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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|9450||2009||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Simulation Modelling Practice and Theory, Volume 17, Issue 4, April 2009, Pages 597–611
This paper introduces a logistics and transportation simulation that can be used to provide insights into potential outcomes of proposed military deployment plans. More specifically, we model a large-scale real-world military deployment planning problem (DPP) that involves planning the movement of military units from their home bases to their final destinations using different transportation assets on a multi-modal transportation network. We apply, for the first time, the event graph methodology and listener event graph object framework to create a simulation model of the DPP. We use and extend Simkit, an open-source Java Application Programming Interface for creating discrete-event simulation (DES) models. We use a medium-resolution modeling approach, as opposed to either high-resolution or low-resolution modeling paradigms, to reduce lengths of simulation runs without compromising reality. To accurately incorporate real and detailed transportation network data into the simulation, we use GeoKIT, a licensed, state-of-the-art, Java-based geographical information system. While our DES model is not a panacea for all, it allows for testing the feasibility and sensitivity of deployment plans under stochastic conditions prior to committing members of the military into harm’s way. The purpose of the paper is to acquaint the readers with the details of the DPP, the simulation model created, and the results of the analysis of a typical real-world case study.
Regional and asymmetric threats and the increase in worldwide terrorist activity have made logistics and mobility increasingly important in our rapidly changing world. This paper focuses on logistics and transportation simulations or computer-based planning tools that are used to provide insight into the potential outcomes of proposed logistical courses of actions prior to and after committing members of the military into harm’s way. Specifically, we deal with the Deployment Planning Problem (DPP), defined and thoroughly described first by Akgün and Tansel . DPP involves positioning of many military units to carry out a mission. During peace-time, military units move from their home bases to their designated destinations using different transportation assets. This movement usually takes place on a multi-modal (land, rail, sea, air, and inland waterways) transportation network. During a crisis, where time is of essence, it has become critical to move soldiers and equipment with limited resources and on a short notice. The movement of the units must conform to a preplanned time-table called time-phased force deployment data (TPFDD). The TPFDD describes, among other things, the initial departure times of military units from their home bases, and their earliest and latest arrival times at their designated destinations. When many units need to deploy, the TPFDD is intended to coordinate their movement in order to efficiently use the existing transportation assets and network. It is also meant to prevent congestion at destinations and transfer points, where mode changes are necessary. Yet, creating TPFDD requires joint work of well-trained logistical and operational planners, and is very time consuming. Military deployment planners need a fast and accurate tool that takes into account the stochastic nature of events to analyze a military deployment plan. A deployment plan may not always go as initially planned. Unexpected breakdown of transportation assets, road traffic accidents, and congestion at transfer points are some of the things that may disrupt a plan. A deployment involves simultaneous movement and utilization of many entities, resources and transportation assets. Thus, a stochastic model is more suitable for this truly hard and real-world problem that deals with expensive military hardware and irreplaceable human life.
نتیجه گیری انگلیسی
In this paper, we have introduced a logistics and transportation simulation developed for use in the military DPP. We applied, for the first time, the EG methodology and LEGO framework to create a multi-modal discrete-event simulation model of the DPP. EGs and LEGOs provided a simple yet powerful and elegant way of representing DES model of deployment, and enabled easy creation of component-based models of a real-world military problem. The medium-resolution used allowed us estimate whether a given plan of deployment will go as intended, and determine prospective problem areas in a relatively short time compared to other existing simulations because of the absence of the need to use several models of differing resolutions in succession. The short run times achieved demonstrated this. The very accurate and detailed GIS data, and the detailed data used in modeling of entities, resources and military equipment in the simulation permitted us not to exchange reality in favor of shorter run times. We had to extend Simkit by writing additional Java classes that are specific to military deployment. The component-based approach adopted in development of our simulation model enables us to easily integrate future additions to our model. These additions may be detailed modeling of infrastructure and resources at transfer points. Our model is generic enough to be used in commercial logistics applications after some problem-specific modifications. Finally, we have simulated a real-world case study to see its robustness under urgent situations. Our simulation provided valuable insights as to when and what percentage of units would be at their designated destinations if the original plan had to be modified for more urgent deployment of military units. This is invaluable information for a commander since a military mission cannot be accomplished and lives could be lost if military units can not arrive at their designated destinations in a timely manner according to their plans.