SIMULATION: Transactions of The Society for Modeling and Simulation International

Aboueljinane, Lina; Sahin, Evren; Jemai, Zied

doi: 10.1177/00375497221139870pmid: N/A

Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest.

Jahedinia, Fatemeh; Bagheri, Morteza; Naderan, Ali; Bahramian, Zahra

doi: 10.1177/00375497221140918pmid: N/A

Passengers’ safety against unexpected incidents such as rail stations’ fire accidents is essential in the safety field. The presence of luggage with passengers occupies extra space, diminishes passenger’s velocity in high densities, and consequently increases the evacuation time. Therefore, studying the mixture of luggage-laden passengers with non-luggage-laden passengers during the emergency evacuation time of a rail station is vital. In this paper, a simulation of a metro-rail transfer station using an extended cellular automata (CA) model is used to illustrate the importance of this consideration. In this model, luggage-laden passengers and non-luggage-laden passengers are defined as two-cell and one-cell groups, respectively. Specific parameters for luggage-laden passengers in minimum wall prevention and velocity are used. Also, the volume of each passenger group is extracted from the Wi-Fi scanners during the busiest time of the normal station operational hours due to metro and railway schedules. The simulation is carried out using the Python programming language. Fourteen scenarios that vary in their impact on the three classifications of station infrastructure, station equipment, and management’s approach are presented. The analysis indicates that approximately 28% of passengers, or 236 passengers, will not be evacuated in the time period predicted by the simulation if the luggage is not considered. Interestingly, resizing retail stores in the corridor reduced emergency evacuation times by 6.3%, the equivalent of removing them. Failures in the two escalators affect an 8% and 9.4% increase in emergency evacuation time and cause 28 and 46 more passengers to be trapped, respectively. Although the construction of the second railway entrance corridor has been suspended, results indicate that it will save 67 passengers and reduce evacuation time by 9.5%.

Abourraja, Mohamed Nezar; Kringos, Nicole; Meijer, Sebastiaan

doi: 10.1177/00375497221144646pmid: N/A

Container terminals are complex systems where containerized cargo undergoes a set of processing and handling operations to be delivered to their outgoing modes. A pool of decision support methods and simulation models has been developed to assist planners and managers in making decisions about daily operations. Nevertheless, most are designed for a particular terminal and not generic types. Indeed, a generic model serves as a conceptual factory to create specific models which greatly reduces the time and efforts of development; however, building such a model is no mean feat. To this aim, the paper on hand discusses the complexity of applying genericity, flexibility, and modularity in system modeling and proposes a generic architecture to build modular and flexible simulation models for container terminals. This architecture is split into a set of smaller, manageable, well-connected, and generic modules that facilitate the creation of highly parametrized specific models. An illustrative example of the architecture usage is presented in a case study, the new container terminal of Stockholm, and the resulting models were validated by subject matter experts. Finally, to prove its efficiency, a numerical study fed with real data is conducted to investigate the handling capacity of the studied system under different handling and flow scenarios. The obtained results show that the terminal handling capacity can be increased by around 50% if three to four more straddle carriers are added to the existing fleet.

Pearson, Wayne; Islam, Mohammed; Lau, Michael; Gash, Robert; Mills, Jason

doi: 10.1177/00375497221151188pmid: N/A

This paper describes a high-fidelity numerical model that simulates vessel stationkeeping operations in ice-rich waters. The discrete event simulation engine incorporates several novel features, including new ice floe failure models for bow and midships locations; an ice floe creation strategy that facilitates rafting of ice floes; and a vessel thruster model that takes into account physical limitations such as thruster angle slew rates and propeller ramp rates. It accommodates a wide range of ice field specifications and runs in real-time on a standard desktop personal computer (Intel® Core™ i7 Processor or equivalent). The model has been validated using physical measurements of a generic drillship model in several broken ice conditions; it predicted thruster forces and motions that were comparable to those observed during dynamic positioning operations.

Zhang, Rui; Yang, Xiaoxia; Zhou, Meiqi; Li, Yongxing; Yang, Xiaoli

doi: 10.1177/00375497221145645pmid: N/A

The behavior of stair/escalator/elevator selection of passengers at the subway platform could directly affect the travel efficiency and even the service level of the station. This paper proposes a stair/escalator/elevator selection model of passengers at the subway platform, where the cellular automata model is used to simulate passenger movement, and the fuzzy logic theory is used to describe selection behavior. The distance to the stair/escalator/elevator and the density of passengers in front of the facility are selected as the main influence factors. The rationality and effectiveness of the selection model are verified by comparing the field data with simulation results of passengers choosing different stairs/escalators/elevators. The relationship between passenger quantity, passenger speed, escalator quantity, escalator location, and the traffic rate is analyzed based on simulation results, which can provide a theoretical reference for the facility layout optimization at the platform floor of the subway station.