論文

Analyzing Emergency Evacuation Strategies for Mass Gatherings using Crowd Simulation and Analysis framework: Hajj Scenario


Hajj is one of the largest mass gatherings where Muslims from all over the world gather in Makah each year for pilgrimage. A mass assembly of such scale bears a huge risk of disaster either natural or man-made. In the past few years, thousands of casualties have occurred while performing different Hajj rituals, especially during the Circumambulation of Kaba (Tawaf) due to stampede or chaos. During such calamitous situations, an appropriate evacuation strategy can help resolve the problem and mitigate further risk of causalities. It is however a daunting research problem to identify an optimal course of action based on several constraints. Modeling and analyzing such a problem of real-time and spatially explicit complexity requires a microscale crowd simulation and analysis framework. Which not only allows the modeler to express the spatial dimensions and features of the environment in real scale, but also provides modalities to capture complex crowd behaviors. In this paper, we propose an Agent-based Crowd Simulation & Analysis framework that incorporates the use of AnyLogic Pedestrian library and integrates/interoperate AnyLogic Simulation environment with the external modules for optimization and analysis. Hence provides a runtime environment for analyzing complex situations, e.g., emergency evacuation strategies.

Agent-Based Simulation Modeling of a Bus Rapid Transit (BRT) Station Using Smart Card Data


A Bus Rapid Transit (BRT) station with multiple loading zones tends to have a longer passenger-bus interface and, thus, lead to longer passenger walking times and longer bus dwell times than ordinary bus stops. As a way to reduce bus dwell times in a BRT station, this study focuses on eliminating delays in passengers’ reaction to their desired bus by designing an improved passenger information system (PIS) that can increase passengers’ certainty about the bus stopping location. This study develops an agent-based simulation model based on observations from a BRT station in Brisbane, Australia to reflect a real BRT operations and passenger flows. The input parameters for the simulation model are calibrated with actual data including smart card records, field measurements, and video recordings. After mapping passenger moving and waiting patterns, and allocation logic of bus loading areas, various what-if analyses can be performed to design better passenger information systems.

Simulating Recovery Strategies to Enhance the Resilience of a Semiconductor Supply Network


Enhancing supply chain resilience is of vital importance in today’s business to manage and mitigate the risks, especially in the semiconductor industry challenged with intrinsic long cycle times and short product life-cycles. Transferring production from a primary site to an alternative site after a disaster is one of the strategies to ensure resilience of the supply network. In this study, different types of alternative sites with various levels of preparedness are investigated. A discrete-event simulation is used to evaluate their operational and financial impacts under four different disruption scenarios. The simulation outcomes demonstrate unexpected positive benefits of various alternative sites in terms of fast recovery and resilience building.

Modeling and Simulation of Port-Of-Entry Systems


This paper describes a suite of simulation models for Port-of-Entry systems, dubbed POESS (POE Simulation System). Port-of-Entry Simulation System was developed with the support of the U.S. Department of Homeland Security (DHS) for use primarily by the U.S. Customs and Border Protection (CBP) agency. Port-of-Entry Simulation System aims to assist CBP in Port-of-Entry design and operational decision making. A Port-of-Entry Simulation System simulation model of the Bridge of the Americas (BOTA) POE, located at El Paso, Texas, is described as an example.

Aircrew Manpower Supply Modeling Under Change: an Agent-Based Discrete Event Simulation Approach


This paper deals with manpower planning using a dynamic and interactive simulation system that is agile and adaptive to robustly accommodate change — without requiring a complete rewrite. The simulation architecture extends the current hybrid modelling paradigm which integrates agent based (AB) constraints and controls with a discrete event simulation (DES) methodology. This allows for a more expressive, authentic representation of both process flows and agent policies that captures the advantage of system dynamics (SD) modelling by integrating agile controls with response feedback. This approach is inspired by the need to develop an aircrew training pipeline simulation for the Australian Defence Force (ADF) that supports the real needs for strategic manpower planning in a context of policy and requirements change management. A case study is provided to illustrate the challenges and approach.

Optimizing Production Allocation with Simulation in The Fashion Industry: a Multi-Company Case Study


Production Planning and Control (PP&C) has been deeply analyzed in the literature, both in general terms and focusing on specific industries, such as the fashion one. The paper aims to add a contribution in this field presenting an optimization model for the Fashion Supply Chain (FSC), developed considering an interdependent environment composed by a group of focal companies that work with both exclusive and not-exclusive suppliers. The proposed framework will combine simulation and optimization models based on parameters, decision variables, constraints and Objective Functions (OFs) collected through a literature review. The framework has been developed in a parametrical way, in order to fit the peculiarities of the different actors operating along the FSC. The empirical implementation of the framework has been conducted using data coming from fashion companies belonged to the same network, considering rush orders as stochastics events for the scenario analysis and Key Performance Indicators (KPIs) assessment.

A Simulation-Based Approach for an Effective AMHS Design in a Legacy Semiconductor Manufacturing Facility


This paper addresses the design of an Automated Material Handling System (AMHS) for wafer lots in the photolithography workshop of a 200mm wafer manufacturing facility (fab) that was not initially built to have such a system. Lots transportation has to be performed using an Overhead Hoist Transport (OHT) system that was already chosen to transport reticles in the workshop. The main objective is to propose a decision support tool to characterize the Automated Material Handling System elements including lot handling, transportation as well as the storage space design. A simulation-based approach is proposed to evaluate different scenarios and propose an effective Automated Material Handling System design. Experimental results based on real instances confirm the capability of the proposed Automated Material Handling System design to support the workshop activity.

On Agent-Based Modeling in Semiconductor Supply Chain Planning


Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a supply chain is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining supply chain planning performance. Our paper argues for an agent-based approach to understand and improve supply chain planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.

A Case Study for Simulation and Optimization Based Planning of Production and Logistics Systems


This paper introduces a practical approach for the comprehensive simulation based planning and optimization of the production and logistics of a discrete goods manufacturer. Although simulation and optimization are well-established planning aides in production and logistics, their actual application in the field is still scarce, especially in small and medium-sized enterprises (SMEs). This is largely due to the complexity of the planning task and lack of practically applicable approaches for real-life planning scenarios. This paper provides a case study from the food industry, featuring a comprehensive planning approach based on simulation and optimization. The approach utilizes an offline-coupled multilevel simulation to smooth production and logistics planning via optimization, to optimally configure the production system using discrete-event simulation and to optimize the logistics network utilizing an agent-based simulation. The connected simulation and optimization modules can enhance the production logistics significantly, potentially providing a reference approach for similar industry applications.

Simulation of The Order Process in Maritime Hinterland Transportation: The Impact of Order Release Times


The integration of information systems between the various actors organizing and executing the transport of containers to seaports is slowly progressing. Transport orders are frequently characterized by high change rates causing high manual revision effort for dispatchers. Therefore, these order changes, often received shortly before the day of departure, raise the question regarding the immediate transmission of transport orders to the subsequent actors in the transport chain. This paper analyzes the impact of different order release times, which define the timing of order transmission, on order process efficiency (processing times and costs) using a multi-method simulation approach. In a case study, four actors, two focusing on transport planning and two on operative transport execution, are considered. The simulation experiments with varying order release times and change rates reveal: A late release of orders from planning to operative actors and a reduction of order changes can significantly increase order process efficiency.