A discrete event simulation model of the Viennese subway system for decision support and strategic planning

In this paper, we present a discrete event simulation model of the Viennese subway network with capacity constraints and time-dependent demand. Demand, passenger transfer and travel times as well as vehicle travel and turning maneuver times are stochastic. Capacity restrictions apply to the number of waiting passengers on a platform and within a vehicle. Passenger generation is a time-dependent Poisson process which uses hourly origin-destination-matrices based on mobile phone data. A statistical analysis of vehicle data revealed that vehicle inter-station travel times are not time- but direction-dependent. The purpose of this model is to support strategic decision making by performing what-if-scenarios to gain managerial insights. Such decisions involve how many vehicles may be needed to achieve certain headways and what are the consequences. There are trade-offs between customer satisfaction (e.g. travel time) and the transportation system provider’s view (e.g. mileage). First results allow for a bottleneck and a sensitivity analysis.

Increasing capacity utilization of shuttle trains in intermodal transport by investing in transshipment technologies for non-cranable semi-trailers

For shuttle trains with a fixed transport capacity which are the dominant operating form in intermodal transport, increasing capacity utilization is of crucial importance due to the low marginal costs of transporting an additional loading unit. Hence, offering rail-based transport services for non-cranable semi-trailers can result in additional earnings for railway companies. However, these earnings have to compensate for the investment costs of the technology. Based on a dynamic investment calculation, this paper presents a simulation model to evaluate the economic profitability of transshipment technologies for non-cranable semi-trailers from the railway company’s perspective. The results depend on the capacity utilization risk faced by the railway company. In particular, if the railway company does not sell all the train capacity to freight forwarders or intermodal operators on a long-term basis, investing in technology for the transshipment of non-cranable semi-trailers can be economically profitable.

A simulation approach for multi-stage supply chain optimization to analyze real world transportation effects

The cost effective management of a supply chain under stochastic influences, e.g. in demand or the replenishment lead time, is a critical issue. In this paper a multi-stage and multi-product supply chain is investigated where each member uses the (s,Q)-policy for inventory management. A bi-objective optimization problem to minimize overall supply chain costs while maximizing service level for retailers is studied. Optimal parameter levels for reorder points and lot sizes are evaluated. In a first step a streamlined analytical solution approach is tested to identify optimal parameter settings. For real applications, this approach neglects the dynamics and interdependencies of the supply chain members. Therefore a simulation-based approach, combining an evolutionary algorithm with simulation, is used for the optimization. The simulation-based approach further enables the modelling of additional real world transportation constraints. The numerical simulation study highlights the potential of simulation-based optimization compared to analytical models for multi-stage multi-product supply chains.

Evaluation of discovered clinical pathways using process mining and joint agent-based discrete-event simulation

The analysis of clinical pathways from event logs provides new insights about care processes. In this paper, we propose a new methodology to automatically perform simulation analysis of patients’ clinical pathways based on a national hospital database. Process mining is used to build highly representative causal nets, which are then converted to state charts in order to be executed. A joint multi-agent discrete-event simulation approach is used to implement models. A practical case study on patients having cardiovascular diseases and eligible to receive an implantable defibrillator is provided. A design of experiments has been proposed to study the impact of medical decisions, such as implanting or not a defibrillator, on the relapse rate, the death rate and the cost. This approach has proven to be an innovative way to extract knowledge from an existing hospital database through simulation, allowing the design and test of new scenarios.

A structured approach for constructing high fidelity ED simulation

This paper presents a structured approach to building a high-fidelity simulation for an emergency department. Our approach has three key features. First, we use the concept of modules as a building block for modeling. A module is a minimum unit that has clinical or administrative meanings in ED operation, and it consists of low level operational activities. Second, we use a structured template to formally represent modules, and we adopt notations and grammars from the business process modeling notation. This provides an enhanced clarity and transparency, which proves very useful in extracting necessary data from a hospital database or from interviewing ED staff. Finally, we define an interface, specifically data structure and handler, for converting information represented in the modules into simulation languages. This interface makes it possible to seamlessly link the modeling process to the implementation process in the simulation construction.

Hybrid modeling for vineyard harvesting operations

Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.

Towards airspace rules for future UAS-based delivery

The growth of the nascent UAS industry will be affected by the airspace coordination rules between drones because these rules can impact business profitability. Few analyses have been reported to support design of commercial UAS operations in low-altitude commercial urban airspace. Analysis of minimum horizontal separation is critical for designing safe and efficient UAS delivery systems. In this paper a constructive simulation model is used to analyze and evaluate proposed UAS airspace traffic. A high density of delivery drones could create a bottleneck in a drone-based supply chain very quickly, especially when a high minimum horizontal separation standard is required. This paper proposes a simple idea on how to organize low-altitude UAS traffic, and evaluates the idea using a simulation model. Additional implications and future work needed in relation to UAS-based delivery are also discussed.

Analysis of future UAS-based delivery

Commercial use of Unmanned Aerial System (UAS) has the potential to reshape the delivery market and to open new business opportunities to small businesses, e.g., local stores, pharmacies, restaurants, as well as to large international and national businesses and government entities, e.g., Amazon, Google, UPS, power companies, and USPS. Simulation models can examine the value added to current business operations, the effects of radical shifts in current operations, and the formation of new types of businesses. This paper presents an envisioned future UAS delivery business operation models and develops a theoretical constructive simulation model. The conducted simulation analysis based on full factorial design estimated causalities between multiple independent and dependent business and policy factors e.g. drone velocity, flying altitude, number of drones, delivery demand, route type, maximum drone fly-time, number of orders completed, time average drone density, order time, drone utilization, and reachability of customers.

A combined discrete-continuous simulation model for analyzing train-pedestrian interactions

Computer simulation has defined itself as a reliable method for the analysis of stochastic and dynamic complex systems in both academic and practical applications. This is largely attributed to the advent and evolution of several simulation taxonomies, such as, Discrete Event Simulation, Continuous Simulation, System Dynamics, Agent-Based Modeling, and hybrid approaches, e.g., combined discrete-continuous simulation, etc. Each of these simulation methods works best for certain types of problems. In this paper, a discrete-continuous simulation approach is described for studying train and pedestrian traffic interactions for purposes of decision support. A practical operations problem related to commodity train operation within two small towns in Alberta, Canada, is then used to demonstrate the implementation of the approach within the Simphony.NET simulation system. Simulation results generated are presented.

Modeling of healthcare systems: past, current and future trends

Increasing demand for healthcare services, due to changes in demographic shifts and constraints in healthcare funding, make it harder to manage effective, sustainable healthcare systems. Many healthcare modeling exercises have been undertaken with the aim of supporting the decision-making process. This paper reviews all of the 456 articles published by the Winter Simulation Conference over the past 48 years (1967–2015) on the subject of modeling and healthcare system simulation, and analyzes the relative frequency of approaches used. A multi-dimensional taxonomy is applied to encompass the modeling techniques, problem applications and decision levels reported in the articles. One of the most significant changes in the modeling of healthcare systems is the fact that Discrete-event Simulation (DES) is no longer used as an autonomous method, but rather as an integral part of the solution. The mixed-methods, hybrid and multi-paradigm approaches feature strongly in the current direction of modeling in healthcare systems.