زما‌ن‌بندی ماشین‌های موازی یکسان با درنظر گرفتن عملیات مشترک به‌منظور کمینه‌ نمودن مجموع انرژی‌های مصرفی و مجموع زمان‌های دیرکرد

• Fang, K., Uhan, N., Zhao, F., & Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234-240.
• Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (2017). Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm. Journal of Cleaner Production, 144, 228-238.
• Jovane, F., Yoshikawa, H., Alting, L., Boër, C. R., Westkamper, E., Williams, D., Paci, A. M. (2008). The incoming global technological and industrial revolution towards competitive sustainable manufacturing. CIRP Annals - Manufacturing Technology, 57(2), 641-659.
• Mori, M., Fujishima, M., Inamasu, Y., & Oda, Y. (2011). A study on energy efficiency improvement for machine tools. CIRP Annals - Manufacturing Technology, 60(1), 145-148.
• Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems. Springer - Verlag New York.
• افسر، امیر. بهنامیان، جواد. (1398). زما‌ن‌بندی چندعاملی ماشین‌های موازی ناهمگن با درنظر گرفتن هزینه انرژی و کارهای به‌هنگام،‌ سال هفتم، شماره 15، صفحه 287–
• Zhang, M., Yan, J., Yan, S., & Zhang, Y., (2019). Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs, Procedia CIRP, Volume 80, 251-256.
• Masmoudi, O., Delorme, X. & Gianessi, P., (2019). Job-shop scheduling problem with energy consideration, International Journal of Production Economics, 216, 12-22.
• Marichelvam, M.K., Geetha, M., (2021). A memetic algorithm to solve uncertain energy-efficient flow shop scheduling problems, The International Journal of Advanced Manufacturing Technology, 115, 515-530.
• Guo, J., Lei, D. & Li, M., (2021). Two-phase imperialist competitive algorithm for energy-efficient flexible job shop scheduling, Journal of Intelligent & Fuzzy Systems, 40, 12125-12137.
• Li Z., Yang H., Zhang S., Liu G. (2016) Unrelated parallel machine scheduling problem with energy and tardiness cost. The International Journal of Advanced Manufacturing Technology, 84(1), 213-226.
• Che A., Zhang S., Wu X. (2017) Energy-conscious unrelated parallel machine scheduling under time-of-use electricity tariffs. Journal of Cleaner Production, 156: 688-697.
• Wang Y., Wang M., Lin S. (2017). Selection of cutting conditions for power constrained parallel machine scheduling. Robotics and ComputerIntegrated Manufacturing, 43, 105-110.
• Zeng Y., Che A., Wu X. (2018). Bi-objective scheduling on uniform parallel machines considering electricity cost. Engineering Optimization, 50(1), 19-36.
• Wu, X. & Che, A., (2018). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling, Omega Volume 82 , 155-165.
• Safarzadeh, H., Akhavan Niaki, S.T., (2019). Bi-Objective Green Scheduling in Uniform Parallel Machine Environments, Journal of Cleaner Production, 217, 559-572.
• Wang, S., Wang, X., Yu, J., Ma, S., & Liu, M. (2018). Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan, Journal of Cleaner Production, 193, 424-440.
• Anghinolfi, D., Paolucci, M. & Ronco, R., (2020). A bi-objective heuristic approach for green identical parallel machine scheduling, European Journal of Operational Research, S0377-2217(20)30631-7.
• Zhang, H., Wu, Y., Pan, R. & Xu, G., (2020). Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs, Journal of Intelligent Manufacturing. s10845-020-01561-6
• Karimi, E., Keshavarz, T. & Shakhsi-Niaei, M., (2021). Unrelated Parallel Machines Scheduling with Sequence Dependent Setup Times to Minimize Makespan and Tariff Charged Energy Consumption, Advances in Industrial Engineering, Winter, 55(1): 91-113.
• Zhou, B. & Gu, J., (2021). Energy-awareness scheduling of unrelated parallel machine scheduling problems with multiple resource constraints, International Journal of Operational Research, Vol. 41, No. 2, pp 196-217.
• Modos, I., Sucha, P. & Hanzalek, Z., (2021). On parallel dedicated machines scheduling under energy consumption limit, Computers and Industrial Engineering, Volume 159, Issue C.
• Rego, M., Pinto, J., Cota, L. & Souza, M., (2022). A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel machine scheduling, Advances in Industrial Engineering, PeerJ Computer Science. 844.
• Asadpour, M., Hodaei, Z., Azami, N. & Kehtari, E., (2022). A green model for identical parallel machines scheduling problem considering tardy jobs and job splitting property, Sustainable Operations and Computers. Volume 3, Pages 149-155.
• Arbib, C., Felici, G., & Servilio, M., (2019). Common operation scheduling with general processing times: A branch-and-cut algorithm to minimize the weighted number of tardy jobs, Omega, 84, 18-30.
• Wang J , Qiao C , Yu H . (2011). On progressive network recovery after a major disrup- In: Proc. of IEEE INFOCOM ;  p. 1925–33 . Shanghai, P.R. China , 10-15.
• Cheng TCE , Diamond J , Lin BMT .(1993) Optimal scheduling in film production to min- imize talent hold J Optim Theory, 79,197–206.
• Cherri, A., Arenales, M., Yanasse, H., Poldi, K., & Vianna, A., (2014). The one-dimensional cutting stock problem with usable leftovers – A survey. European Journal of Operational Research, 236, 395-402.
• Hinxman, A., )1980(. The trim-loss and assortment problems: A survey. European Journal of Operational Research 5, 8–18.
• Dyckhoff, H., )1990(. A typology of cutting and packing problems. European Journal of Operational Research 44, 145–159.
• Wascher, G., Haußner, H., & Schumann, H., (2007). An improved typology of cutting and packing problems European Journal of Operational Research, 183, 1109-1130.
• Arbib, C., Marinelli, F., (2014). On cutting stock with due dates, Omega, 46,11-20.
• Kwon, S., Joung, S., & Lee, K., (2019). Comparative analysis of pattern-based models for the two-dimensional two-stage guillotine cutting stock problem, Computers and Operations Research, 109, 159-169.
• Cui, Y., Zhong, C., & Yao, Y. (2015). Pattern-set generation algorithm for the one-dimensional cutting stock problem with setup cost. European Journal of Operational Research, 243(2), 540–546.
• Wuttke, D., Heese, H.S., (2017). Two-dimensional cutting stock problem with sequence dependent setup times, European Journal of Operational Research, 1-13.
• Ma, N., Liu, Y., & Zhou, Z., (2019). Two heuristics for the capacitated multi-period cutting stock problem with pattern setup cost, Computers and Operations Research, 109, 218-229.
• Arbib C, Felici G, Servilio M., (2016). Sorting common operations to minimize the number of tardy jobs. Networks, 64, 306–20.
• Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. R. (1979). Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of discrete mathematics: Elsevier; p. 287-326.
• Garey, M. R. & Johnson D. S. (1978). “Strong” NP-completeness results: motivation, examples, and implications. Journal of the ACM, 25(3):499-508.
• LI, Z., Yuan, H., Zhang, S. & Liu, G. (2015). Unrelated parallel machine scheduling problem with energy and tardiness cost, Int J Adv Manuf Technol, 170-015-7657-2.
• Azizoglu, M. & Kirca, O. (1977). Tardiness minimization on parallel machines, Int. J. Production Economics 55, 163-168.
• Lenstra, J.K., Rinnooy Kan, A.H.G. & Brucker, P. (1977). Complexity of machine scheduling problems, Annals of Discrete Mathematics 1, 343–362.
• Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems, Applied Mathematics and Computation, Volume 213, Issue 2, Pages 455-465.
• Deb K., Agarwal S., Pratap A. & Meyarivan T. (2000). A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature, 6: 849-858.
• خیرخواه، امیرسامان. نوبری، آرش. حاجی‌پور، وحید. (1395). ارائه الگوریتم رقابت استعماری چندهدفه جهت بهینه‌سازی مسأله برنامه‌ریزی تولید ادغامی پایا، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید،‌ سال چهارم، شماره 7، صفحه 1-15.
• Bandyopadhyay, S., Bhattacharya, R., (2013). Solving multi-objective parallel machine scheduling problem by a modified NSGA-II. Applied Mathematical Modelling.
• Rahmati, S.H.., Hajipour, V. & Akhavan Niak, S.T. (2013). A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem, Applied Soft Computing 13, 1728–1740.
• Ehtesham Rasi, R. (2021). Optimization of the multi-objective flexible job shop scheduling model by applying NSGAII and NRGA algorithms, Journal of Industrial Engineering and Management Studies, 8, No. 1, 2021, pp. 45-71.
• Al Jadaan, O., Rajamani, L. & Rao, C.R. (2008). Non-Dominated Ranked Genetic Algorithm for Solving Muti-Objective Optimization Problems: NRGA, Journal of Theoretical and Applied Information Technology.
• نجفی، امیرعباس. ارجمند، مسعود. (1395). ارائه سه الگوریتم فراابتکاری توسعه‌یافته به‌منظور حل مسأله هزینه دسترس‌پذیری منابع بااهداف کمینه‌سازی زمان اتمام پروژه و مجموع هزینه‌های منابع به‌صورت هم‌زمان، ویژه‌نامه ‌یازدهمین ‌کنفرانس بین‌المللی مهندسی‌ صنایع،‌ دوره 50، شماره 3، صفحه 471-482.
• حاجی‌سلطانی، فاطمه. سیف‌برقی، مهدی. (1399). ارائه مدلی چندهدفه برای مکانیابی-تخصیص سیستم‌های مراقبت سلامت پیشگیرانه با تقاضای احتمالی، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید،‌ سال هشتم، شماره 16، صفحه 15-37.
• Taguchi, G., (1986). Introduction to Quality Engineering 1th Ed University of California, The Organization.
• Afzalirad, M. & Rezaeian, J. (2017). A realistic variant of bi-objective unrelated scheduling parallel machine problem: NSGA-II and MOACO approaches. Applied Soft Computing 50, 109–123.
• Cota, L., Coelho, V., Guimaraes, F. & Souza, M., (2018). Bi-criteria formulation for green scheduling with unrelated parallel machines with sequence-dependent setup times, Intl. Trans. in Op. Res. 00, 1–22.

Balakrishnan, N., Kanet, J. & Sridharan, S., (1999). Early/tardy scheduling with sequence dependent setups on uniform parallel machines, Computers & Operations Research 26, 127-141.