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Shared Autonomous Vehicles (SAVs) are expected to have a transformative role in future transportation systems, by reducing vehicle ownership, helping in alleviating congestion, improving accessibility and traffic safety, and changing travel behavior and urban infrastructure. The potential introduction of SAVs in transportation systems has triggered the need of exploiting suitable tools for designing and planning SAV operations and services and assessing their impacts. An explicit category of such tools are agent-based models (ABMs), whose advantage in efficiently representing transportation systems with a fine level of detail, has allowed them to gain importance in modeling SAVs. This paper systematically reviews and organizes the current state-of-the-art on ABMs dealing with SAVs. The review is two-fold: first, the methodological aspects of exploiting ABMs in the context of SAV services and operations are analyzed and second, ABM-based findings on the anticipated impacts of SAVs to traffic, travel behavior, land uses, the environment and so on, are presented and discussed. The paper concludes with recommendations for future research on SAVs and other, potential ABM applications for that purpose. [ABSTRACT FROM AUTHOR]

This paper introduces an agent-based micro-simulation model to acquire the responses of road freight transportation systems like the productivity of trucks, the capability of road freight transportation companies, and the economic state of actors to different regulatory reforms. The shippers, freight companies, and drivers are the key decision-makers. These agents have different choices, including transport carrier choice, shipping price, truck allocation to shipment, and empty backhauls. Performance measures were used for the calibration phase with less than 3.7% average error. Finally, three viable regulatory reforms were evaluated, and their performance measures were estimated and discussed. We found that the shipping cost will reduce by 6% if a competitive market structure replaces the mandatory freight assignment procedure. Furthermore, we observed a 20% increase in the market share of the top fifty transport companies and a 3% improvement in the overall productivity when the physical freight companies' branch requirements are relaxed. [ABSTRACT FROM AUTHOR]

The high amount of hazardous medical waste involves high risks, so optimising waste management processes is crucial. Some research proposes hybrid modelling that combines simulations and operation research approaches, and most hybrid modelling methods focus on optimising waste transport routes. This paper proposes hybrid modelling to optimise the number of workers with minimum asynchronous waiting time (AWT) and activity costs based on waste management collaboration processes. Hybrid modelling consists of an integrated discrete-event simulation, agent-based simulation and improved MCDM methods (MOORA and COPRAS). The cases of waste management processes under normal and overload conditions verify the performance of the proposed hybrid modelling. Improved MCDM methods save 27% of MCDM processing time. The AWT and activity cost under normal condition using the hybrid modelling decreased by 38 % and 22 %, respectively. Hybrid modelling can minimise 74 % AWT and 31 % activity cost compared to the actual model under an overload condition. MOORA is better when reducing activity cost, and COPRAS is better when minimising AWT. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

This paper describes Fraport's practical approach towards the implementation of a comprehensive passenger flow management solution. The combination of data preparation, forecasting and simulation, in conjunction with real-time information regarding the terminal situation, helped to improve passenger service. An approach, not used before at airports to forecast the distribution of passengers within terminal buildings in almost real-time, is introduced. Further extension of the programme, in the future, could be to use derived data to provide information to passengers directly, eg by the use of smartphones. [ABSTRACT FROM AUTHOR]

The lack of visibility due to foggy conditions is known to cause of a lot of accidents every year in the United Arab Emirates, eventually leading to fatal injuries. Yet, today's technology can help to overcome these visibility issues by providing dynamic information to the driver about the current weather and an appropriate speed limit. This paper explores four strategies, ranging from static road signs to advanced inter-vehicular communication, to better warn the drivers and make them adapt their speed depending on the weather. To evaluate the impact of each policy, agent-based simulations are designed and performed. The results show that a dynamic communication about the weather conditions, supported by either an infrastructure-to-vehicle or a vehicle-to-vehicle protocol, can reduce the probability of occurrence of accidents. [ABSTRACT FROM AUTHOR]

This paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community). We discover that majority‐explorer teams are to be preferred when complexity is high and over the long run, whereas majority‐exploiter teams are more effective in the short run and at low complexities. Furthermore, we show that higher levels of specialization yield better results at higher complexities, and that majority‐explorer teams benefit the most from higher levels of density. We conclude that different team compositions are to be preferred at different stages of maturity, and that selecting a time horizon for operations is of crucial importance when designing an innovation team. [ABSTRACT FROM AUTHOR]

Traffic planning and management is an important task for governments to achieve a proper functioning of the cities. The configuration of road signs and traffic lights is part of this process. The selection of an appropriate configuration is modeled as a variant of the traffic light cycle optimization problem (TLCOP), which is NP-Hard. To evaluate the impact of each configuration, agent-based simulations can be performed. Based on the importance and complexity of the problem, the objective of this paper is to present and evaluate a metaheuristic scheme to solve it and analyze solutions obtained. The algorithms were applied to six zones of Havana, Cuba. Among all the algorithms used, the genetic algorithm obtained the best performance. [ABSTRACT FROM AUTHOR]

