No description available.

A case study for this project is performed at Autoliv in Thailand the company uses a lean production flow approach. This manufacturing plant operates on the level of Industry 3.0, which automates processes using information technology. The case study scenario in manufacturing plants that runs in industrial automation 3.0 has some problems in material handling under the logistics division. By implementing AGV (Automated Guided Vehicle) with help of simulation, the time reduction factor through route and time optimization can be processed and displayed. This will have a direct impact on increasing the material handling efficiency in the production plant. The simulation used for AGV in plant layout is Discrete Event Simulation (DES), which divides each event according to its time. Siemens’s Tecnomatix Plant Simulation software is used as a software. This software handles complex production systems and provides control methods. In this thesis work, the entire plant is implemented in the simulated environment based on the plant layout as per dimensions. Then the AGV routing is done from source to destination. The stations are made as per the requirements between the source and the destination for loading from/ unloading to AGV. The processing time of these stations is given as inputs and the simulation is run for a shift to get the throughput. The specification of the AGV such as speed and dimensions given in the simulated environment is taken from the case study of AGV. A total of 55 AGV models’ are studied and 40 of them are selected for this particular plant layout, they are selected based on their features along with the throughput of units transferred from source to destination. The parameters of the AGV are based on the case study of AGV models. The unit throughput acquired in the simulated environment by the AGV is 20% to 22.5% more efficient than manual material handling.

Background Manufacturers have to adopt modern technologies to compete at the top of their field. However, adopting new technologies can be expensive and difficult to validate prior to implementation. One technology that has difficulties receiving a wider application is bin picking. Bin picking uses vision technology to communicate with an industrial robot. Consequently, the technology enables robots to pick randomly sorted objects. Research finds that the difficulty in assessing its performance can explain the lacklustre application of bin picking. In addition, research on bin picking is primarily focused on its technical difficulties and neglects information that can be valuable for potential adopters.  Objectives This thesis aims to aid decision-makers in assessing the implications and opportunities of bin picking. Furthermore, the thesis desire to inspire potential adopters by analysing the viability of bin picking through feasibility and the tangible and intangible benefits in a real-world setting. Methods A utility function is developed and assigned categories based on interviews with suppliers and adopters of the technology and literature review. The utility function highlights the feasibility and the intangible benefits of a bin picking solution and enables ranking among alternatives. The highest scoring article is used to conduct a feasibility study in collaboration with suppliers of bin picking technology. Based on the feasibility studies, a DES is created to highlight the implications that may arise in a real manufacturing environment. Finally, financial calculations through NPV, IRR, PP and DPP are created to evaluate the solution. Results All NPV calculations (excluding a 12.5 \% discount rate) are positive with enough years. The IRR is positive when the time span exceeds 11 years. The PP is 10-11 years while the DPP is 12-13, 14-15, 19-20, 32-33 years at a discount rate of 2.5 \%, 5 \%, 7.5 \% respectively 10 \%. However, the investment is never recoupable at a discount rate of 12.5 \%. The categories of the utility function have a clear impact on the feasibility and intangible benefits of the technology in a real-world setting. Bin picking relieves MMH tasks for operators, frees up facility space and reduces the collision risk. However, there are several risks with a bin picking solution. Conclusions Bin picking can become financially viable through automating MMH processes. However, how much capital is released depends on the man-hours spent in the previous process. The feasibility of bin picking implementation is dependent on the geometric complexity of the article, the sorting method inside the bin, the surrounding environment and the time margin. Decision-makers need to account for these factors prior to implementation. The intangible benefits can incentivise decision-makers to implement bin picking, even if the financial calculations show a net loss on the investment. / Bakgrund Tillverkare måste anta moderna tekniker för att kunna konkurrera vid toppen av sin bransch. Det kan dock vara både dyrt och svårt att validera ny teknologi före implementation. En teknologi som har haft det svårt att nå en bredare tillämpning är bin picking. Bin picking använder visionteknik för att kommunicera med industrirobotar. Teknologin gör det möjligt för robotar att plocka slumpmässigt sorterade objekt. Den låga tillämpningen av teknologin kan enligt forskare bero på svårigheterna med att bedöma dess prestanda. Forskning fokuserar dessutom främst på de tekniska problemen med bin picking och försummar information som är värdefull för potentiella användare. Syftet Syftet med denna studie är att tillhandahålla underlag för beslutsfattare att bedöma konsekvenserna och möjligheterna med bin picking. Vidare avser studien att inspirera potentiella användare genom att analysera lönsamheten av bin picking via dess genomförbarhet och dess materiella samt immateriella förmåner i en verklig miljö. Metod En nyttofunktion utvecklas och tilldelas kategorier baserat på intervjuer med leverantörer och antagare av teknologin samt från tidigare litteratur. Nyttofunktionen lyfter fram genomförbarheten samt de immateriella förmånerna i en bin  picking lösning, dessutom möjliggör den rangording mellan alternativ. Artikeln som rankas högst används för att genomföra en förstudie tillsammans med leverantörer av bin picking teknologi. En DES som baseras på förstudierna skapas för att lyfta fram de implikationer som kan uppstå i en verklig produktionsmiljö. Slutligen utvärderas lösningen genom finansiella medel, innefattande NPV, IRR, PP och DPP. Resultat Alla NPV-beräkningar (exklusive vid en diskonteringsränta på 12,5 \%) är positiva efter tillräckligt många år. IRR är positivt när tiden överstiger 11 år. PP är 10-11 år, medans DPP är 12-13, 14-15, 19-20, 32-33 år med en diskonteringsränta på 2,5 \%, 5 \%, 7,5 \% respektive 10 \%. Investeringen är dock aldrig återbetalningsbar vid en diskonteringsränta på 12,5 \%. Nyttofunktionens kategorier har en tydlig påverkan gällande teknologins genomförbarhet och immateriella fördelar i en verklig produktionsmiljö. Bin picking avlastar operatörer från MMH-uppgifter, frigör fabriksyta och minskar kollisionsrisken. Det finns dock flera risker med en bin picking lösning. Slutsats Bin picking kan vara ekonomiskt lönsamt genom att automatisera MMH processer. Hur mycket kapital som frigörs beror dock på det antal arbetstimmar som spenderas vid den manuella processen. Genomförbarheten vid implementeringen av bin picking är beroende av artikelns geometriska komplexitet, sorteringsmetod, omgivningen och tidsmarginal. Detta är faktorer som beslutsfattare måste ta hänsyn till före implementation. De immateriella fördelarna kan ge beslutsfattare incitament att införa bin picking, även om de finansiella beräkningar visar en förlust vid en investering.

