COMPUTER SIMULATION SOFTWARE REUSE BY GENERIC/SPECIFIC DOMAIN MODELING APPROACH

The objective of this paper is to provide a brief introduction to the area of domain analysis as seen from the software engineering perspective. The approach is by illustrating the concepts through selected reported experiences and to point out the specific characteristics of these experiences that relate to domain analysis. Definitions are introduced after the examples to avoid over explaining the concepts. A model for the domain analysis process is also proposed. The concept of a library based domain infrastructure is introduced as an attempt to show how domain analysis is integrated into the software development process.A second objective in this paper is to give a perspective on some of the research issues facing domain analysis. The nature of the process calls for a variety of multidiciplinary issues ranging from knowledge acquisition and knowledge representation to management and methodologies to cultural and social questions.

This article aims to provide a description of fundamental elements of discrete event simulation (DES), the process and the values of applying DES in assisting healthcare design and planning decision-making. More importantly, it explores how new technology such as electronic medical records, real-time location services (RTLS), and other simulation methods such as space syntax analysis (SSA) can facilitate and complement DES. Healthcare administrators increasingly recognize DES as an effective tool for allocating resources and process improvement. However, limited studies have described specifically how DES can facilitate healthcare design. Three case studies were provided to illustrate the value of DES in supporting healthcare design. The first case study used DES to validate a surgical suite design for shorter surgeon walking distance. The second case study used DES to facilitate capacity planning in a clinic through testing the utilization of exam rooms upon various growth scenario. The detailed process data for the current clinic were captured through RTLS tracking. The third case study applied DES in an emergency department for both site selection in master planning and capacity test at various growth scenarios with seasonal volume swing. In addition, SSA was conducted to evaluate the impacts of design on visual surveillance, team communication, and co-awareness. It is recognized that the DES analysis is an effective tool to address the process aspects of healthcare environments and should be combined with post-occupancy evaluation and SSA to address behavioral aspects of operations to provide more solid evidence for future design.

Domain analysis is an expansion of conventional requirements analysis. Domain analysis can support effective software reuse. However, domain analysis is time consuming and is limited to a particular application area. Analogical approaches to software reuse, on the other hand, often occur across domains. Analogical problem solving is a process of transferring knowledge from a well-understood base domain to a new target problem area. Analogy can facilitate software reuse for poorly understood problems or new application areas. Analogy shares similar concepts with reuse and some analogy theories have been applied to software reuse. However, current research on software analogy often overlooks the importance of analysis for the base domain and does not consider some critical aspects of analogy concepts. Reuse must be based on high quality artifacts, especially reuse across domains. This paper presents an approach to integrate domain analysis and analogy methods. In our view, domain analysis and software analogy have complementary roles. Domain analysis is regarded as a process to identify and supply necessary information for analogical transfer. Software analogy can provide the analyst with similar problems and solutions to reuse previous domain analysis knowledge or artifacts for a new domain. This paper presents case studies to demonstrate the increase of efficiency in applying the approach. Evaluation of the approach from various perspectives is also reported.

Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer

Goal: this study aimed to use the experimental simulation method to obtain the optimum results by using the simulation technique of the models, which are modeled mathematically but not solved.
 Design/Methodology/Approach: a case study was conducted to apply the discrete-event simulation (DES) approach based on the design of experiment (DOE) technique to derive strategies and analyze situations in an emergency department (ED).
 Results: by integrating the DOE approach with the DES technique for the ED, the average waiting time for a patient in a small ED was reduced from 40.09 minutes to 9.83 minutes (75.48%), which is a significant result. Increasing the capacity of the resources without increasing the number of daily resources for the ED resulted in an increase from 133 to 147 (10.53%) in the number of patients treated. Additionally, the optimum number of healthcare resources to employ in EDs was calculated.
 Limitations: there is no shift system for healthcare resources in the DES model. Since the ED was considered in this study, the same number of resources was run during the working hours of the DES model.
 Practical implications: for researchers and healthcare professionals, the developed method is used to obtain results that cannot be obtained through the mathematical modeling of healthcare problems. The results of the changes in health resources on the outputs are obtained by this method as the shortest time frame and lowest cost.
 Originality/Value: there is almost no detailed study of DES with the DOE technique. Providing high reliability has resulted in obtaining optimum values through the integration of the DOE approach with the DES technique in the healthcare area. It is very difficult to get accurate results anywhere where the human factor is present.

