(4), 226-243. https://doi.org/10.22105/mrpe.2023.140567.
[30] Shokr, H., & Afrazeh, A. (2024). Interpretive structural modeling (ISM) of risk management in Iran's construction industry. Innovation management and operational strategies, 5(1), 54-78. https://doi.org/10.22105/imos.2024.453441.1348.
[31] Abbas, H., Asim, Z., Ahmed, Z., & Moosa, S. (2022). Exploring and establishing the barriers to sustainable humanitarian supply chains using fuzzy interpretive structural modeling and fuzzy MICMAC analysis. Social responsibility journal, 18(8), 1463-1484. https://doi.org/10.1108/SRJ-12-2020-0485
[32] Faghidian, S. F., & Mahmodi, S. (2024). Evaluation of total quality management enablers using the DEMATEL-ISM integration method in the steel industry. Systemic analytics, 2(1), 14-26. https://doi.org/10.31181/sa2120246
[33] Khalifa, H. A. E. W., Edalatpanah, S. A., & Bozanic, D. (2023). Enhanced a novel approach for smoothing data in modelling and decision-making problems under fuzziness. Computational algorithms and numerical dimensions, 2(3), 163-172. https://doi.org/10.22105/cand.2023.192505.
[34] Ragade, R. K. (1976). Fuzzy interpretive structural modeling. Cybernetics and system, 6(3-4), 189-211. https://doi.org/10.1080/01969727608927531
[35] Wang, L., Ma, L., Wu, K. J., Chiu, A. S., & Nathaphan, S. (2018). Applying fuzzy interpretive structural modeling to evaluate responsible consumption and production under uncertainty. Industrial management & data systems, 118(2), 432-462. https://doi.org/10.1108/IMDS-03-2017-0109
[36] Ajalli, M., Asgharizadeh, E., Jannatifar, H., & Abbasi, A. (2016). A fuzzy ISM approach for analyzing the implementation obstacles of electronic government in Iran. Science, technology, humanities and business management (ICSTHBM-16), 15. https://B2n.ir/up3519
[37] Sarma, P. R. S., & Pramod, V. R. (2014). Structural flexibility in supply chains: TISM and FISM approach. In Systemic flexibility and business agility (pp. 305-321). New Delhi: Springer India. https://doi.org/10.1007/978-81-322-2151-7_19
[38] Tseng, M. L. (2013). Modeling sustainable production indicators with linguistic preferences. Journal of cleaner production, 40, 46-56. https://doi.org/10.1016/j.jclepro.2010.11.019
[39] Khodadadi-Karimvand, M., & Shirouyehzad, H. (2021). Well drilling fuzzy risk assessment using fuzzy FMEA and fuzzy TOPSIS. Journal of fuzzy extension and applications, 2(2), 144-155. https://doi.org/10.22105/jfea.2021.275955.1086.
[40] Lee, E. S., & Li, R. J. (1988). Comparison of fuzzy numbers based on the probability measure of fuzzy events. Computers & mathematics with applications, 15(10), 887-896. https://doi.org/10.1016/0898-1221(88)90124-1
[41] Cheng, C. H. (1999). Evaluating weapon systems using ranking fuzzy numbers. Fuzzy sets and systems, 107(1), 25-35. https://doi.org/10.1016/s0165-0114(97)00348-5
[42] Ali, A. R., Albouy-Kissi, A., Vacavant, A., Grand-brochier, M., & Boire, J. Y. (2014). A novel fuzzy c-means based defuzzification approach with an adapted Minkowski distance. 19th computer vision winter workshop Zuzana Kúkelová and Jan Heller (Eds.) Křtiny, Czech Republic. https://cmp.felk.cvut.cz/cvww2014/papers/11/11.pdf
[43] Das, S. K. (2021). Optimization of fuzzy linear fractional programming problem with fuzzy numbers. Big data and computing visions, 1(1), 30-35. https://doi.org/10.22105/bdcv.2021.142084
[44] Khalifa, H. A. E. W., Bozanic, D., Najafi, H. S., & Kumar, P. (2024). Solving vendor selection problem by interval approximation of piecewise quadratic fuzzy number. Optimality, 1(1), 23-33. https://doi.org/10.22105/opt.v1i1.23
[45] Liu, J., Wan, L., Wang, W., Yang, G., Ma, Q., Zhou, H., ... & Lu, F. (2023). Integrated fuzzy DEMATEL-ISM-NK for metro operation safety risk factor analysis and multi-factor risk coupling study. Sustainability, 15(7), 5898. https://doi.org/10.3390/su15075898
[46] Nishat Faisal, M., Banwet, D. K., & Shankar, R. (2006). Supply chain risk mitigation: Modeling the enablers. Business process management journal, 12(4), 535-552. https://doi.org/10.1108/14637150610678113/full/html
[47] Attri, R., Dev, N., & Sharma, V. (2013). Interpretive structural modelling (ISM) approach: An overview. Research journal of management sciences, 2319(2), 1171. https://B2n.ir/bh3033
[48] Krynke, M. (2020). Risk management in the process of personnel allocation to jobs. System safety: Human-technical facility-environment, 2(1), 82-99. https://sciendo.com/pdf/10.2478/czoto-2020-0012
[49] Vaezi, E., Najafi, S. E., Hajimolana, S. M., Hosseinzadeh Lotfi, F., & Ahadzadeh Namin, M. (2020). Production planning and efficiency evaluation of a three-stage network. Journal of industrial and systems engineering, 13(2), 155-178. https://www.jise.ir/article_118487_0.html
[50] MacDuffie, J. P., Sethuraman, K., & Fisher, M. L. (1996). Product variety and manufacturing performance: Evidence from the international automotive assembly plant study. Management science, 42(3), 350-369. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.42.3.350
[51] Hobday, M. (1998). Product complexity, innovation and industrial organisation. Research policy, 26(6), 689-710. https://www.sciencedirect.com/science/article/pii/S0048733397000449
[52] Orfi, N., Terpenny, J., & Sahin-Sariisik, A. (2011). Harnessing product complexity: Step 1—establishing product complexity dimensions and indicators. The engineering economist, 56(1), 59-79. https://doi.org/10.1080/0013791X.2010.549935
[53] García-Alcaraz, J. L., Sánchez-Ramírez, C., Avelar-Sosa, L., & Alor-Hernández, G. (Eds.). (2020). Techniques, tools and methodologies applied to global supply chain ecosystems. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-26488-8
[54] Van den Berge, R., Magnier, L., & Mugge, R. (2021). Too good to go? Consumers’ replacement behaviour and potential strategies for stimulating product retention. Current opinion in psychology, 39, 66-71. https://doi.org/10.1016/j.copsyc.2020.07.014