Maintaining coherence between the independent views of multiple participants at distributed locations is essential in an Embedded Simulation environment. Currently, the Distributed Interactive Simulation (DIS) protocol maintains coherence by broadcasting the entity state streams from each simulation station. In this dissertation, a novel alternative to DIS that replaces the transmitting sources with local sources is developed, validated, and assessed by analytical and experimental means. The proposed Concurrent Model approach reduces the communication burden to transmission of only synchronization and model-update messages. Necessary and sufficient conditions for the correctness of Concurrent Models in a discrete event simulation environment are established by developing Behavioral Congruence ¨B(EL, ER) and Temporal Congruence ¨T(t, ER) functions. They indicate model discrepancies with respect to the simulation time t, and the local and remote entity state streams EL and ER, respectively. Performance benefits were quantified in terms of the bandwidth reduction ratio BR=N/I obtained from the comparison of the OneSAF Testbed Semi-Automated Forces (OTBSAF) simulator under DIS requiring a total of N bits and a testbed modified for the Concurrent Model approach which required I bits. In the experiments conducted, a range of 100 d BR d 294 was obtained representing two orders of magnitude reduction in simulation traffic. Investigation showed that the models rely heavily on the priority data structure of the discrete event simulation and that performance of the overall simulation can be enhanced by an additional 6% by improving the queue management. A low run-time overhead, self-adapting storage policy called the Smart Priority Queue (SPQ) was developed and evaluated within the Concurrent Model. The proposed SPQ policies employ a lowcomplexity linear queue for near head activities and a rapid-indexing variable binwidth calendar queue for distant events. The SPQ configuration is determined by monitoring queue access behavior using cost scoring factors and then applying heuristics to adjust the organization of the underlying data structures. Results indicate that optimizing storage to the spatial distribution of queue access can decrease HOLD operation cost between 25% and 250% over existing algorithms such as calendar queues. Taken together, these techniques provide an entity state generation mechanism capable of overcoming the challenges of Embedded Simulation in harsh mobile communications environments with restricted bandwidth, increased message latency, and extended message drop-outs.

