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We built three simulation models that can assist rail transit planners and operators to evaluate high and low probability rail-centered hazard events that could lead to serious consequences for rail-centered networks and their surrounding regions. Our key objective is to provide these models to users who, through planning with these models, can prevent events or more effectively react to them. The first of the three models is an industrial systems simulation tool that closely replicates rail passenger traffic flows between New York Penn Station and Trenton, New Jersey. Second, we built and used a line source plume model to trace chemical plumes released by a slow-moving freight train that could impact rail passengers, as well as people in surrounding areas. Third, we crafted an economic simulation model that estimates the regional economic consequences of a variety of rail-related hazard events through the year 2020. Each model can work independently of the others. However, used together they help provide a coherent story about what could happen and set the stage for planning that should make rail-centered transport systems more resistant and resilient to hazard events. We highlight the limitations and opportunities presented by using these models individually or in sequence.

An optimization model is proposed to keep ports and waterways under surveillance against threats from divers, torpedoes or explosives mounted on the hull of vessels. A sonar sensor is utilized for under water detection. The proposed model addresses two key concepts of sensor placement which are mostly ignored in the literature. These features are “multiple coverage” meaning that the detection probability in a section increases as the number of sensors covering that section increases and “range-dependant detection probability” implying that the detection probability decreases as the distance from a sensor increases. The proposed mathematical model is a mixed integer linear programming model. A greedy heuristic approach reaches near-optimal solutions for large scale scenarios. Numerical results are provided to show the accuracy and speed of the heuristic algorithm. It turns out that the algorithm performs reasonably well when the sonars are scarce in the region of interest. This is an advantage for using the heuristic method in real world scenarios, as the density of sonars is low in these scenarios due to the budget limitations.

Over the last decade, supply chain coordination has attracted much attention in the context of industrial logistics systems, because of the role played in production support logistics. There is a growing interest in the analysis and measurement of performances, because performance analysis allows us to build supporting decision tools for the design, planning and management of the supply chain. This paper presents an analytical approach for performance analysis based on a decomposition principle. The approach is applied to a serial supply chain that consists of two levels of suppliers, a production plant and a distribution centre, and operates as a pull system. This configuration is typical for bulk materials such as grain. The method allows us to approximately calculate performance indicators (such as customer satisfaction and inventory levels) taking into account all the interactions between the different echelons of the supply chain. The accuracy of the approximations is confirmed by the results obtaine...

This paper deals with simulation modeling of the vessel traffic in Delaware River. The purpose is to study the impact of deepening on the navigational efficiency in the River. In this regard, vessel calls to terminals, lightering and barge operations, tidal and navigational rules in the River, terminal and anchorage properties as well as vessel profiles are considered in the model. The simulation model is specifically built to be able to perform scenario and policy analyses as well as a comprehensive risk analysis of the Delaware River and Bay area. This paper investigates effects of deepening on port performance measures. The statistics tracked in this respect are the overall port and terminal utilization, port times and terminal calls, anchorage visits and delays based on various vessel visits, categories and movements.

We consider the value of decision support tools for passenger rail system managers. First, we call for models that follow events along main rail lines and then into the surrounding environment where they can cascade onto connected light rail, bus, auto, truck, and other transport modes. Second, we suggest that both probabilistic risk assessment (PRA-based) and agent-based models have a role to play at different scales of analysis and for different kinds of risks. Third, we argue that economic impact tools need more systematic evaluation. Fourth, we note that developers of decision support tools face a challenge of balancing their desire for theoretical elegance and the tendency to focus only on high consequence events against decisionmakers' mistrust of complex tools that they and their staff cannot manage and incorporate into their routine operations, as well as the high costs of developing, updating, and applying decision support tools to transport systems undergoing budget cuts and worker and service reductions. Language: en