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ± 10 % variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector. • Introduced an agent-based model to simulate residential consumer enrollment in DR. • Utilized economic and noneconomic factors to identify consumer decision-making to enroll. • Introduced different enrollment contract types in DR for consumers. • Analyzed different parameters influencing consumer enrollment rates. [ABSTRACT FROM AUTHOR]

Industry 4.0 leads to a strong digitalization of industrial processes, but also a significant increase in communication and cooperation between the machines that make it up. This is the case with autonomous industrial vehicles (AIVs) and other cooperative mobile robots which are multiplying in factories, often in the form of fleets of vehicles, and whose intelligence and autonomy are increasing. While the autonomy of autonomous vehicles has been well characterized in the field of road and road transport, this is not the case for the autonomous vehicles used in industry. The establishment and deployment of AIV fleets raises several challenges, all of which depend on the actual level of autonomy of the AIVs: acceptance by employees, vehicle location, traffic fluidity, collision detection, or vehicle perception of changing environments. Thus, simulation serves to account for the constraints and requirements formulated by the manufacturers and future users of AIVs. In this paper, after having proposed a broad state of the art on the problems to be solved in order to simulate AIVs before proceeding to experiments in real conditions, we present a method to estimate positions of AIVs moving in a closed industrial environment, the extension of a collision detection algorithm to deal with the obstacle avoidance issue, and the development of an agent-based simulation platform for simulating these two methods and algorithms. The resulting/final/subsequent simulation will allow us to experiment in real conditions. [ABSTRACT FROM AUTHOR]

At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phúc Xá district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network. [ABSTRACT FROM AUTHOR]

One of the challenges for agent-based modeling is being able to incorporate human behavior. Human behavior is a multifaceted phenomenon, with strategic coalition formation being one form. A hybrid agent-based modeling approach, called ABMSCORE, has been derived to emulate strategic group formation. In this paper, we describe a simulation experiment to compare the ABMSCORE with actual human behavior. The comparison criterion is the respective rates of finding an ideal coalition. In our experimental design, we go to great lengths to ensure the similarity of the scenarios in the two trial types: trials with computerized agents only and trials involving human participants when one of the computerized agents is replaced by an actual human. We did this to limit the number of possible extraneous variables introduced into the experimental system. The scenario considered is the glove game, a standard cooperative game that has been previously used in human experiments. Our results indicate that the ABMSCORE model produces similar rates of finding the ideal coalition as the human players; however, there are some limitations. This research provides evidence for using the ABMSCORE modeling approach to model human strategic coalition formation in agent-based models. [ABSTRACT FROM AUTHOR]

The post-disaster humanitarian logistic structure (PD-HL) used in a disaster determines the performance of the relief distribution, which impacts human suffering and loss of lives. This paper develops an Agent-Based simulation model to explore the performance of three different PD-HL structures: Agency Centric Efforts (ACEs), Partially Integrated Efforts (PIEs), and Collaborative Aid Networks (CANs). Using the proposed simulation framework, the authors analyze the role of information sharing and crowd movement in the distribution of relief supplies, and evaluate the ability of different PD-HL structures to respond to disasters of varying intensities. The performance of each PD-HL structure is measured using the social costs, i.e., the sum of logistic costs incurred by the relief organizations carrying out logistical activities, and the deprivation costs and walking costs of affected people. The study also discusses the humanitarian operations during the 2010 Port-au-Prince earthquake to demonstrate the real-world application of the simulation framework. The results suggest that sharing information can reduce the deprivation costs in general. However, as the level of information sharing increases, its marginal effect on deprivation costs decreases. Setting up temporary shelters to limit the movement of affected people can help reduce uncertainty in demands, thereby facilitating the distribution of relief supplies. The distribution capacity is the primary factor affecting the performance of the three PD-HL structures. The results indicate that CANs are particularly effective in responding to catastrophic disasters due to their high distribution capacity. However, ACEs and PIEs can also perform well in situations where their distribution capacities are adequate to ensure that available supplies reach the affected population. In addition, the waiting time of disaster-affected people at the points of distribution (PODs) significantly affects the distribution of relief supplies, while walking speed has no significant impact on the distribution of relief supplies. • This paper develops an Agent-Based simulation model to explore the performance of three Post-Disaster Humanitarian Logistic (PD-HL) structures. • Using the proposed simulation framework, the authors analyze the roles of information sharing and crowd movement in the distribution of relief supplies. • The research also discusses the humanitarian operations in the 2010 Port-au-Prince earthquake to explain how the simulation framework can be applied to real-world cases. • The findings of this study suggest that sharing information and controlling the movement of survivors can help improve the distribution efficiency. [ABSTRACT FROM AUTHOR]

