Production planning includes the main steps, such as determining the optimal production, inventory, and other essential production parameters dealing with demand uncertainty during a specific period of planning. Aggregate production planning (APP) is a medium-range network-based production and employment planning that normally spans a time horizon that ranges from 3 to 18 months and is about determining the optimum production quantities, hiring and lay-off rates, workforce and inventory levels, backordering and subcontracting volumes, and so on for each time period within the planning horizon subject to the limitations of available production resources. Such a planning technique typically involves one product or a family of similar products, i.e., products with similarities in the production process, skills required, materials needed, etc., despite minor differences so that considering the problem from an aggregated viewpoint is still valid.The APP problem is one of the most complex issues in operations research due to its large number of decision variables. For this reason, it is necessary to use approximate solution methods to optimize it. Approximate solution methods are some methods that provide solutions with a minimal error by drastically reducing the solution time.

Alireza Goli is currently an assistant professor of the Department of Industrial Engineering and Future Studies Faculty of Engineering at the University of Isfahan, Iran. He received his Bachelor and Master Degree in Industrial Engineering from Golpayegan University of Technology (Iran, 2013) and Isfahan University of Technology (Iran, 2015), respectively. Then, he received a Ph.D. degree in Industrial Engineering from Yazd University (Iran, 2019). Recently, he has been featured among the "World’s Top 2% Researchers/Scientists in 2021" list identified by Elsevier BV, Stanford University. He has published more than 60 papers in high-quality journals and conferences and has been serving as a reviewer in many reputed journals such as IEEE Transactions on Fuzzy System, Journal of Supercomputing, and Annals of Operations Research. He has been serving on the guest editorial board in several journals such as Annals of Operations Research (Springer) and Environmental Science and Pollution Research (Springer). In addition, He has been serving as a reviewer in many reputed journals such as Supercomputing, IEEE Transactions on Fuzzy System, and expert system with application. He is working as a member of the editorial board in different journals like Mathematical Problems in Engineering and Journal of Applied Research on Industrial Engineering. His current research interests include supply chain management, disaster relief optimization, meta-heuristic algorithms, robust optimization, artificial intelligence, and portfolio management.