SIMULATION: Transactions of The Society for Modeling and Simulation International

Tseng, Shih-Hsien; Aldi Winata, Oey

doi: 10.1177/00375497251326918pmid: N/A

The steel industry plays a crucial role in global economic development, involving complex production and logistics processes. Effective transportation management, particularly crane utilization, is essential to maintaining smooth operations. However, inadequate planning regarding crane scheduling can lead to bottlenecks, reducing production efficiency. Despite its importance, the role of transportation in enhancing production flow remains underexplored. This study employs simulation modeling using Simio to analyze the production and transportation system of a steel company in Taiwan. Real-world data on production stages, transportation routes, and operational constraints are integrated into the model. Through various what-if scenarios, the impact of crane availability on production performance is evaluated. Key performance indicators such as throughput, station occupancy, and crane utilization rates highlight significant overuse of specific cranes, leading to inefficiencies. To address these bottlenecks, the model is iteratively adjusted by increasing the number of cranes. The results indicate that deploying six cranes, ensuring none exceeds 70% utilization, enhances throughput and balances production flow. This configuration reduces operational risks, minimizes downtime, and enhances overall efficiency. The study underscores the importance of integrating transportation planning into production management, providing valuable insights for improving operational decision-making in the steel industry.

Yang, Yuxin; Altarawneh, Lubna; Alattar, Mohammad Sa’eed; Farrag, Abdelrahman; Kwon, Soongeol; Jin, Yu

doi: 10.1177/00375497251328047pmid: N/A

Efficient production planning is a critical and challenging task in Make-To-Order (MTO) Automated Manufacturing Systems (AMSs), requiring a flexible production process capable of managing large volumes of highly-customized orders while preventing resource contention. Considering the timing of customers’ needs and the availability of production resources, it becomes important to find an efficient order-dispatching sequence to optimize the coordination across multiple production units. To achieve this, a simulation model is essential to evaluate and validate the proposed algorithm’s performance prior to real-world implementation. In this study, a heuristic algorithm based on a Threshold- and Priority-Based Dispatching Rule (TPDR) is presented aimed at minimizing flow time while avoiding potential deadlocks and meeting key performance indicators (KPIs). The proposed heuristic is integrated into a discrete-event simulation (DES) framework, allowing for dynamic adjustments to the dispatching sequence of high-volume and highly-customized orders based on real-time system/machine performance. To assess its effectiveness, a case study of a Mail Order Pharmacy Automation (MOPA) system is conducted within three DES models, comparing the proposed TPDR-based heuristic with three widely used dispatching rules. The simulation results demonstrate that the TPDR-based heuristic algorithm significantly enhances productivity and eliminates production bottlenecks while maintaining throughput levels.

Zhao, Shengwen; Zheng, Liang; Bao, Ji; Chen, Yanzhan; Zhang, Shuaichao

doi: 10.1177/00375497251331487pmid: N/A

This paper proposes a real-time coordinated ramp metering (RCRM) method to simultaneously maximize the number of vehicles entering the expressway mainline from on-ramps and space mean speed of the expressway mainline. This method applies a proportional-differential (PD) controller to adjust vehicular flow entering the expressway mainline from on-ramps. It also utilizes shockwave analysis to dynamically determine the upstream on-ramps that have to be coordinated. In order to ensure the RCRM method can withstand traffic demand uncertainty in real-time, we establish a ramp metering stochastic simulation-based optimization (RMSSO) model to fine-tune the weighting coefficients for on-ramps and PD gains and solve it by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Simulation experiments in Edmonton show that the optimized RCRM schemes improve the space mean speed of the mainline by around 40% almost without sacrificing the number of vehicles entering the mainline from on-ramps. The outperformance and robustness of the optimized RCRM scheme by BOSPAS are also validated under traffic demand uncertainties.

Hocaoğlu, Mehmet Fatih

doi: 10.1177/00375497251333382pmid: N/A

This paper introduces a novel simulation modeling framework that seamlessly integrates Petri nets with logic programming, resulting in intelligent and adaptable simulation models capable of handling both discrete and continuous events. Instead of treating systems as standalone Petri nets, this methodology embeds Petri net semantics within simulation models, creating a hybrid state vector that incorporates attributes from both the Petri net and model-specific states. In addition, the modular design of the model supports high reusability and scalability, making it applicable across various domains, including industrial automation and workflow management. Embedding Petri nets within simulation entities enhances dynamic decision-making and specialized time-management capabilities. This approach reduces errors associated with fixed time-step sizes, thereby improving the accuracy of continuous event simulations. The solution introduces the external transition concept for Petri nets and implements a time-management approach based on qualitative landmarks and mathematically meaningful points, effectively minimizing errors caused by simulation time-step variations. To demonstrate the framework’s versatility and effectiveness, six example applications illustrate how combining Petri nets with logic programming enhances the analytical power and flexibility of traditional simulation models. Ultimately, this paper establishes a comprehensive hybrid simulation framework that unifies discrete and continuous analysis, endows simulation entities with reasoning capabilities through logic programming, and provides robust modularity.

Staff, Marta E; Mustafee, Navonil

doi: 10.1177/00375497251334387pmid: N/A

Perishable products are associated with a limited shelf-life, and their efficient management often requires close matching of supply with demand. Due to the inherent uncertainty in supply chains, determining stock reordering points and issuance policies is challenging. Tools and techniques from Operations Research/Management Science (OR/MS) support decision-makers in making well-informed decisions related to perishable inventory management. Among the plethora of OR/MS methods, discrete-event simulation (DES) is well suited for studying inventory systems, as this typically models products moving in and out of storage within a stochastic supply chain environment, and in the case of perishable goods, enabling age tracking of products. This paper presents a literature review of DES applied to perishable inventory management. Our base set of literature consists of 25 papers retrieved through searches of scholarly databases. Notably, our review highlights that fields such as the pharmaceutical, organ donation, and floral and horticultural supply chains are relatively underexplored. Furthermore, while most modeling studies consider uncertainty on the demand side, uncertainties related to lead time, yield, or product lifetime have not been modeled to a great extent. Our review is a key source of literature for researchers and practitioners on the current state-of-the-art in DES modeling for perishable inventory; it identifies research gaps and provides directions for future research.