The Strait of Istanbul, the narrow waterway separating Europe from Asia, holds a strategic importance in maritime transportation as it links the Black Sea to the Mediterranean. It is considered as one of the world's most congested and difficult-to-navigate waterways. Over 55,000 transit vessels pass through the Strait annually, roughly 20% of which carry dangerous cargo. In this study, we have analyzed safety risks pertaining to transit vessel maritime traffic in the Strait of Istanbul and proposed ways to mitigate them. Safety risk analysis was performed by incorporating a probabilistic accident risk model into the simulation model. A mathematical risk model was developed based on probabilistic arguments regarding instigators, situations, accidents, consequences, and historical data, as well as subject-matter expert opinions. Scenario analysis was carried out to study the behavior of the accident risks, with respect to changes in the surrounding geographical, meteorological, and traffic conditions. Our numerical investigations suggested some significant policy indications. Local traffic density and pilotage turned out to be two main factors affecting the risks at the Strait of Istanbul. Results further indicate that scheduling changes to allow more vessels into the Strait will increase risks to extreme levels. Conversely, scheduling policy changes that are opted to reduce risks may cause major increases in average vessel waiting times. This in turn signifies that the current operations at the Strait of Istanbul have reached a critical level beyond which both risks and vessel delays are unacceptable.

A multi-stage production/inventory system with decentralized two-card kanban control policies producing multiple product types is considered. The system involves one-at-a-time processing at each stage, finite target levels in the buffers and batch transfers between production stages. The demand process for each product is assumed to be Poisson and excess demand is backordered. Products have a priority structure and the processor at each stage is shared according to a switching rule. Our objective is to obtain steady-state performance measures such as the average inventory and service levels for each product type. We propose an approximation algorithm based on: (i) characterization of the delay by a product type before receiving the processor's attention at each stage; and (ii) creation of subsystems for all the storage activity and phase-type modeling of the remaining system's behavior. Numerical examples are presented to show the accuracy of the method and its potential use in system design.

Manufacturing-related models have traditionally made independence assumptions on associated stochastic processes in order to achieve tractability of analytical models and simplify Monte Carlo models. This paper aims to alert users to potential deleterious implications stemming from unfounded independence assumptions in traditional stochastic models of manufacturing systems. Specifically, it demonstrates the dramatic impact that appreciable autocorrelations can have on manufacturing performance measures through a preliminary study of prediction errors incurred in ignoring dependence. To this end, the study compared performance measures of common manufacturing models with renewal components to their autocorrelated counterparts, drawn from the TES (Transform-Expand-Sample) class. TES models constitute a versatile class of stochastic processes, designed to capture empirical distributions and autocorrelations, simultaneously, and as such, are suitable for both Monte Carlo simulation and analytical modeling of autocorrelated time series. A brief overview of simple TES processes and their generation algorithms is also included.

A multiple-product facility consisting of a manufacturing process and a finished product warehouse for several types of products is considered. An (R,r) continuous-review inventory control policy is applied to each type of product in the warehouse. The demand arrival process of each type is assumed to be Poisson. The processing times and the set-up times are arbitrarily distributed. We have developed an iterative procedure to approximately compute the average inventory level of each product in the warehouse under different priority schemes.

We summarize the findings of a recent bench marking study carried out in the pharmaceutical industry to assess and compare the manufacturing productivity of U. S., Japanese, and European manufacturers.

A production/inventory system consisting of multiple stages in series with arbitrary processing times and with intermediate buffer inventories is considered. There is a finished product warehouse at which demand occurs for the finished products according to a compound Poisson process. Inventory levels of both intermediate and finished stocks are controlled by a continuous-review (R, r) inventory control policy. Production at a stage continues until the inventory level in the downstream buffer reaches its target value R. Once the inventory level drops to its reorder level r, the stage undergoes a set-up and production resumes. An approximation method is developed to obtain the steady-state performance measures of the system. Numerical examples are presented to show the accuracy of the proposed method.

This paper overviews the pharmaceutical industry from a manufacturing perspective. Summarized is the current state of the industry within the U.S. as well as in other countries with significant pharmaceutical manufacturing activity. Various classifications of pharmaceutical products and processes, the concept of biotechnology, and the role of the FDA are briefly introduced. Results of a recent benchmarking study and research issues related to pharmaceutical manufacturing are presented.

