Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning

Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. Results: The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue

Collaboration in Truck Appointment System in Container Terminals

Due to the continual increase of the global containerized trade, many container terminals face the problem of high demands that their current resource capacity cannot afford. The consequences of such situation are not only the long queues of trucks at the entrance gates and storage yards but also the large turnaround times of trucks. In response, Truck Appointment Systems (TAS) were introduced to schedule truck arrivals in order to alleviate the terminal rush hours, however, the mandatory appointments developed by TASs have a negative impact on the operations as well as resources of the trucking companies. In recent literature, this issue was considered by introducing collaborative TAS in which the trucking companies as well as the container terminals collaborate to set the truck appointments. This work elaborates on the difference between traditional and collaborative TAS and demonstrates how collaborative TAS can improve the performance of the container terminal and reduce the cost of the trucking companies.&nbsp

A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

Background: The slot planning problem is a container allocation problem within a certain location on a vessel. It is considered a sub-problem of a successful decomposition approach for the container vessel stowage planning problem. This decision has a direct effect on container handling operations and the vessel berthing time, which are key indicators for the container terminal efficiency. Methods: In this paper, an approach combining a rule-based fuzzy logic algorithm with a rule-based search algorithm is developed to solve the slot planning problem. The rules in the proposed fuzzy logic algorithm aim at improving the objective function and minimizing/eliminating constraint violation. Results: The computational results of 236 slot planning instances illustrate the efficiency and effectiveness of the proposed algorithm. Conclusions: The results show that the proposed approach is fast and can produce optimal or near-optimal solutions for a comprehensive industrial set of instances

Scheduling External Trucks Appointments in Container Terminals to Minimize Cost and Truck Turnaround Times

Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost

A new two-stage variable neighborhood search algorithm for the nurse rostering problem

The nurse rostering problem refers to the assignment of nurses to daily shifts according to the required demand of each shift and day, with consideration for the operational requirements and nurses’ preferences. This problem is known to be an NP-hard problem, difficult to be solved using the known exact solution methods especially for large size instances. Mostly, this problem is modeled with soft and hard constraint, and the objective is set to minimize the violations for the soft constraints. In this paper, a new two-stage variable neighborhood search algorithm is proposed for solving the nurse rostering problem. The first stage aims at minimizing the violations of the soft constraints with the higher penalty weights in the objective function. While the second stage considers minimizing the total solution penalty taking into account all the soft constraint. The proposed algorithm was tested on the 24 benchmark instances of Curtois and Qu (Technical Report, ASAP Research Group, School of Computer Science, University of Nottingham (2014)). The test results revealed that the proposed algorithm is able to compete with the results of a recent heuristic approach from literature for most of the tested instances

Scheduling External Trucks Appointments in Container Terminals to Minimize Cost and Truck Turnaround Times

Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost