WSC 2009 Final Abstracts

Sequential Monte Carlo-based Fidelity Selection in Dynamic-data-driven Adaptive Multi-Scale Simulations (DDDAMS)
Nurcin Celik and Young-Jun Son (The University of Arizona)

Abstract:
In DDDAMS paradigm, the fidelity of a complex simulation model adapts to available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. Real-time inferencing for a large-scale system may involve hundreds of sensors for various quantity of interests, which makes it a challenging task considering limited resources. In this work, a Sequential Monte Carlo method (sequential Bayesian inference technique) is proposed and embedded into the simulation to enable its ideal fidelity selection given massive datasets. As dynamic information becomes available, the proposed method makes efficient inferences to determine the sources of abnormality in the system. A parallelization frame is also discussed to further reduce the number of data accesses while maintaining the accuracy of parameter estimates. A prototype DDDAMS involving the proposed algorithm has been successfully implemented for preventive maintenance and part routing scheduling in a semiconductor supply chain.

Development of Web-based Simulator for Supply Chain Management
Donghwa Jeong (Korea university), Minseok Seo (Samsung Electronics co., LTD) and Yoonho Seo (Korea Unversity)

Abstract:
Designing and planning of supply chain network (SCN) is one of the most important decisions to be in dynamic business envirnment. Under today's dynamic circumstances, SCN is too complex to allow realist models to be evaluated analytically. For this reason, we have developed a new simulator. This simulator provides most of the features needed to build a simulation model and can be accessed through the internet. Java Server Page is used to allow users to run this simulator through the internet, and MS-SQL is incorporated to save the computational results and input data.

A Simulation Based Hybrid Algorithm for Yard Crane Dispatching in Container Terminals
Xi Guo, Shell Ying Huang, Wen Jing Hsu, and Malcolm Yoke Hean Low (Nanyang Technological University)

Abstract:
The problem of yard crane dispatching in container terminals is addressed in this paper. We proposed two new hybrid algorithms which combine the advantages of A* heuristic search and Recursive Backtracking with prioritized search order to accelerate the solution process. The algorithms proposed use real time data-driven simulation to accurately predict the time taken by the yard crane in performing its operations and this helps in getting an optimal dispatching sequence that can be followed by the yard crane. Experiments carried out show that the proposed algorithms consistently perform very well over all tested cases. The best performing algorithm is able to find the optimal solution over possible dispatching sequences in about 0.3 to 0.4 seconds under heavy workload. The characteristics of memory saving and interruptibility enable the algorithm to be easily integrated into a complete yard crane management system in real world applications. In such real time yard crane management system, our proposed algorithms can be used as an effective and efficient tool to support complex and intelligent higher level planning in addition to managing the yard crane operations in its appointed zone.

A Simulation Framework to Evaluate Airport Gate Allocation Policies Under Extreme Delay Conditions
Konstantinos Kontoyiannakis, Eduardo Serrano, Kevin Tse, Marcial Lapp, and Amy Cohn (University of Michigan)

Abstract:
Severe weather can lead to significant runway capacity reductions. Runway priority is typically given to inbound flights, thus fewer flights depart and fewer gates become available for arriving aircraft, leading to delays on the tarmac. We provide a simulation-based framework for evaluating gate allocation policies under reduced runway capacity. We first analyze a simple example, demonstrating the complexity of the problem and some key insights into different operating policies. Having shown that even simple scenarios can be difficult (if not impossible) to evaluate in closed form, we turn to simulation. We model the impact of reduced runway capacity on inbound and outbound flights by considering a major U.S. airport and its legacy carrier, focusing on the impact of delays on passengers. The contributions of this work are to highlight the challenges of accurately modeling the impact of runway capacity reductions and to present a simulation-based framework for evaluating operational policies.