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

In the early design phase of embedded systems, discrete-event simulation is extensively used to analyse time properties of hardware-software architectures. Improvement of simulation efficiency has become imperative for tackling the ever increasing complexity of multi-processor execution platforms. The fundamental limitation of current discrete-event simulators lies in the time-consuming context switching required in simulation of concurrent processes. In this paper, we present a new simulation approach that reduces the number of events managed by a simulator while preserving timing accuracy of hardware-software architecture models. The proposed simulation approach abstracts the simulated processes by an equivalent executable model which computes the synchronization instants with no involvement of the simulation kernel. To consider concurrent accesses to platform shared resources, a correction technique that adjusts the computed synchronization instants is proposed as well. The proposed simulation approach was experimentally validated with an industrial modeling and simulation framework and we estimated the potential benefits through various case studies. Compared to traditional lock-step simulation approaches, the proposed approach enables significant simulation speed-up with no loss of timing accuracy. A simulation speed-up by a factor of 14.5 was achieved with no loss of timing accuracy through experimentation with a system model made of 20 functions, two processors and shared communication resources. Application of the proposed approach to simulation of a communication receiver model led to a simulation speed-up by a factor of 4 with no loss of timing accuracy. The proposed simulation approach has potential to support automatic generation of efficient system models.

The authors examine domain modeling approaches from the viewpoint of their operational goals. Domain models are representations of an application domain that can be used for a variety of operational goals in support of specific software engineering tasks or processes. Some of the operational goals of domain modeling for software engineering are illustrated. A workshop on domain modeling was organized around operational goals and their resultant domain models and modeling methodologies. The workshop verified the importance of generalized meta-models and their use in instantiating domain knowledge into application domain models. Representation, domain classification and analysis, model development, instantiation evolution and validation, and knowledge structuring and inference are discussed. >

Globalization leads to growing distribution distances in logistics. This increases the cost- and energy-related effort for the transportation of goods. As a consequence, companies need to react to ensure their competitiveness, in the best case already in the strategic planning of the distribution chain. Due to the fact that the distribution chain is a complex and dynamic network, it is difficult to reach adequate results with analytic methods. Discrete event simulation is an approach to achieve results that consider the dynamics of the system. In this paper, it is discussed how discrete event simulation is suitable for the strategic design of the distribution concerning an optimal exploitation of CO2 emissions, costs and service level applied for a case in the food sector. This has been realized by application-oriented scenarios in the context of a European project called e-SAVE that came out in the course of the seventh framework program (FP7). With the simulation approach, various distribution chain scenarios have been modelled, beginning with two independent distribution chains and later with various design alternatives with an increasing merging level. Finally, the results have been compared and evaluated concerning the impact of the factors given above, in order to identify the most CO2- and cost-efficient alternative. As assessment instrument the discrete event simulation tool SimChain has been utilized. In this context, the data model of the tool and several features of the implemented building blocks have been amended with respect to the requirements of the distribution scenarios.

In this paper, we consider the modeling and simulation of low-volume mixed model assembly lines that can be found in the aerospace industry. Low-volume mixed model assembly processes are characterized by a large amount of tasks to be manually performed, buffer space constraints, specialized resources like jigs and tools, and a large number of external suppliers. The main principles of modeling and simulating such manufacturing systems are discussed. Based on a domain analysis, the major building blocks of simulation models for low-volume mixed model assembly lines are derived. We exemplify their implementation using the commercial discrete-event simulation tool AutoSched AP. As an application of these building blocks, we analyze the cabin installation process in a final assembly line in aircraft production using discrete-event simulation.

