Despite a high potential to improve the productivity, quality and safety and also to reduce costs, automated technologies are not widely spread in the construction sector. This paper presents a simulation-based approach to analyze the technical and economic feasibility of wire robots for automated construction in future investigations. Masonry buildings are considered as an appropriate application case due to repetitive construction procedures and high demands concerning accuracy of construction. A simulation model representing the fundamental mechanics of a wire robot is created. Special focus lies on creating collision-free motion profiles which can be exported to the robot control system. BIM models can be used to set-up the simulation model and to prepare the required input data. Following a modular structure, the model can be applied with different purposes in the exploration of the approach. The construction of a one-story masonry building serves as case study proving the concept’s functionality.
Production planning is a complex problem that is typically decomposed into decisions carried out at different control levels. The various methods used for production planning often assume a static environment, therefore, the plans developed may not be feasible when shop floor events change dynamically. In such an operating environment, a system simulation model updated with real-time data can be used to validate a proposed plan. In this paper, we propose a framework to evaluate and validate the feasibility of high-level production plans using a simulation model at a lower level thereby providing a base for improving the upper level plan. The idea is demonstrated with an assembly plant where the aggregate plan is evaluated using discrete event simulation (DES) of shop floor operations with resources allocated according to constraints imposed by the aggregate plan. We also discuss standardized integration interfaces required between simulations and production planning tools.
Time bound sequences are constraints deemed necessary to ensure product quality and avoid yield loss due to time dependent effects. Although they are commonly applied in production system control they cause severe logistical challenges. In this paper, we evaluate the effects of time constraints in combination with batching on a real metallization work center of an opto-semiconductor fab. We use simulation to analyze the impact of these production constraints and point out potentials to increase work center performance. We have a closer look at the required planning horizon, the influence of dedication, the capacity loss due to time bounds and the effects of batching strategies on wafer cost. Our results show the importance to tackle these issues. Furthermore, we will discuss actions taken in response to the experiments.
Developing manufacturing simulation models usually requires experts with knowledge of multiple areas including manufacturing, modeling, and simulation software. The expertise requirements increase for virtual factory models that include representations of manufacturing at multiple resolution levels. This paper reports on an initial effort to automatically generate virtual factory models using manufacturing configuration data in standard formats as the primary input. The execution of the virtual factory generates time series data in standard formats mimicking a real factory. Steps are described for auto-generation of model components in a software environment primarily oriented for model development via a graphic user interface. Advantages and limitations of the approach and the software environment used are discussed. The paper concludes with a discussion of challenges in verification and validation of the virtual factory prototype model with its multiple hierarchical models and future directions.
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.
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.
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.
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.
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.