The Effect of Customer Segmentation on an Inventory System in the Presence of Supply Disruptions
Yuerong Chen and Xueping Li (The University of Tennessee)

Abstract:
Customer segmentation is an important marketing tool. Effective customer segmentation helps the enterprises increase profits and improve customer service level. On the other hand, due to possible detrimental consequences, supply disruptions have been receiving more and more attention. This paper aims to investigate the effect of customer segmentation on a single-product inventory system in the presence of supply disruptions. The concerned inventory system involves an unreliable supplier, a retailer, and customers. The retailer adopts a continuous-review (s, S) inventory policy. Partial backordering is considered when stockouts occur. This inventory system is simulated. Based on different customer backorder proportions, the effect of customer segmentation on the inventory system is studied under different scenarios about supply disruption severity. The experimental results show that supply disruption duration is an important factor in influencing the effect of customer segmentation on the inventory system. Some managerial insights are also derived from the results.

The Impact of Human Decision Makers’ Individualities on the Wholesale Price Contract’s Efficiency: Simulating the Newsvendor Problem
Stavrianna Dimitriou, Stewart Robinson, and Kathy Kotiadis (Warwick Business School)

Abstract:
Suppliers and retailers in the news vendor setting need to submit their pricing and inventory decisions respectively, well before actual customer demand is realized. In the literature they have both been typically considered as perfectly rational optimizers, exclusively interested in their own respective benefits. Under the above set of conditions the wholesale price only contract has long been analytically proven as inefficient. We asked real human subjects to act as suppliers or retailers in simulation games performed in the laboratory. We found their decisions to significantly deviate from the perfectly rational decisions. By using Agent Based Simulation as the evaluation tool, we investigated the effect of their varying individual preferences on the contract’s efficiency. In doing so we established sufficient evidence that the contract can emerge as efficient, in spite of the underlying strategies’ under-performances. This counter-intuitive result fully supports the contract’s long observed wide popularity.

Simulating Sku Proliferation in a Health Care Supply Chain
Manuel Rossetti (University of Arkansas) and Yanchao Liu (University of Wisconsin)

Abstract:
This paper investigates stock keeping unit (SKU) proliferation and its impact on a health care supply chain. There are two types of proliferation: acceptance and adoption. The acceptance case occurs when a hospital requests a SKU and the SKU is not carried by the system. The adoption case occurs when the SKU is not carried by the requesting hospital, but is carried by some other hospital within the system. A multi-item, multi-echelon supply chain model with load building and delivery details was constructed to examine cost and performance tradeoffs. Object-oriented modeling and implementation techniques for the SKU proliferation simulation are described. The results indicate that the acceptance and adoption jointly affect the performance of the supply chain in terms of the service level (demand fill rate) and the cost. The models and results provide a test bed for addressing and mitigating the effect of proliferation within healthcare supply chains.

Sample Average Approximation Approach to Multi-location Transshipment Problem with Capacitated Production
Deniz Ozdemir (Universidad Autonoma de Nuevo Leon), Enver Yücesan (INSEAD) and Yale T. Herer (Technion)

Abstract:
We consider a supply chain, which consists of N stocking locations and one supplier. The locations may be coordinated through replenishment strategies and lateral transshipments, i.e., transfer of a product among locations at the same echelon level. The supplier has limited production capacity. Therefore, the total amount of product supplied to the N locations is limited in each time period. When total replenishment orders exceed total supply, not all locations will be able to attain their base stock values. Therefore, different allocation rules are considered to specify how the supplier rations its limited capacity among the locations. We team up the modeling flexibility of simulation with sample path optimization to address the multi-location transshipment problem. We solve the sample average approximation problem by random search and by gradient search. With this numerical approach, we can study problems with non-identical costs and correlated demand structures.

