# Special Issue on Scheduling Theory and Algorithms for Sustainable Manufacturing
###### Abstract  
The following Special Issue was initiated at the 10 th IFAC triennial conference MIM 2022 ([[https://hub.imt-atlantique.fr/mim2022/](https://hub.imt-atlantique.fr/mim2022/)]([https://hub.imt-atlantique.fr/mim2022/](https://hub.imt-atlantique.fr/mim2022/)) accessed on 24 December 2024) on Manufacturing Modelling, Management and Control, held in Nantes, France. The authors of the most outstanding papers on the topics of decision aid, combinatorial optimization, and scheduling from the corresponding sessions of the conference were invited to submit extended versions to the present Special Issue. A special focus was placed on how the scheduling theory and algorithms can help to solve complex combinatorial optimization problems in manufacturing from sustainability and related perspectives. Following their presentation at the conference and selection by the session chairs and members of the program committee, as mentioned above, a select group of authors were invited to submit an extended and improved version of their work to this Special Issue. However, the Special Issue was also open to papers that were not presented at the conference if they fell within the scope of the issue and were in relation or complementary to the papers selected at the conference. The motivation of this Special Issue was to present state-of-the art mathematical models and algorithms providing efficient solutions for practical planning and scheduling issues in digital, sustainable, and human-centric manufacturing and logistics [1]. At present, the production and logistics systems for goods and services are faced with both production cost optimization and scarcity of resources, including energy [2]; such systems are becoming increasingly digitalized [3]. Scheduling and algorithms play a central role and offer the possibility to reduce production waste, manage efficiently, and limit the consumption of material resources and energy [4]. An increasing number of scholars are focussing their efforts on applying sustainability criteria when solving planning and scheduling problems in modern digital production environments [5, 6]. Artificial intelligence techniques are often used, taking into account a large amount of available data and the complexity of the problem that needs to be solved. To address this issue, multi-objective optimization approaches have been developed. The majority of studies on the planning and scheduling of production systems and their logistics from sustainability perspectives focus on economic issues and the minimization of electricity consumption and/or carbon emissions. These factors represent the economic and environmental parts of sustainability. Some researchers also integrate social responsibility issues into their work [6]. Various models and algorithms have been developed for planning and scheduling sustainable production systems, with various parameters, objective functions, constraints, model types, and optimization methods employed. Energy consumption and greenhousegas emissions are the most studied factors of sustainability in the current planning and scheduling approaches [7]. The objective of this Special Issue was to complement the existing literature with the most recent research findings and to extend the scope of possible applications and techniques used in the planning and scheduling of production systems and logistics in the digital and AI era. After a rigorous peer review process in accordance with the high standards of the journal, the following two reviews and nine research papers were selected for publication in this Special Issue:  
\"A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective\" by [PERSON], [PERSON], [PERSON], and [PERSON] presents a systematic review of production scheduling based on reinforcement learning techniques covering the period between 1996 and 2024.