Batch scheduling is a prevalent policy in many industries such as burn-in operations in semiconductor manufacturing and heat treatment operations in metalworking. In this paper, we consider the problem of minimising makespan on a single batch processing machine in the presence of dynamic job arrivals and non-identical job sizes. The problem under study is NP-hard. Consequently, we develop a number of efficient construction heuristics. The performance of the proposed heuristics is evaluated by comparing their results to two lower bounds, and other solution approaches published in the literature, namely the first-fit longest processing time-earliest release time (FFLPT-ERT) heuristic, hybrid genetic algorithm (HGA), joint genetic algorithm and dynamic programming (GA+DP) approach and ant colony optimisation (ACO) algorithm. The computational experiments demonstrate the superiority of the proposed heuristics with respect to solution quality, especially for the problems with small size jobs. Moreover, the computational costs of the proposed heuristics are very low.

Purpose: Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approach: As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.Findings: The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implications: With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/value: To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Multi Automated Guided Vehicle (multi-AGV) systems are widely used in Work-in-Process (WIP) warehouses to improve the efficiency of material transportation. However, collisions and deadlocks between AGVs are inevitable. Many algorithms have been proposed to solve these problems, but their performance is inefficient in the WIP warehouse due to the lack of consideration of its features. In this paper, to fill the gap between current research and real-world application requirement, we construct a collision and deadlock solving model for a multi-AGV system in a WIP warehouse (CDSMWW). Then we propose a coordination strategy based on dynamic spare point application (DSPA), aiming to improve the efficiency of solving AGV collisions and deadlocks in a WIP warehouse. In this method, the AGV records its predecessor, successor, and reserved points in its local controller and can only enter the set of sequential shared points if its application for spare points is successful. Otherwise, it has to stop to avoid causing an unresolvable deadlock. If a deadlock occurs while this AGV is in the sequential shared points, the AGV moves to the spare point to resolve it. Whenever an AGV leaves the set of consecutive shared points, it releases all its spare points. We apply the algorithm to both the central and local controllers to improve the calculation efficiency. Extensive simulation results demonstrate the feasibility of the DSPA scheme under realistic WIP warehouse environment and its higher efficiency compared to previous state-of-the-art methods.

Motivated by observing real-world instances of multi-aisle automated storage and retrieval systems (AS/RSs) with shared storage, we introduced a new optimization problem called the parallel crane scheduling (PCS) problem. Unlike the single crane scheduling (SCS) problem, the decisions of the PCS problem include not only the request sequencing and storage/retrieval location selection, but also assigning requests to cranes. The PCS problem better reflects the real-life situation, but it is more complex, since these three decisions are interrelated and interact with one another. In this study, since the empty location vacated by any retrieval operation is instantly available, we introduced a new dynamic programming model combined with a mixed-integer linear programming model to describe this complex problem. Considering the feature of location-dependent processing time, we transformed the PCS problem into a variant of the unrelated parallel machine scheduling problem. We developed an apparent tardiness cost-based construction heuristic and an ant colony system algorithm with a problem-specific local optimization. Our experiments demonstrated that the proposed algorithms provide excellent performance, along with the insight that globally scheduling multiple aisles could be considered to reduce the total tardiness when designing an operation scheme for multi-aisle AS/RSs.

Inspired by long-distance road transport in industrial logistics in China, this paper studies a simultaneous loading scheduling and vehicle routing problem over a multi-workday planning horizon. Industrial cargo often requires specialized facilities, and these facilities vary in performance and quantity and are subject to available time constraints. Consequently, achieving coordinated optimization of vehicle routing and loading scheduling becomes a significant challenge in practice. We describe the studied problem as a multi-trip vehicle routing problem with time windows and resource synchronization on heterogeneous facilities. First, we develop a mixed integer programming model in a multi-workday setting to minimize the total travel distance and the number of vehicles. Moreover, a three-phase heuristic approach is developed. An initial solution is constructed using a sequential strategy in the first phase, and then an adaptive large neighbourhood search and a post-optimization procedure based on ejection chains are, respectively, designed to optimize the two hierarchical objective functions. Finally, extensive computational experiments are conducted to demonstrate the effectiveness of the proposed method. Specifically, the research results indicate that in long-distance road transport in industrial scenarios, expanding the planning horizon from a single workday to a multi-workday period could significantly reduce logistics operational costs and improve service quality.