The quadratic assignment problem (QAP) is one of the most important combinatorial optimization problems in theory and practice. In this paper, we are proposing a hierarchical data-driven parallel memetic algorithm to solve the problem. A component-based hierarchical system architecture with a...

Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data. In healthcare, biases have marked medical history, leading to unequal care affecting marginalised groups. Patterns in missing data often reflect these group discrepancies, but...

Real-life problems are complex because they are typically made up of several sub-problems interacting. These interactions establish an interdependent relationship in which the quality of one partial solution impacts the quality of other partial solutions. Recently, a new benchmark known as the...

This paper tackles the green bike-sharing reposition problem (GBSRP) considering the reposition quality and carbon emission during the reposition simultaneously. With the expanding scale of today’s bikesharing system (BSS), the environmental pollution during the reposition becomes obvious....

To solve the technician scheduling problem, this paper establishes a technician scheduling model considering the task release time, and designs a branch delimitation algorithm to solve the problem accurately by fully utilizing each technician's skills and allocating limited human resources to...

The joint replenishment problem is a fundamental model in supply chain management theory that has applications in inventory management, logistics, and maintenance scheduling. In this problem, there are multiple item types, each having a given time-dependent sequence of demands that need to be...

Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost-related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modeling-based...

Effective scheduling of twin automated stacking cranes (ASCs) in automated storage yard is critical to maximize operational efficiency. While Deep Reinforcement Learning (DRL) is promising in solving NP-hard scheduling problems, twin ASCs scheduling is challenging due to its unique properties...

It is important for companies to select the right suppliers to deal with in order to avoid any disruptions. Considerable research has been conducted to find the solution for this problem. Previous attempts were made to account for uncertainties and disruptions in decision-making in order to rank...