Discrete event simulation development requires significant investments in time and resources. Descriptions of discrete event simulation models are associated with world views, including the process interaction orientation. Historically, these models have been encoded using high-level programming languages or special purpose, typically vendor-specific, simulation languages. These approaches complicate simulation model reuse and interchange. The current document-centric World Wide Web is evolving into a Semantic Web that communicates information using ontologies. The Web Ontology Language – OWL, was used to encode a Process Interaction Modeling Ontology for Discrete Event Simulations (PIMODES). The PIMODES ontology was developed using ontology engineering processes. Software was developed to demonstrate the feasibility of interchanging models from commercial simulation packages using PIMODES as an intermediate representation. The purpose of PIMODES is to provide a vendor-neutral open representation to support model interchange. Model interchange enables reuse and provides an opportunity to improve simulation quality, reduce development costs, and reduce development times.

In human-computer systems, such as supervisory control systems, large volumes of incoming and complex information can degrade overall system performance. Strategically integrating automation to offload tasks from the operator has been shown to increase not only human performance but also operator efficiency and safety. However, increased automation allows for increased task complexity, which can lead to high cognitive workload and degradation of situational awareness. Adaptive automation is one potential solution to resolve these issues, while maintaining the benefits of traditional automation. Adaptive automation occurs dynamically, with the quantity of automated tasks changing in real-time to meet performance or workload goals. While numerous studies evaluate the relative performance of manual and adaptive systems, little attention has focused on the implications of selecting particular invoking or revoking strategies for adaptive automation. Thus, evaluations of adaptive systems tend to focus on the relative performance among multiple systems rather than the relative performance within a system. This study takes an intra-system approach specifically evaluating the relationship between cognitive workload and situational awareness that occurs when selecting a particular invoking-revoking strategy for an adaptive system. The case scenario is a human supervisory control situation that involves a system operator who receives and interprets intelligence outputs from multiple unmanned assets, and then identifies and reports potential threats and changes in the environment. In order to investigate this relationship between workload and situational awareness, discrete event simulation (DES) is used. DES is a standard technique in the analysis iv of systems, and the advantage of using DES to explore this relationship is that it can represent a human-computer system as the state of the system evolves over time. Furthermore, and most importantly, a well-designed DES model can represent the human operators, the tasks to be performed, and the cognitive demands placed on the operators. In addition to evaluating the cognitive workload to situational awareness tradeoff, this research demonstrates that DES can quite effectively model and predict human cognitive workload, specifically for system evaluation. This research finds that the predicted workload of the DES models highly correlates with well-established subjective measures and is more predictive of cognitive workload than numerous physiological measures. This research then uses the validated DES models to explore and predict the cognitive workload impacts of adaptive automation through various invoking and revoking strategies. The study provides insights into the workload-situational awareness tradeoffs that occur when selecting particular invoking and revoking strategies. First, in order to establish an appropriate target workload range, it is necessary to account for both performance goals and the portion of the workload-performance curve for the task in question. Second, establishing an invoking threshold may require a tradeoff between workload and situational awareness, which is influenced by the task’s location on the workload-situational awareness continuum. Finally, this study finds that revoking strategies differ in their ability to achieve workload and situational awareness goals. For the case scenario examined, revoking strategies based on duration are best suited to improve workload, while revoking strategies based on revoking thresholds are better for maintaining situational awareness.

