Academic articles

Production plans are based on fair assumptions of process performance and all operation parameters are taken as averages. There are a number of events that happen in any manufacturing setup during the course of production like periodic delivery of raw materials or changeovers on a machine. The interaction between these events is non-linear and cannot be easily visualized. As a result of which most of the production plans in any company have only a limited realization. This paper provides an example of how simulation using AnyLogic has been applied in one such plant scenario to visualize the plan outcome.

The recent circular economy movement has raised awareness and interest about untapped environmental and economic potential in the manufacturing industry. One of the crucial aspects in the implementation of circular or closedloop manufacturing approach is the design of circular products. While it is obvious that three post-use strategies, i.e., reuse, remanufacturing, and recycling, are highly relevant to achieve loop closure, it is enormously challenging to choose “the right” strategy (if at all) during the early design stage and especially at the single component level. One reason is that economic and environmental impacts of adapting these strategies are not explicit as they vary depending on the chosen business model and associated supply chains. In this scenario, decision support is essential to motivate adaptation of regenerative design strategies. The main purpose of this paper is to provide reliable decision support at the intersection of multiple lifecycle design and business models in the circular economy context to identify effects on cost.

This paper deals with manpower planning using a dynamic and interactive simulation system that is agile and adaptive to robustly accommodate change — without requiring a complete rewrite. The simulation architecture extends the current hybrid modelling paradigm which integrates agent based (AB) constraints and controls with a discrete event simulation (DES) methodology. This allows for a more expressive, authentic representation of both process flows and agent policies that captures the advantage of system dynamics (SD) modelling by integrating agile controls with response feedback. This approach is inspired by the need to develop an aircrew training pipeline simulation for the Australian Defence Force (ADF) that supports the real needs for strategic manpower planning in a context of policy and requirements change management. A case study is provided to illustrate the challenges and approach.

This paper addresses the design of an Automated Material Handling System (AMHS) for wafer lots in the photolithography workshop of a 200mm wafer manufacturing facility (fab) that was not initially built to have such a system. Lots transportation has to be performed using an Overhead Hoist Transport (OHT) system that was already chosen to transport reticles in the workshop. The main objective is to propose a decision support tool to characterize the Automated Material Handling System elements including lot handling, transportation as well as the storage space design. A simulation-based approach is proposed to evaluate different scenarios and propose an effective Automated Material Handling System design. Experimental results based on real instances confirm the capability of the proposed Automated Material Handling System design to support the workshop activity.

Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a supply chain is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining supply chain planning performance. Our paper argues for an agent-based approach to understand and improve supply chain planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.

This paper introduces a practical approach for the comprehensive simulation based planning and optimization of the production and logistics of a discrete goods manufacturer. Although simulation and optimization are well-established planning aides in production and logistics, their actual application in the field is still scarce, especially in small and medium-sized enterprises (SMEs). This is largely due to the complexity of the planning task and lack of practically applicable approaches for real-life planning scenarios. This paper provides a case study from the food industry, featuring a comprehensive planning approach based on simulation and optimization. The approach utilizes an offline-coupled multilevel simulation to smooth production and logistics planning via optimization, to optimally configure the production system using discrete-event simulation and to optimize the logistics network utilizing an agent-based simulation. The connected simulation and optimization modules can enhance the production logistics significantly, potentially providing a reference approach for similar industry applications.

The integration of information systems between the various actors organizing and executing the transport of containers to seaports is slowly progressing. Transport orders are frequently characterized by high change rates causing high manual revision effort for dispatchers. Therefore, these order changes, often received shortly before the day of departure, raise the question regarding the immediate transmission of transport orders to the subsequent actors in the transport chain. This paper analyzes the impact of different order release times, which define the timing of order transmission, on order process efficiency (processing times and costs) using a multi-method simulation approach. In a case study, four actors, two focusing on transport planning and two on operative transport execution, are considered. The simulation experiments with varying order release times and change rates reveal: A late release of orders from planning to operative actors and a reduction of order changes can significantly increase order process efficiency.

Home hospital services; provide some hospital level services at the patient’s residence. The services include for example: palliative care, administering chemotherapy drugs, changing dressings and care for newborns. The rationale of the service is that by providing high quality care to patients at their homes their experience of the care is better and hence they respond to the treatment and/or recover quicker and are less likely to need to report to hospital to receive care for more serious/expensive conditions. The aim of this study is to evaluate the effectiveness of the home hospital service, to optimize the current configuration given existing constraints and to evaluate potential future scenarios. Using a combined discrete event simulation, agent based model and geographical information system we assess the system effects of different demand patterns, appointment scheduling algorithms (e.g. travelling salesman problem), varying levels of resource on patient outcomes and impact on hospital visits.

There is a growing trend in the number of M&S studies that report on the use of Hybrid Simulation. However, the meaning and the usage of the term varies considerably. Indeed, the hybrid simulation panel during last year’s conference (WSC2016) laid bare the strong views, from the panelists and audience alike, as to what constitutes a hybrid model and what is new? The ensuing debate set the scene for this year’s paper, in which we discuss the various perspectives on hybrid simulation by focusing on three aspects: its definition, its purpose and its benefits. We hope this paper will pave the way for further studies on this subject, with the objective of achieving a convergence of the definition of hybrid simulation.

Mechanized tunneling is one of the most common methods used for underground constructions for infrastructure systems. Since a tunnel boring machine (TBM) represents a non-redundant single machine system, the efficiency of maintenance work highly impacts the overall project performance. The wear and tear of cutting tools is a critical, but mostly unknown process. To plan the maintenance work of cutting tools efficiently, it is necessary to know the current tool conditions and adapt the planned maintenance strategies to the actual status accordingly. In this paper, an existing theoretical empiric surrogate model to describe cutting tool conditions will be used and implemented as a software component within a process simulation tool that manages TBM steering parameters. Further, different maintenance setups for TBM cutting tools are presented and evaluated. To prove the capability of the presented approach, a case study will show the effects that improved maintenance work can have on project performance.