Q-learning Guided Algorithms for Bi-Criteria Minimization of Total Flow Time and Makespan in No-Wait Permutation Flowshops
Damla Yuksel
PhD Candidate, IE Department, Yaşar University.
Combining deep reinforcement learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions, thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study, we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly, a deterministic mixed-integer linear programming model is provided. Then, Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning and Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning. Moreover, the performance of the proposed Q-learning guided algorithms is compared over a collection of heuristics in the literature. The complete computational experiment, that is performed on the 480 problem instances known as the VRF benchmark set, indicates that the proposed Q-learning guided algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time, both are competitive with each other on the benchmark problems. Among all the features that have been compared, the Q-learning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover (i) the effectiveness of the Q-learning integration into metaheuristics applied for the flowshop scheduling problems, and (ii) many more bi-criteria NWFSPs for revealing the trade-offs between other conflicting objectives, such as makespan & the number of tardy jobs, to overcome various industries' problems.
Damla Yuksel obtained her bachelor’s degree in Industrial Systems Engineering and completed the Double Major Program in International Trade and Finance at Izmir University of Economics, Turkey in 2016. She studied at Maynooth University, Ireland as an exchange student within the scope of the Erasmus + Student Exchange Program. Later, she received her master’s degree in Industrial Engineering at Yaşar University, Turkey in 2019. Currently, she is a Ph.D. candidate in the Industrial Engineering department at Yasar University and focuses on no-wait permutation flowshop scheduling problems in her Ph.D. thesis. She worked as a Research Assistant for five years in the Industrial Engineering department at Yaşar University. Her research interests include scheduling problems, green/energy-efficient scheduling, mathematical modelling, multi-objective optimization, supply chain network designs, and sustainability and circularity in supply chains.
Collaborating with Artificial Intelligence - How Work(ing) with AI Is Changing
Dr. Maarten Renkema
Assistant Professor, HBE Department, University of Twente.
Organizations increasingly use Artificial Intelligence (AI) technologies in both their primary processes as well as for managing employees. The developments in AI technologies open up new possibilities for algorithmic applications that in turn change the world of work and influence organizational practices. Because of these developments, workers increasingly have to deal with AI in their daily work practices. Whereas technological breakthroughs in the past impacted routine work performed by blue collar workers in industrial settings, current AI technologies have become more advanced and thereby also influence knowledge work. Knowledge workers, characterized by the creation, dissemination and application of knowledge, are exposed to novel AI applications, such as Generative AI. Instead of being displaced, knowledge workers will collaborate with AI applications in their work, which has implications for their work characteristics and the quality of work. Given the recent developments in AI, research that is focused on knowledge workers’ experiences in collaborating with AI is limited. For that reason, our SAMKIN research project is focused on examining these experiences. Based on four case studies involving over 80 interviews, we have explored and uncovered how knowledge workers experience this collaboration and how the work of knowledge workers is impacted by the (potential) use of AI.
Maarten Renkema is Assistant Professor at the University of Twente (NL) in the field of Human Resource Management & Innovation. His research focuses on the intersection between HRM, technology and innovation, approached from a multilevel perspective. Particularly he is interested in combining two main areas, (1) employee-driven innovation and (2) innovative and high-tech HRM activities. Research of Maarten has been published in peer-reviewed international journals such as Human Resource Management Review, Creativity & Innovation Management, The International Journal of Human Resource Management, Personnel Review, Journal of Nursing Management and Journal of Organizational Effectiveness: People and Performance. Furthermore, he was involved in organizing and participating several international workshops about multilevel HRM & Innovation.