With the advances in the information and computing technologies, the ways the manufacturing enterprise systems are being managed are changing. More integration and adoption of the system perspective push further towards a more flattened enterprise. This, in addition to the varying levels of aggregation and details and the presence of the continuous and discrete types of behavior, created serious challenges for the use of the existing simulation tools for simulating the modern manufacturing enterprise system. The commonly used discrete event simulation (DES) techniques face difficulties in modeling such integrated systems due to increased model complexity, the lack of data at the aggregate management levels, and the unsuitability of DES to model the financial sectors of the enterprise. System dynamics (SD) has been effective in providing the needs of top management levels but unsuccessful in offering the needed granularity at the detailed operational levels of the manufacturing system. On the other hand the existing hybrid continuous-discrete tools are based on certain assumptions that do not fit the requirements of the common decision making situations in the business systems. This research has identified a need for new simulation modeling approaches that responds to the changing business environments towards more integration and flattened enterprise systems. These tools should be able to develop comprehensive models that are inexpensive, scalable, and able to accommodate the continuous and discrete modes of behavior, the stochastic and deterministic natures of the various business units, and the detail complexity and dynamic complexity perspectives in decision making. The research proposes and develops a framework to combine and synchronize the SD and DES simulation paradigms to simulate the manufacturing enterprise system. The new approach can respond to the identified requirements in simulating the modern manufacturing enterprise systems. It is directed toward building comprehensive simulation models that can accommodate all management levels while explicitly recognizing the differences between them in terms of scope and frequency of decision making as well as the levels of details preferred and used at each level. This SDDES framework maintains the integrity of the two simulation paradigms and can use existing/legacy simulation models without requiring learning new simulation or computer programming skills. The new framework uses a modular structure by which the SD and DES models are treated as members of a comprehensive simulation. A new synchronization mechanism that that maintains the integrity of the two simulation paradigms and is not event-driven is utilized to coordinate the interactions between the simulation modules. It avoids having one simulation paradigm dominating the other. For communication and model management purposes the SDDES formalism provides a generic format to describe, specify, and document the simulation modules and the information sharing processes. The SDDES controller which is the communication manager, implements the synchronization mechanism and manages the simulation run ensuring correct exchange of data in terms of timeliness and format, between the modules. It also offers the user interface through which users interact with the simulation modules.

The objective of this research is to increase the robustness of discrete-event simulation (DES) when input uncertainties associated models and parameters are present. Input uncertainties in simulation have different sources, including lack of data, conflicting information and beliefs, lack of introspection, measurement errors, and lack of information about dependency. A reliable solution is obtained from a simulation mechanism that accounts for these uncertainty components in simulation. An interval-based simulation (IBS) mechanism based on imprecise probabilities is proposed, where the statistical distribution parameters in simulation are intervals instead of precise real numbers. This approach incorporates variability and uncertainty in systems. In this research, a standard procedure to estimate interval parameters of probability distributions is developed based on the measurement of simulation robustness. New mechanisms based on the inverse transform to generate interval random variates are proposed. A generic approach to specify the required replication length to achieve a desired level of robustness is derived. Furthermore, three simulation clock advancement approaches in the interval-based simulation are investigated. A library of Java-based IBS toolkits that simulates queueing systems is developed to demonstrate the new proposed reliable simulation. New interval statistics for interval data analysis are proposed to support decision making. To assess the performance of the IBS, we developed an interval-based metamodel for automated material handling systems, which generates interval performance measures that are more reliable and computationally more efficient than traditional DES simulation results.