Simulation Based Regression Analysis for Rack Configuration of Autonomous Vehicle Storage and Retrieval System
Banu Y. Ekren and Sunderesh S. Heragu (University of Louisville)

Abstract:
In this study, a simulation based regression analysis for rack configuration of an autonomous vehicle storage and retrieval system (AVS/RS) is presented. We develop a mathematical function for rack configuration of an AVS/RS that reflects the relationship between the output (response) and the input variables (factors) of the system. In the regression model, the output is the average cycle time for storage and retrieval and the input variables are the number of tiers, aisles and bays that determine the size of the warehouse. The simulation model of the system is developed using ARENA 12.0, a commercial software. We use MINITAB statistical software to complete the statistical analysis and to fit a regression function. Two different approaches are used for developing the regression analysis stepwise regression and the best subsets. We optimize the regression function using the LINGO software. We apply this approach to a company that uses AVS/RS in France.

Simulation-based Personnel Planning for Materials Handling at a Cross-docking Center under Retail Distribution Environment
Yan Liu and Soemon Takakuwa (Nagoya University)

Abstract:
This study focuses on the personnel planning of materials handling at a real cross-docking center under a retail distribution environment. Materials handling at a cross-docking center is complicated and labour intensive. Managers are usually under enormous pressure to optimize personnel planning to deal with an increasing variety of items handled by different processes. In this study, a flexible and stepwise procedure of personnel planning is proposed to minimize the total personnel expenses at a cross-docking center, especially taking merchandise mixing and operator skills into consideration. The procedure includes adopting simulation together with integer programming. In addition, the procedure is applied to an actual cross-docking center in order to confirm its effectiveness. Furthermore, the proposed method is found to be both practical and powerful to assist logistics managers in their personnel planning efforts.

Simulation for Predictive Control of a Distribution Center
Lourdes A. Medina, R. Ufuk Bilsel, Richard A. Wysk, Vittaldas Prabhu, and A. Ravi Ravindran (The Pennsylvania State University)

Abstract:
In this paper we present the application of Simulation for Predictive Control (SimPC) as a decision making tool for improvement of non-automated distribution centers (DCs). SimPC is focused on determining the viability of a given truck–dock assignment schedule, including arrival times and dock assignments for inbound and outbound trucks. SimPC also serves to perform iterative procedures of system parameter adjustments while searching for a viable schedule. The proposed model utilizes real-time data from DC’s warehouse management system (WMS) to obtain the current state of the DC, which serves as initial conditions for the simulation. The model emulates the decision rules imbedded in WMS, which include as-signing tasks to system-guided resources, selecting storage locations for inbound operations and determining retrieval locations for outbound operations. The SimPC model provides insights for the identification of scheduling problems, guidance for operational and tactical solutions, and serves as a tool to verify these solutions.

A Markov Process Based Dilemma Zone Protection Algorithm
Pengfei Li and Montasir Abbas (Virginia Tech)

Abstract:
Dilemma zone (DZ) is an area at high-speed signalized intersections, where drivers can neither cross safely nor stop comfortably at the yellow onset. The dilemma zone problem is a leading cause for crashes at high-speed signalized intersections and is therefore a pressing issue. This paper presents a novel Markov-chain-based dilemma zone protection algorithm that considers the number of vehicles caught in DZ as a Markov process. The new algorithm can predict the numbers of vehicles in DZ and determine the best time to end the green so as to reduce the number of vehicles caught in DZ per hour. The algorithm was compared to the traditional green extension system and the results showed that the new algorithm was superior.

Techniques for Rapid Initialization in In-vehicle Traffic Simulators
Vishwanath Palagummi, Richard M Fujimoto, and Michael P Hunter (Georgia Institute of Technology)

Abstract:
On-line in-vehicle traffic simulation has been proposed as a means to provide predictions of future states of a traffic net-work based on current traffic conditions. The area covered by an in-vehicle simulation may change dynamically during the vehicle’s journey. This paper is concerned with the issue of initializing the state of new regions that are added to a running microscopic traffic simulation. A warm-up period is required where the new road segments must be populated with simulated vehicles. Techniques that convert vehicle flow rate and queue length information to vehicle positions and speeds are pro-posed to minimize this warm-up period. These techniques are evaluated and compared with respect to estimated travel times of vehicles traveling through different paths in the road network under different traffic conditions. Travel time predictions are compared to a microscopic simulation of the entire region.

Accelerating Traffic Microsimulations: A Parallel Discrete-Event Queue-based Approach for Speed and Scale
Sunil Thulasidasan and Stephan Eidenbenz (Los Alamos National Laboratory)

Abstract:
We present FastTrans -- a parallel, distributed-memory simulator for transportation networks that uses a queue-based event-driven approach to traffic microsimulation. Queue-based simulation models have been shown to be significantly faster than cellular-automata type approaches, sacrificing spatial granularity for speed, while preserving link and intersection dynamics with high fidelity. Significant advances over previous work include the size of the simulated network, support for dynamic responses to congestion and the absence of precomputed routes -- all routing calculations are executed online. We present initial results from a scalability study using a real-world network from the North-East region of the United States comprising over 1.5 million network elements and over 25 million vehicular trips. Simulation of an entire day's worth of realistic vehicular itineraries involving approximately five billion simulated events executes in less than an hour of wall-clock time on a distributed computing cluster. Initial results suggest almost linear speed-ups with cluster size.

An Ant Based Simulation Optimization for Vehicle Routing Problem with Stochastic Demands
Mukul Tripathi, Glenn Kuriger, and Hung-da Wan (University of Texas at San Antonio)

Abstract:
The Vehicle Routing Problem (VRP) is of considerable economic significance in logistic systems as it manages the distribution of goods to make an efficient transportation system. Considering a practical application, this paper solves a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modeled as a stochastic variable as opposed to conventional VRP. To deal with the additional computational complexity, this paper uses a simulation optimization approach to solve the VRPSD. To enhance the algorithm performance, a neighborhood search embedded Adaptive Ant Algorithm (NSAAA), an improved Ant Colony Optimization approach, is proposed. The performance of the proposed methodology is benchmarked against a set of test instances generated using Design of Experiment (DOE) techniques. The results verified the robustness of the proposed algorithm against Ant Colony Optimization and Genetic Algorithm, over which it always demonstrated better results, thereby proving its supremacy on the concerned problem.

An Ant Colony Optimization Approach to Solve Cooperative Transportation Planning Problems
Ralf Sprenger and Lars Mönch (University of Hagen)

Abstract:
In this paper, we suggest efficient heuristics to solve a cooperative transportation planning problem that is motivated by a scenario found in the German food industry. After an appropriate decomposition of the entire problem into sub problems, we obtain a set of rich vehicle routing problems (VRP) including due dates for the delivery of the orders, capacity constraints and maximum operating time window constraints for the vehicles, and outsourcing options. Each of these sub problems is solved by a greedy heuristic that takes the distance of the locations of customers and the time window constraints into account. The greedy heuristic is further improved by applying an Ant Colony System (ACS). The suggested heuristics are assessed in a rolling horizon setting using discrete event simulation. The results of some preliminary computational experiments are provided. We show that the ACS based heuristic outperforms the greedy heuristic.

Applying Decision-oriented Accounting Principles for the Simulation-based Design of Logistics Systems in Production
Niklas Labitzke, Thomas Stefan Spengler, and Thomas Volling (Institute of Automotive Management and Industrial Production)

Abstract:
In this contribution, we focus on the configuration of logistics systems embedded into production processes. To evaluate the dynamic behavior of alternative configurations, Discrete-Event Simulation (DES) proofs helpful. Emphasis is typically put on physical performance measures. However, as configuration decisions have significant financial impact to the firm, an additional monetary impact assessment is usually performed. This requires cost accounting techniques that appropriately incorporate system complexity into the financial model. To this end, we propose a novel approach to extend the applicability of DES for configuration problems. The basic idea is to incorporate technical consumption or engineering production functions into Riebel’s Generic Direct Cost Accounting and to add both methods to a standard DES modeling. Consequently, the informational value of DES is significantly improved. Misleading decision support can be avoided and insights into the relationship between processes and the value structure are achieved. Both of which contribute towards improved configuration decisions.

