# Special Issue on Scheduling Theory and Algorithms for Sustainable Manufacturing
###### Abstract
This review provides new insights into the use of reinforcement learning in production scheduling and outlines future challenges to be addressed by practitioners and members of the academic community. In the review \"Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models--A Survey\" by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], the authors examine the integration of artificial intelligence and data-driven and machine learning technologies into remanufacturing processes to improve both operational efficiency and sustainability. A comprehensive review of existing knowledge and algorithms is presented and the possibilities to use these techniques in the circular economy are discussed. \"Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies\", presented by [PERSON], [PERSON], and [PERSON], addresses the widely recognized issue of nervousness in master production schedules. Taking into account variations in demand, production schedules change and create instability in the system. The findings presented in this article suggest a product-driven system to complement master production scheduling techniques with intelligent agents to reduce nervousness without significantly increasing production costs. [PERSON] and [PERSON], in their article \"Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm\", propose an improvement to the \"Slime Mold Algorithm\" optimization method along three axes: the definition of a better initial population (based on reverse learning of Bernoulli mapping), an improved mutation strategy, and an optimized boundary-check mechanism. This enhancement allows for faster convergence of the algorithm. Experiments on test functions are conducted to demonstrate the performance and robustness of the approach, which is then applied to the multiprocessor fair scheduling problem to reduce the average execution time on each processor. In the article \"Mitigating Co-Activity Conflicts and Resource Overallocation in Construction Projects: A Modular Heuristic Scheduling Approach with Primavera P6 EPPM Integration\" by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], a heuristic approach for managing complex construction projects is presented, integrating Primavera P6 EPPM and Synchro 4D. The approach enables proactive conflict detection and the resolution of spatial conflicts during concurrent tasks, in addition to resource verification prior to task initiation, thereby ensuring the generation of feasible and conflict-free construction schedules. This approach integrates seamlessly with existing industry tools. In the article \"Towards Sustainable Inventory Management: A Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains\" by [PERSON], [PERSON], [PERSON], and [PERSON], the authors consider the issue of sustainable supply chain management consisting of the optimization of inventory levels in storage facilities, minimizing holding costs, energy consumption, and shortage risk concurrently and thus integrating sustainability considerations into inventory management. The next article, \"Multi-Criteria Decision Support System for Automatically Selecting Photovoltaic Sets to Maximise Micro Solar Generation\", by [PERSON], [PERSON], and [PERSON] takes into account technological advancements that have made solar energy more accessible. The authors propose a multi-criteria decision support system to select the most suitable photovoltaic systems for microgrids, using the AHP and TOPSIS methods. When tested in real-world conditions, the system provided solutions with efficient yields and high internal rates of return. The authors of this article establish a methodological framework and a practical decision tool to enhance the feasibility of solar projects and improve their accessibility.