Emergency departments in Sweden are usually struggling with long waiting times, delays and bottlenecks in the system. The National Board of Health and Welfare and the County Council of Västra Götaland have established to decrease the average time a patient stays in an emergency department as important priority as well as the waiting time to be seen by a nurse and by a physician. Healthcare systems are usually characterized by its complexity because of the variability and stochastic nature of the different processes involved in the flow of patients, staff and resources. In order to increase the use of the existing resources and to reduce the waiting times of patients, a system improvement methodology involving discrete-event simulation and process analysis has been used. In this project a computer-based simulation tool was applied at the emergency department of the hospital Kärnsjukhuset in Skövde, which belongs to Skaraborgs Sjukhus and is one of the largest emergency departments in the region of Västra Götaland. A three-dimensional model was created to help visualize and understand the problems, as well as to identify improvements by the different stakeholders involved. Continually, the simulation model was modified to test possible improved scenarios with the aim to increase the service level of the system.  The design, implementation and analysis of these scenarios have provided decision makers of the emergency department with the necessary information to implement or reject the ideas of the different improved scenarios. Some of these scenarios had a significant impact with small changes so they were implemented in the real system; some others had non-significant impact in the results so they were not implemented. The main result of this project has been to identify which system changes will lead to a reduction of the different waiting times of patients. In addition, the simulation and experiments of future solutions show a more efficient use of the existing resources. This design of a better configuration of the system gives Kärnsjukhuset the possibility to increase the service level of the system and to meet some of the requirements established by the County Council. This project shows that the use of simulation tools provides enormous benefits for healthcare system analysis and improvement; new ideas and scenarios can be designed without disturbing the normal activities of the hospital, saving considerable time, money and resources.

Resource management is at the heart of many diverse science and engineering research areas. Although the general notion of what constitutes a resource entity seems similar in different research areas, their types, characteristics, and constraints governing their behavior are vastly different depending on the particular domain of research and the nature of the research itself. Often research related to resource modeling and management focus on largely homogeneous resources in a relatively simplified model of the real world. The problem becomes much more challenging to deal with when working with a complex real life domain with many heterogeneous resource types and intricate constraints. In this dissertation, we have looked at the modeling and management of resource instances and tried to develop a better sense of what makes them different from other objects in a system. As part of this work, We formally define the general resource management problem, identify its major sub problem areas and their associated complexities, and look at the problem in the context of a particularly complex and dynamic environment, namely the emergency department (ED) of a hospital. We propose an approach to the problem and some of its complexities by presenting an overall unifying view, as well as tools and methods for dealing with, this pervasive, yet surprisingly under examined, type of entity, i.e. resources. We have discovered that one of the discerning characteristics of resource instances in complex and dynamic environments seem to be their dynamic capability profile that may changes depending on system context. This, in turn, often results in complex substitutability relationship amongst resource instances. We have identified four major sub-problem areas that can provide a holistic view of any resource management service. These separate, yet interconnected, areas of con- cerns include resource modeling, resource request specification, resource constraint management, and resource allocation. Resource modeling involves capturing of re- source characteristics and their potentially dynamic behavior. Request definitions describe how resource users specify requirements for resources in a particular do- main. In most domains, there are constraints that need to be satisfied while serving resources to fulfill specific requests. The fourth area of concerns, the allocation of resources, is a complex component with multiple subcomponents that closely inter- act with each other. In this thesis, we have described an architecture for a exible resource management service based on the above described separation of concerns. We have proposed some simple, yet effective, techniques for modeling resource in- stances, specifying resource requests, specifying and managing resource constraints, and allocating resource instances to meet a resource demand characterized by a con- tinuous stream of requests. Using our proposed design, we have developed ROMEO, a resource management service and customized it to serve a task coordination frame- work based on Litlle-JIL process definition language. Our work then concentrated on evaluating the effectiveness of ROMEO in supporting simulations and executions of complex processes. For this evaluation purpose, we developed a simulation infras- tructure named JSim on top of Juliette, Little-JIL's execution environment. We ran a variety of simulations of patient care processes in EDs using our ROMEO-JSim infrastructure. We also used ROMEO to support the actual execution (rather than just the simulation) of a large mediation process. A central premise, hypothesized and explored in this thesis, is a novel way of thinking about resource instances in dynamic domains, namely defining them with a set of guarded capabilities, some of which may be dependent on the execution state of the system. This has led us to think about how to represent execution states of a running system and what types of system state information might be important for representing the guard functions on the capabilities of a resource instance that define the resource instance's ability to satisfy a request at a given execution state of the system. We have also identified a small set of common types of attributes of resource instances that seem able to support specification of a large variety of resource instances in complex domains. We believe that our research supports our hypothesis that specifying resource instances as having sets of guarded capabilities provides a useful abstraction for modeling many of the complex dynamic behaviors of resource instances in such domains as hospital EDs.