A First Approach on Modelling Staff Proactiveness in Retail Simulation Models

There has been a noticeable shift in the relative composition of the industry in the developed countries in recent years; manufacturing is decreasing while the service sector is becoming more important. However, currently most simulation models for investigating service systems are still built in the same way as manufacturing simulation models, using a process-oriented world view, i.e. they model the flow of passive entities through a system. These kinds of models allow studying aspects of operational management but are not well suited for studying the dynamics that appear in service systems due to human behaviour. For these kinds of studies we require tools that allow modelling the system and entities using an object-oriented world view, where intelligent objects serve as abstract \'actors\' that are goal directed and can behave proactively. In our work we combine process-oriented discrete event simulation modelling and object-oriented agent based simulation modelling to investigate the impact of people management practices on retail productivity. In this paper, we reveal in a series of experiments what impact considering proactivity can have on the output accuracy of simulation models of human centric systems. The model and data we use for this investigation are based on a case study in a UK department store. We show that considering proactivity positively influences the validity of these kinds of models and therefore allows analysts to make better recommendations regarding strategies to apply people management practices.Retail Performance, Management Practices, Proactive Behaviour, Service Experience, Agent-Based Modelling, Simulation

3D GIS Modeling of Soft Geo-Objects: Taking Rainfall, Overland Flow, and Soil Erosion as an Example

In physics, objects can be divided into rigid and soft objects according to the object deformation capacity. Similarly, geo-object can also be classified into rigid geo-objects (e.g., building, urban) and soft geo-objects (e.g., mudflow, water, soil erosion). There are three types of approaches for 3D GIS modeling, i.e., surface-based, volume-based, and hybrids in terms of geometry. These approaches are suitable for representing rigid geo-objects, but they are not suitable to simulate the intrinsic properties of the soft geo-object, i.e., dynamics and deformation. And so far there are few GIS modeling methods for simulation of soft geo-objects. GIS flow elements (FEs) and GIS soft voxels (SVs) were proposed for 3D modeling of soft geo-objects. GIS flow elements can realistically represent the dynamics and stochastics of soft geo-objects, while GIS soft voxels simulate deformation of soft geo-objects. The authors discuss the implementation and computer programming of GIS flow elements and GIS soft voxels in this study. GIS FE and SV have been successfully applied in a case study toward the simulation of the process of rainfall, overland flow, and soil erosion. A software system has been designed and developed, which has the functions of data management, model computation, and 3D simulation

Assessment of the Predictive Reliability of a SWAT Flow Model and the Evaluation of Runoff Generation and BMP effectiveness in a Shale-Gas Impacted Watershed Using a Modeling Approach

In order to ensure a harmonious harness of shale-gas resources while ensuring minimal damage to the environment, it is imperative that studies to conduct to inform various aspects of managing the environment. This includes the development of reliable hydrologic models to inform in decisions concerning water and the environment. The first objective of this study was to evaluate the predictive reliability of the Soil and Water Assessment Tool (SWAT) model based on respective methods of LULC data classification and data type spatial resolution. Results showed that the high-resolution data classified with object-oriented image method does not provide any significant advantage in terms of the model\u27s flow predictive reliability. The second goal focused on an application of the object-oriented image analysis technique for change detection related to shale-gas infrastructure and subsequently evaluates the impact of shale-gas infrastructure on stream-flow in the South Fork of the Little Red River (SFLRR). Results showed that since the upsurge in shale-gas related activities in the Fayetteville Shale Play (between 2006 and 2010), shale-gas related infrastructure in the SFLRR have increased by 78% corresponding to a differential increase on storm water flow by approximately 10% over a projected period of simulation. The last objective deals with the evaluation of BMP effectiveness in a shale-gas watershed using a modeling approach. Three BMPs identified to control flow were introduced and simulated for a simulation (2000 to 2009) and projected (2010 to 2020) periods. The differences in the flow output at the watershed outlet for each BMP scenario were derived by comparing baseline and respective BMP scenarios. Results indicate that the BMPs have an average effectiveness of approximately 80% in reducing storm water flow attributable to shale-gas

A first approach on modelling staff proactiveness in retail simulation models

There has been a noticeable shift in the relative composition of the industry in the developed countries in recent years; manufacturing is decreasing while the service sector is becoming more important. However, currently most simulation models for investigating service systems are still built in the same way as manufacturing simulation models, using a process-oriented world view, i.e. they model the flow of passive entities through a system. These kinds of models allow studying aspects of operational management but are not well suited for studying the dynamics that appear in service systems due to human behaviour. For these kinds of studies we require tools that allow modelling the system and entities using an object-oriented world view, where intelligent objects serve as abstract “actors” that are goal directed and can behave proactively. In our work we combine process-oriented discrete event simulation modelling and object-oriented agent based simulation modelling to investigate the impact of people management practices on retail productivity. In this paper, we reveal in a series of experiments what impact considering proactivity can have on the output accuracy of simulation models of human centric systems. The model and data we use for this investigation are based on a case study in a UK department store. We show that considering proactivity positively influences the validity of these kinds of models and therefore allows analysts to make better recommendations regarding strategies to apply people management practises

Object Watershed Link Simulation (OWLS)

Object Watershed Link Simulation (OWLS) is a physically based watershed model. In the OWLS model, a watershed is defined as a three-dimensional object with linkages between cells and their attributes (e.g., area, slope, soil type, etc.). A cell is defined as the linkages of edges and their attributes (e.g., length, slope, etc.) and an edge is defined as the linkage of nodes and their attributes (e.g., depth of soil, elevation). The watershed hydrologic components such as water depth, surface flow, etc., are features associated with the cells, edges and nodes of a watershed. Simulation of hydrologic processes across a watershed involves the calculation of flows and water balances for these cells, edges, and nodes and their linkages. Therefore, the OWLS model is a three-dimensional, object-linked, vector-based model. OWLS includes four sub-models that focus on (1) Data Processing, (2) Geomorphology, (3) Hydrology and (4) Visualization. The Data Processing Model handles conversions of raw data from watershed surveys into OWLS format. It also handles missing data interpolation and extrapolation for air temperature, precipitation, and streamflow. The Geomorphologic Model handles the automatic watershed delineation for flowpaths, streams, and boundaries, as well as stream geometry and macropore geometry. The Hydrologic Model handles water balance, flow calculation and flow routing for the canopy, surface, subsurface and macropore system associated with each cell. The Visualization Model handles 3-D watershed projection, 2-D watershed projection, hydrograph presentation, and 3-D dynamic watershed animation for simulated flows and other hydrologic components of the Hydrologic Model. The OWLS model was tested with data from the Bear Brook Watershed of Maine (BBWM). Results from parameter calibration and validation indicate that the model generally provided good estimation of streamflows for rain-based flood events and unstable estimations for rain-on-snow events or snowmelt-based events when air temperature was high

Development Of Distributed Grid-Based Hydrological Model And Floodplain Inundation Management System

A physical based, distributed hydrological model was developed to route overland flows during isolated HISD storms. The model has operated on a grid or cell basis and routed the excess rainfall over the grids, conforming to the DEM-derived drainage paths, to the basin outlet. The rainfall-runoff hydrological modelling was implemented in MATLAB 7.0. The system has integrated GIS, RS, DEM, data management capability and a dynamic basin model within a common Windows environment. The simulation algorithms of the rainfall-runoff model have operated on grid bases compatible with the MATLAB programming language, which has been used to write instructions to many grid-based operations. Due to the MATLAB architecture, the system has been proven successful for large-scale basin modeling, which requires high level resolution, record keeping and technical transfer. The model has estimated the runoff using the Soil Conservation Service-Curve Numbers (SCS-CN), determined by the land use/ land cover and the hydrological soil group found in each grid. The overland flow mechanics were described by the diffusion wave approximation of St Venant equations, which were numerically solved for depth of flow and runoff by the finite volume method (FVM). The grid cell physical properties such as topography, land use, soil, and Manning’s roughness’ coefficient were extracted from published maps for discretized cells of the Klang River basin(KRB) using a GIS. The land use/cover classes were derived from interpreted information of Landsat TM imagery using the combined object-oriented segmentation - fuzzy logic algorithm. The DEM of 90m resolution, used to calculate slopes that generated runoffs, was derived from radar data sets (C-band) of the Shuttle Radar Topography Mission (SRTM) using the interferometric approach. Four criteria were used for the assessment of the model performance - Model bias, Nash– Sutcliffe and model efficiencies for both low and high flows during both calibration and validation periods. The results showed the advantages of integrating RS, DEM and GIS with hydrologic simulation in generating runoff processes in the spatial domain, attaining as well fairly high precision simulation with the general hydrologic trends well captured by the model. This study has also involved the application of flood modeling, which has integrated the results of the grid-based overland flow routing model into MIKE11 onedimensional hydrodynamic model. The discharge hydrographs were extracted from the grid-based overland flow routing model in ASCII format and imported into MIKE11 hydrodynamic modeling system. The MIKE11 model was developed based on surveyed, stream cross-section data to perform hydrodynamic simulation of the flooding process. The MIKE11 modeling was applied to the Klang River system comprising 9 main tributaries. The analysis has considered the river system with and without Stormwater Management and Road Tunnel (SMART) project, which involve structural flood mitigations measures including retention ponds, bypass tunnel and flow diversions, where the river physical condition was modified accordingly. Hourly data for flow were created into compatible MIKE11 time series in a separate file as input to the parameter editors. Initial and boundary conditions were based on the inputs for MIKE11 operational analysis. It has been found that the modeled predictions of depth and discharge matched observed data. A good agreement between the simulated and observed data was achieved for rating curves with RMSE = 0.96, 0.94, 0.95, and 0.97 at respective calibration points. From the results revealed by the MIKE11 modeling simulation, there were evidences that SMART was useful for flood mitigation of Klang River Basin. For instance at Tun Perak Bridge, the normal level for the Klang River was 25m, the alert level was 28m and the danger level was 29.5m. The value from the simulation showed that the maximum water level without SMART was 32m. However this level with SMART was only 27.8m which did not exceed the alert and danger level at Tun Perak Bridge. This area is the most critical part of KL. Once the water level from the Klang River exceeds the flood wall, the whole KL will be badly flooded. Finally, the results of the runoff modeling were integrated in MIKE-GIS model for flood inundation mapping. A digital planimetric view and topographic mapping of the floodplain was developed using the three-dimensional floodplain visualization approach through the integration of a digital terrain model. This model was synthesized from MIKE11 stream cross-sectional coordinate into a digital surface model, generated from aerial stereo pair photos using Ortho Engine PCI image processing software. The resulting formulated surface model provided a good representation of the general landscape and contained additional details within the stream channel. Integration of 3D-GIS and spatial analytical techniques together with hydrologic and hydraulic modeling processes has enhanced the visualization and display techniques for visual presentation and generation of flood inundation maps for early warning and contingency planning

COMPARISON OF THE MICRO-SIMULATION SOFTWARE AIMSUN & IHCM-1997 FOR HIGHWAY TRAFFIC PERFORMANCE ANALYSIS

In order to make good decisions in transportation, decision-makers need some references to support it. One of the sources for such reference is by performing a micro-simulation; a model for representing real-world conditions including the behavior of travelers, vehicles and the infrastructure. This study examines and presents a comparison between AIMSUN (a commercial micro-simulation software) and Indonesia Highway Capacity Manual 1997(IHCM-1997) in relation to the road traffic performance of the study object Södralänken, E266 and E75, in the southern part of Stockholm, Sweden. A calibration process was conducted in order to find the best value of a set of parameters in each software, selected based on the lowest value of a Root Mean Square Error (RMSE) computed based on observed speed data and the model output. The parameters were then validated using evening peak-hour data. The comparisons were conducted in terms of flow, speed and density by AIMSUN, IHCM-1997 and the observation data on morning and evening peak-hour. The results are from the given experiments with the AIMSUN software with the best set of parameters being when the value of Maximum Desired Speed is at 100 km/h and Speed Acceptance is at 1,1. It shows that the significant difference between AIMSUN, IHCM-1997 and observation lays on the speed. IHCM-1997 gives relatively higher speed than both AIMSUN and observation data

A fine-grain time-sharing Time Warp system

Although Parallel Discrete Event Simulation (PDES) platforms relying on the Time Warp (optimistic) synchronization protocol already allow for exploiting parallelism, several techniques have been proposed to further favor performance. Among them we can mention optimized approaches for state restore, as well as techniques for load balancing or (dynamically) controlling the speculation degree, the latter being specifically targeted at reducing the incidence of causality errors leading to waste of computation. However, in state of the art Time Warp systems, events’ processing is not preemptable, which may prevent the possibility to promptly react to the injection of higher priority (say lower timestamp) events. Delaying the processing of these events may, in turn, give rise to higher incidence of incorrect speculation. In this article we present the design and realization of a fine-grain time-sharing Time Warp system, to be run on multi-core Linux machines, which makes systematic use of event preemption in order to dynamically reassign the CPU to higher priority events/tasks. Our proposal is based on a truly dual mode execution, application vs platform, which includes a timer-interrupt based support for bringing control back to platform mode for possible CPU reassignment according to very fine grain periods. The latter facility is offered by an ad-hoc timer-interrupt management module for Linux, which we release, together with the overall time-sharing support, within the open source ROOT-Sim platform. An experimental assessment based on the classical PHOLD benchmark and two real world models is presented, which shows how our proposal effectively leads to the reduction of the incidence of causality errors, as compared to traditional Time Warp, especially when running with higher degrees of parallelism

A comparative analysis of business process modelling techniques

Business process modelling is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. Several modelling techniques have been proposed and used to capture the characteristics of business processes. However, available techniques view business processes from different perspectives and have different features and capabilities. Furthermore, to date limited guidelines exist for selecting appropriate modelling techniques based on the characteristics of the problem and its requirements. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on five criteria: flexibility, ease of use, understandability, simulation support and scope. The study highlights some of the major paradigmatic differences between the techniques. The proposed framework can serve as the basis for evaluating further modelling techniques and generating selection procedures

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624