A production/inventory system consisting of a single-stage facility producing multiple product types and a warehouse is considered. The demand process is assumed to be Poisson and the excess demand is backordered. Products have a priority structure and the processor is shared according to a switching rule. (R, r) continuous-review inventory control policies are used to start and stop production of each product. Production of a product continues until its target level is reached. Then, a switch-over takes place to the highest priority product among the ones that need the processor's attention. A set-up takes place every time a switch-over occurs. Both processing and set-up times are assumed to have arbitrary distributions. The number of switch-overs among the high priority products is restricted in case low priority products need attention. The objective is to obtain performance measures such as the average inventory and backorder levels as well as die probability of having a backorder upon arrival for eac...

Manufacturing facilities in a host of industrial sectors are structured as a series of production stages. Jobs in a variety of forms are transferred from one stage to another to be processed in a prescribed order; these jobs eventually leave the system as finished or semifinished products. Such systems are referred to as production lines. The flexibility afforded by computer-controlled machinery enables production lines to handle a broad range of operations. Various operations permit the deployment of a sequence of intelligent workstations on the shop floor for processing or assembling various products. The chapter discusses simulation modeling of workstation sequences in the production line context. Design problems in production lines are primarily resource allocation problems. These problems include workload allocation and buffer capacity allocation for a given set of workstations with associated processing times. Generally, design problems are quite difficult to solve in manufacturing systems. This is partly because of the combinatorial nature of such problems.

A supply chain system (supply chain, for short) is a network that mediates the flow of entities involved in a product life cycle, from production to vending. Such a network consists of nodes and arcs. Nodes represent suppliers, manufacturers, distributors, and vendors (e.g., retail stores), as well as their inventory facilities for storing products and transportation facilities for shipping them among nodes. Arcs represent routes connecting the nodes along which goods are transported in a variety of modes (trucking, railways, airways, and so on). From a sufficiently high vantage point, supply chains have essentially a feed-forward network structure, with upstream and downstream components arranged in echelons (stages), such that raw materials, parts, products, and so on flow downstream, payments flow upstream, and information flows in both directions. However, there are supply chains where entity flows may not be strictly directional because products may be returned for repair or refund. This chapter focuses on inventory management and material flow among echelons.

Monte Carlo simulation is an invaluable tool for studying transportation systems and solving their attendant problems. Generally, strategic decisions concerning transportation systems often involve identifying system capacities and eliminating bottlenecks so as to increase system throughput. The associated operations are extremely expensive, and therefore need to be studied carefully before any action is taken. Fortunately, simulation studies can be helpful and even instrumental in identifying correct designs for large-scale transportation projects. Transportation-related simulation models employ entities to model the moving objects, such as cars, trucks, trains, planes, ships, tugboats, humans, and so on. Contention for transportation-oriented service by entities is modeled through resources, such as road pavement, traffic lights, tollbooths, airport taxiways and runways, and berths and piers, as well as material handling equipment. To make modeling more realistic, resources may experience stoppages for various reasons (failures, weather, strikes, etc.), which can further exacerbate delays in the system. This chapter provides an overview of Arena facilities for modeling transportation systems. Their use is illustrated in a number of transportation-related examples.

A two-valve fluid-flow system with finite storage in between is considered. Valves are subject to failures with exponential up times and phase type repair times. We have developed a continuous time Markov Chain approach to study the steady-state behavior of the valves and the material in the storage. The Markov chain has both continuous and discrete states. We have constructed a system of linear equations for the boundary states and a set of differential equations for the interior states, that are solved using the eigenvalue method. We have also investigated the impact of down time variability on the system performance metrics.

A production/inventory system consisting of work stations in series and a finished product warehouse is introduced. Bounds for the throughput are developed through the analysis of the reversed system of tandem queues. Examples are provided to show the bounds.