Discrete Event Simulation (DEVS) is a widely used approach for modelling and simulation of dynamic discrete systems. The modern object-oriented DEVS world view regards active objects (entities) passing passive objects (stations) along given path. An event mechanism updates movement of entities, being capable also of collisions and other state-dependent phenomena. Consequently; DEVS can be used for simulation of pedestrian and evacuation dynamics. Classical straightforward modelling approaches, based on abstract classical queuing systems, do not take into account the spatial distributions primarily — only for animation a spatial component is used. But modern object-oriented tools allow modelling of a spatial distribution primarily by means of topological attributes as well in active and passive objects at the modelling level. This contribution discusses the “spatial” features of DEVS simulation systems The chosen simulation tool, Enterprise Dynamics, offers a high flexibility due to its programming language 4d script that allows adding further functionality to the provided elements as well as creating completely new ones.

According to a survey of Indonesian pet healthcare service users, only 19% of pet health applications are used since the functions offered by the applications are insufficient to meet the needs of the users. Developing feature model for the pet health application domain is the goal of this research. Using a mixed-methods approach, this research examined nine pet health apps which are available on Google Play and the App Store, observed 30 pet health service consumers through surveys using purposive sampling, and conducted document analyses. The Feature Oriented Domain Analysis (FODA) approach is applied within the Software Product Line Engineering (SPLE) framework. The result of this research is a pet health application domain feature model consisting of five non-functional requirements and 45 functional requirements. With an average assessment score of 96%, domain experts have determined that the feature model implementation in this study is in line with user needs.

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather. SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model. DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints. Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term. The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements. An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

The methodology of discrete-event simulation provides a promising alternative solution to designing and analyzing dynamic, complicated, and interactive construction systems. In this paper, an attempt is made to extend the previous work of simplifying construction simulation by delving into the fundamental approaches for discrete-event simulation. A new simplified discrete-event simulation approach (SDESA) is presented through extracting the constructive features from the existing event/activity-based simulation methods; both the algorithm and the model structure of simulation are streamlined such that simulating construction systems is made as easy as applying the critical path method (CPM). Two applications based on real road construction projects in Hong Kong serve as case studies to illustrate the methodology of simulation modeling with SDESA and reveal the simplicity and effectiveness of SDESA in modeling complex construction systems and achieving the preset objectives of such modeling. They are a granular base-course construction system featuring both cyclic and linear processes and an asphalt paving construction system with complicated technological/logical constraints. As a general-purpose method for construction planning, SDESA enables practitioners to deal with what the CPM-based network analysis method fails to solve by offering discrete-event simulation capabilities. Furthermore, the SDESA can potentially be adapted to special-purpose simulation tools to tackle large and complicated construction systems of practical size that have yet to find convenient solutions with existing simulation methods.

Smart manufacturing needs to handle increased uncertainty by becoming more responsive and more flexible to reconfigure. Advances in technology within industry 4.0 can provide acquisition of large amounts of data, to support decision making in manufacturing. Those possibilities have brought anew attention to the applicability of discrete event simulation for production flow modelling when moving towards design of logistics systems 4.0. This paper reports a study investigating challenges and opportunities for initiation of discrete event simulation, as a tool for decision making in the era of industry 4.0 manufacturing. The research has been approached through action research in combination with a real case study at a manufacturing company in the energy sector. The Covid-19 pandemic fated that adjusted and new ways of communication, collaboration, and data collection, in relation to the methods, had to be explored and tried. Throughout the study, production data, such as processing times, have been collected and analyzed for discrete event simulation modelling. The complexity of introducing discrete event simulation as a new tool for decision making is highlighted, where we emphasize the human knowledge and involvement yet necessary to understand and to draw conclusions from the data. The results also demonstrate that the data analysis has given valuable insights into production characteristics, that need addressing. Thus, revealing opportunities for how the initiative of introducing discrete event simulation as an anew tool in the wake of industry 4.0, can act as a catalyst for improved decision making in future manufacturing.