Abdellah El Fallahi

As the port operations are becoming volatile and uncertain, container handling in the port terminals is becoming more complex due to the rising customer demands. Further, scheduling interruptions create ambiguity in real-time operations. Therefore, the present study focused on the container terminal’s ability to schedule and mitigate real-time probabilistic fluctuations. This article proposes a novel proactive–reactive scheduling approach to solve the quay crane (QC) scheduling problem in the volatility, uncertainty, complexity, and ambiguity world, using equipment reliability functions. The proposed approach is based on a novel reliability assessment formula based on practical facts, including the reliability probabilistic distribution of QC and the container workload variability. Moreover, a proactive iterated local search algorithm is proposed and incorporated with the reliability formula to solve the QC scheduling under fluctuations. The efficiency of the algorithm is illustrated through comparison to an exact method. Real-time reactive simulations that compare the schedules before and after the Go-Live phase show the noteworthy improvement derived from generating plans using the proposed proactive approach compared to an alternative nonproactive one.

In container terminals (CTs), the performance of quay cranes (QCs) is extremely impacted by their operators’ productivity, which makes the QC worker assignment a relevant element in the QC scheduling. Nevertheless, in the literature, this problem was tackled without considering the objectives of balancing operators’ workload and minimising the distance required to move between QCs, even though practitioners underline their importance in maintaining an efficient working environment. Accordingly, and based on a real-case study raised by a CT partner, this paper addresses a novel problem referred to as the travelling QC worker assignment and balancing problem. First, we propose a novel mathematical model with original constraints faced by the company to assign operators to QCs on a daily basis. Second, to find approximate solutions in a reasonable time, a constructive heuristic and an iterative local search (ILS) are proposed and tested on real datasets. We also propose a simulation-optimisation mechanism to evaluate the robustness of the solutions under real-time probabilistic perturbations. The proposed algorithms are integrated into a decision support tool, a network-linked application allowing users in a real CT to generate automatic assignments. The proposed simulation-optimisation procedure helps obtain balanced planning, avoid workload conflicts, and increase productivity.

The efficient management of maritime logistics operations improves the performance of global supply chains. An important issue in container terminal operations is the scheduling of quay cranes (QCs), which is affected by the productivity rate of QCs. This productivity rate depends on the type of tasks to be completed by QCs on any given vessel. In this paper, we propose an artificial neural network (ANN) model with a variable neighbourhood search (VNS) as a training algorithm to build a productivity rate predictive model. This model considers several predictors depending on the type of containers in the vessel and the expected equipment downtime. We also study how QC scheduling is impacted by the productivity rate of QCs. The proposed predictive model and a moving average model are used to estimate the productivity rate, which is then taken as input to the QC scheduling optimisation model. Results show that when the proposed predictive model is used, QC schedules are closer to those generated using real data obtained from our container terminal partner.

Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed.