Agent-based Simulation of Workers’ Behaviors, Productivity, and Safety around Construction Obstacles

2020 ◽  
Author(s):  
Omar Binhomaid ◽  
Tarek Hegazy
Keyword(s):  
Autonomous Agents ◽  
Productivity Loss ◽  
Agent Based Modeling ◽  
Sensitivity Analyses ◽  
Construction Sites ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Site Model ◽  
Behavioral Rules ◽  
Improvement Measures

Construction sites involve many hazardous areas and fixed/movable objects around which the workers interact. Unguided site policies or workers’ behaviors can lead to productivity loss and/or accidents. In this paper, an agent-based modeling and simulation (ABMS) framework has been developed to: (1) provide a detailed site model with temporary facilities and obstacles (danger zones and productivity-hindering spots); (2) model the workers’ natural behaviors towards safety while moving around the site; and (3) quantify the consequent site productivity and accident potential of any site configuration. The paper discusses the site model and the behavioral rules that autonomous agents follow to simulate the movement of aggressive and avoider workers around hazard areas. The results of two case studies and sensitivity analyses show that different simulations with different site improvement measures can quantify the effectiveness of, and optimize, site operational measures and establish rewards for positive worker’s behaviors that improve productivity and safety.

scholarly journals Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study

BMJ ◽  
2021 ◽  
pp. n1087
Author(s):  
Santiago Romero-Brufau ◽  
Ayush Chopra ◽  
Alex J Ryu ◽  
Esma Gel ◽  
Ramesh Raskar ◽  
...  
Keyword(s):  
Hospital Admissions ◽  
Vaccination Strategy ◽  
Population Based ◽  
Agent Based Modeling ◽  
Sensitivity Analyses ◽  
Agent Based ◽  
Cumulative Mortality ◽  
Vaccination Rates ◽  
Dose Strategy ◽  
Modeling Study

AbstractObjectiveTo estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.DesignSimulation agent based modeling study.SettingSimulated population based on real world US county.ParticipantsThe simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions.InterventionsSimulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated.Main outcome measuresCumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days.ResultsOver all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

scholarly journals The Public Health Impact of Delaying a Second Dose of the BNT162b2 or mRNA-1273 COVID-19 Vaccine

2021 ◽  
Author(s):  
Santiago Romero-Brufau ◽  
Ayush Chopra ◽  
Alex J Ryu ◽  
Esma Gel ◽  
Ramesh Raskar ◽  
...  
Keyword(s):  
Vaccination Strategy ◽  
Population Level ◽  
Health Impact ◽  
Vaccination Rate ◽  
Agent Based Modeling ◽  
Sensitivity Analyses ◽  
Agent Based ◽  
Cumulative Mortality ◽  
Vaccination Rates ◽  
The Public

AbstractObjectivesTo estimate population health outcomes under delayedsecond dose versus standard schedule SARS-CoV-2 mRNA vaccination.DesignAgent-based modeling on a simulated population of 100,000 based on a real-world US county. The simulation runs were replicated 10 times. To test the robustness of these findings, simulations were performed under different estimates for single-dose efficacy and vaccine administration rates, and under the possibility that a vaccine prevents only symptoms but not asymptomatic spread.Settingpopulation level simulation.Participants100,000 agents are included in the simulation, with a representative distribution of demographics and occupations. Networks of contacts are established to simulate potentially infectious interactions though occupation, household, and random interactionsInterventionswe simulate standard Covid-19 vaccination, versus delayed-second-dose vaccination prioritizing first dose. Sensitivity analyses include first-dose vaccine efficacy of 70%, 80% and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread; and an alternative vaccination strategy that implements delayed-second-dose only for those under 65 years of age.Main outcome measurescumulative Covid-19 mortality over 180 days, cumulative infections and hospitalizations.ResultsOver all simulation replications, the median cumulative mortality per 100,000 for standard versus delayed second dose was 226 vs 179; 233 vs 207; and 235 vs 236; for 90%, 80% and 70% first-dose efficacy, respectively. The delayed-second-dose strategy was optimal for vaccine efficacies at or above 80%, and vaccination rates at or below 0.3% population per day, both under sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100,000. The delayed-second-dose for those under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed-second-dose vaccination strategy, at least for those under 65, could result in reduced cumulative mortality under certain conditions.

Computational Intelligence as a Platform for a Data Collection Methodology in Management Science

2011 ◽  
pp. 38-54
Author(s):  
Kristina R. Jespersen
Keyword(s):  
Data Collection ◽  
Computational Intelligence ◽  
Role Play ◽  
Management Science ◽  
Agent Based Modeling ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Technological Support ◽  
Business Applications ◽  
New Generation

With an increased focus in management science on how to collect data close to the real world of managers, we consider how agent-based simulations have interesting prospects that are usable for the design of business applications aimed at the collection of data. As an example of a new generation of data collection methodologies, this chapter discusses and presents a behavioral simulation founded in the agent-based simulation life cycle and supported by Web technology. With agent-based modeling the complexity of the method can be increased without limiting the research as a result of limited technological support. This makes it possible to exploit the advantages of a questionnaire, an experimental design, a role-play and a scenario, gaining the synergy of a combination of these methodologies. At the end of the chapter an example of a simulation is presented for researchers and practitioners to study. 1

QUANTIFYING THE IMPACT OF CONGESTION ON CONSTRUCTION LABOR PRODUCTIVITY USING AGENT-BASED MODELING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Rahal ◽  
Hiam Khoury
Keyword(s):  
Labor Productivity ◽  
Spatial Configuration ◽  
Quantitative Model ◽  
Agent Based Modeling ◽  
Direct Impact ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Future Work ◽  
The Impact ◽  
Construction Trades

Several findings from the construction field stipulate that productivity falloffs are primarily management-related; however, this notion does not consider the direct impact of these same management decisions on the workers themselves. For instance, the planning of the workspace layout delves in a spatial configuration which if not properly managed can potentially result in congestion that, in turn, directly affects labor productivity. Previous research efforts developed models to analyze the effect of congestion on labor productivity but failed to capture all the complexities of this mechanism and its dynamics. Therefore, this paper puts forward the groundwork of an agent-based simulation model (ABM) and presents work targeted at quantifying the impact of congestion on the productivity of construction crews. More specifically, the ABM model takes into account two construction trades working in the same area and tackles five scenarios each depicting different congestion and interaction levels. At the heart of this simulation is a quantitative model that defines essential congestion metrics and outputs space interference values. Experiments were conducted and results highlighted that the higher the space interference values the less productive the crews become. Additionally, these values will constitute an integral part in future work when studying the impact of congestion on the crews' learning curve, whereby the latter being a major gauge for levels of productivity.

Agent-based modeling to inform flood emergency planning and management

2011 ◽  
Vol 9 (1) ◽  
pp. 71 ◽  
Author(s):  
Manuela Di Mauro, BEng, MEng, PhD ◽  
Darren Lumbroso, MEng, MSc, CEng ◽  
Andy Tagg, MEng, CEng
Keyword(s):  
Emergency Management ◽  
Historical Data ◽  
Management Strategies ◽  
Agent Based Modeling ◽  
Emergency Planning ◽  
Sensitivity Analyses ◽  
Robust Method ◽  
Loss Of Life ◽  
Building Collapse ◽  
Agent Based

Objective: Agent-based modeling can provide powerful tools to inform flood emergency management and to provide an assessment of loss of life due to a flood event. The objective of this work is to study the suitability and robustness of this type of models for being applied in practice in managing flood emergencies.Design: This article describes the application of a prototype, agent-based Life Safety Model (LSM) to two populated areas in the Thames Estuary. Parameters sensitivity analyses have also been performed to assess the robustness and the applicability of this model as part of the actual emergency practice.Results: The model of the two areas resulted in the estimation of the number of fatalities for each scenario for different causes such as drowning, exhaustion, building collapse, and vehicles being swept away. The model was also successfully validated against historical data from the 1953 Canvey Island flood.Conclusions: The LSM offers a scientifically robust method of assessing injuries and lives lost, and it allows the comparison of different emergency management strategies that could assist in reducing the loss of life during future flood incidents.

On the contributions and the validation of an agent-based simulation model of innovation diffusion

2016 ◽  
Vol 50 (3/4) ◽  
pp. 647-657 ◽  
Author(s):  
Mohammad G. Nejad
Keyword(s):  
Simulation Modeling ◽  
Agent Based Modeling ◽  
Test Theory ◽  
Future Research ◽  
Agent Based Simulation ◽  
Content Type ◽  
Agent Based ◽  
Points Of View ◽  
Complex Phenomena ◽  
Specific Paper

Purpose This paper provides an overview of agent-based modeling and simulation (ABMS) and evaluates the questions that have been raised regarding the “assumptions and mechanisms used” by a well-cited paper that has used this methodology. Design/methodology/approach This work provides a review of agent-based simulation modeling and its capabilities to advance and test theory. The commentary then evaluates and addresses the raised questions and reservations. Findings Agent-based modeling offers unique capabilities that can be used to explore complex phenomena in business and marketing. Some of the raised reservations may be considered as directions for future research. However, the criticisms are for most part unsupported by existing research and do not undermine the contributions of the paper that is being discussed. Practical implications Given its relative novelty, reservations regarding agent-based simulation modeling are quite natural. Discussions like this one would bring together different points of view and lead to a better understanding of how using ABMS can benefit academia and industry. Originality/value This commentary is part of an intellectual dialogue that seeks to provide different points of view about agent-based simulation modeling using a specific paper as an example.

scholarly journals GETTING AWAY FROM NUMBERS: USING QUALITATIVE OBSERVATION FOR AGENT-BASED MODELING

2008 ◽  
Vol 11 (02) ◽  
pp. 175-185 ◽  
Author(s):  
LU YANG ◽  
NIGEL GILBERT
Keyword(s):  
Social Sciences ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Participant Observation ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Qualitative Observation

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

scholarly journals Agent-Based Modeling

2016 ◽  
Vol 56 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Sarah Nicholls ◽  
Bas Amelung ◽  
Jillian Student
Keyword(s):  
Complex Systems ◽  
Autonomous Agents ◽  
Agent Based Modeling ◽  
Potential Outcomes ◽  
Tourism Planning ◽  
Agent Based ◽  
Modeling Techniques ◽  
Potential Applications ◽  
Planning Development

Agent-based modeling (ABM) is a way of representing complex systems of autonomous agents or actors, and of simulating the multiple potential outcomes of these agents’ behaviors and interactions in the form of a range of alternatives or futures. Despite the complexity of the tourism system, and the power and flexibility of ABM to overcome the assumptions such as homogeneity, linearity, equilibrium, and rationality typical of traditional modeling techniques, ABM has received little attention from tourism researchers and practitioners. The purpose of this paper is to introduce ABM to a wider tourism audience. Specifically, the appropriateness of tourism as a phenomenon to be subjected to ABM is established; the power and benefits of ABM as an alternative scientific mechanism are illuminated; the few existing applications of ABM in the tourism arena are summarized; and, a range of potential applications in the areas of tourism planning, development, marketing and management is proposed.

scholarly journals Shared behavioral mechanisms underlie C. elegans aggregation and swarming

2018 ◽  
Author(s):  
S Serena Ding ◽  
Linus J. Schumacher ◽  
Avelino E. Javer ◽  
Robert G. Endres ◽  
André EX Brown
Keyword(s):  
A Priori ◽  
Population Level ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Individual Level ◽  
C Elegans ◽  
Behavioral Rules ◽  
Level Statistics ◽  
Approximate Bayesian ◽  
Behavioral Mechanisms

AbstractIn complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic C. elegans uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.

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