Verification and Validation Activities Within a New Procedure Model for V&v in Production and Logistics Simulation
Markus Rabe (Fraunhofer IPK), Sven Spieckermann (SimPlan AG) and Sigrid Wenzel (University of Kassel)

Abstract:
Verification and Validation (V&V) of simulation models have been strongly investigated in the context of defense applications. Significantly less substantial work can be found for applications in production and logistics, which is surprising when taking into account the massive impact that wrong or inadequate simulation results can have on strategic and investment-related decisions for large production and logistics systems. The authors have, therefore, founded an expert group for this specific topic in the year 2003. The major result of this expert group was the development of a specific procedure model for V&V in the context of simulation for production and logistics, which was documented in a book (in German) and summarized in a paper at the WSC’2008. This paper explains details of the approach, discussing the criteria to be applied when assessing the validity of a model, and providing examples how to conduct V&V with reference to these criteria.

Yardsim: A Rail Yard Simulation Framework and Its Implementation in a Major Railroad in the U.S.
Edward Lin and Clark Cheng (Norfolk Southern Corporation)

Abstract:
Rail is the most environmentally friendly and fuel efficient mode of freight transportation. Classification yards are railroads?single-most complex operation. Railroad operations are directly influenced by their performance. In this paper, we present a rail yard simulation framework, or YardSim, and describe how it has been used to model a major hump yard on a Class I rail-road in the US. The framework, that models complicated rail yard operations from inbound arrivals, inspection, switching, assembling, brake test, to outbound departures, can be used to pinpoint bottlenecks, identify improvement opportunities in operating practice and yard infrastructure, and assess the impact of changes in traffic volume and service plan. The YardSim provides the flexibility and adaptability to conduct and manage what-if analysis on train plan, yard infrastructure, operating policy, operational strategy, and yard resources such as crews and locomotives. Therefore, the development cycle for a new yard can be reduced significantly.

A Simulation-Based Approach for Estimating the Commercial Capacity of Railways
Giuseppe Confessore and Giacomo Liotta (Institute of Industrial Technologies and Automation - National Research Council of Italy (ITIA - CNR)), Patrizia Cicini and Francesco Rondinone (Rete Ferroviaria Italiana S.p.A.) and Paolo De Luca (ACT Solutions S.r.l. - Analytics and Control Technology)

Abstract:
Rail transport is expected to play a remarkable role for a sustainable mobility in Europe. This paper presents an approach for estimating the commercial capacity of railways. The commercial capacity is intended as the number of possible paths in a defined time window on a rail line, or part of it, considering a fixed path mix, with market-oriented quality. The capacity management is one of the most important tasks of railway Infrastructure Managers. The proposed simulation-based approach relies on the use of an optimizer and a simulator. The study has been developed for the rail line Verona-Brennero, located in the Italian part of the European Corridor Hamburg-Napoli. Computational results allowed to estimate the commercial capacity differences between the whole line and three important line sections within it. Other computational experiments showed the relevant estimated increase in commercial capacity that a reduction in time spacing between trains could imply.

A Dynamic Data-Driven Approach for Rail Transport System Simulation
Yilin Huang and Alexander Verbraeck (Delft University of Technology)

Abstract:
Public rail transport systems concern infrastructure and control strategies with long life spans. While many rail system simulations aim at planning and design, this paper proposes a dynamic data-driven approach to improve the adaptability of the model, hence promoting an extended use of the simulation model. In the proposed approach, the simulation study uses real data streams for automatic model calibration at run-time. For situations that cannot be automated, expert interference can be supported by interactive processes. Different model calibration schemes can be applied to several replications simultaneously to assess the schemes and to determine the parameter values that best match the most recent situation. The model can be fed with data derived from different scenarios, from decision variations or from real-time measurements to accomplish accurate and automated model calibration. This provides a foundation for the use of simulation for railway controller training tools and real-time rail monitoring systems.