The continuous advancement of connected vehicle (CV) technologies and its increasing market penetration (MP) will change the nature of traffic flow fundamentals by upending the traditional traffic composition. The objective of this paper is to create an agent-based modeling framework to evaluate the impact of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) technology on the mobility performance (i.e., travel time and its reliability) of a two-lane highway work zone scenario. This paper studies when and at what MP will the mobility benefits of connected vehicles emerge. Results showed that the mobility benefits are highly dependent on the CVs MP levels and traffic flow rates. In addition, the study found that the higher the traffic flow rate is, the higher MP level is required to enable the mobility benefits in the mixed traffic environment. Research findings will inform agency professionals, traffic engineers, and planners the achievable benefits that could accompany the implementation of CV technologies in diverse roadway settings such as work zone. Moreover, the results will help the decision-makers in agencies as state DOTs to develop effective resource allocation strategies and evaluate the cost and benefit ratio of deploying this technology in highway work zone scenario. [ABSTRACT FROM AUTHOR]

This paper deals with how to recognise if the patterns of homophily found in a social network are resilient to small disturbances that may occur in that network. Data from a survey of students in a secondary school in the Canary Islands were replicated using an agent-based model. The model calculated homophily indices and their statistical significance and then simulated small alterations in the distribution of links. The results clearly show that some homophily indices resist these kinds of perturbations and others do not. Evidence suggests that the distribution of individuals across the social network communities is a key factor in explaining why certain patterns of relationships are more resilient than others. [ABSTRACT FROM AUTHOR]

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources. [ABSTRACT FROM AUTHOR]

The South African sunflower industry is considering transferring to a quality-based marketing system driven by an incentive. However, the ability of silos to offer necessary segregation services is critical in such a transition (Baker et al., 1997) and the silo industry is concerned about the negative impact segregation could have on operations and finances. This paper proposes a multi-method simulation approach to quantify the impact of quality-based segregation and sunflower farmer response to the incentive on silo bin utilisation and the ability of the silo to store contents of arriving trucks (service level). A combination of agent-based simulation, discrete event simulation and Bayesian network sampling is used to capture system behaviour where data is scarce. Therefore, in this study, a mixed methods ABM and DES model is implemented in a new environment: a grade-based segregation problem in the South African silo industry. Several scenarios are modelled to cross-validate methods and to tease out the impact of farmer response on the results. The model is applied to a case study silo complex to test the concept. Results obtained for the case study silo show a significant negative impact on costs due to lower service levels and bin utilisation, incurring relocation and opportunity costs. Overall, this study highlights that it is necessary to consider the impact that sunflower segregation could have on each unique silo complex and provides a method to quantify the stated impact. • A model is developed to analyse the impact of quality-based sunflower segregation on a silo. • An agent-based model is used to solve a silo segregation problem within a new environment: the South African context. • An expert-driven Bayesian network is used to overcome a data scarcity constraint. • For a case study silo, a 6%–11% reduction in financial performance was found relative to the base case. [ABSTRACT FROM AUTHOR]

Due to more and more awareness of environmental conservancy and sustainable development, automated container terminals (ACTs) have gained increasing attention and become popular worldwide. However, there is a crucial challenge in implementing ACTs, i.e., how to develop terminal layout planning (including terminal layout and facility selection) to realize performance goals. Focusing on this challenge, the aim of this paper is to resolve how to appropriately select terminal layout and facilities in ACTs with respect to various performance criteria. Several research limitations can be gathered from the existing literature: (i) Most of the existing works mainly develop optimization technologies to tackle layout optimization and facility comparison issues within a few types of terminal layout and facilities, yet few researchers have proposed evaluation methods for making selection decisions on terminal planning. (ii) Only a few types of criteria are considered, and requirements and preferences of different participants are ignored, not to say realizing an optimal compromise by employing comprehensive criteria weights in evaluation processes. (iii) New technological improvement in ACTs has not received much attention, and there still lacks a method to precisely quantify the concerned criteria. Accordingly, this paper introduces a criteria system comprising economic, environment, social, resource and performance groups and develops an agent-based simulation method considering different terminal layout and facilities to determine criteria values. A multi-criteria decision making (MCDM) framework is proposed for making the most reasonable and appropriate selection decisions, which employs a variation coefficient weighted method to calculate comprehensive criteria weights, which integrate subjective and objective weights obtained from AHP (analytic hierarchy process) method and entropy weight method. To overcome rank reversal problems in the TOPSIS (technique for order preference by similarity to an ideal solution) method, a new improved rank reversal robust TOPSIS method, called NR-TOPSIS, is utilized to rank and select alternatives, which is ranking stable in the experiments performed. An illustrative application on a container terminal in China demonstrates the effectiveness of the proposed MCDM method and sensitivity and comparative analysis verifies the robustness and stability of the method. [ABSTRACT FROM AUTHOR]

In the past, most companies in the European apparel industry focused on minimizing manufacturing costs in the design of supply chains in conjunction with long-distance shipping from production sites in the Far East and relatively long production cycles. Today, for some market segments, the speed of production cycles is more important than the cost because short throughput time allows the flexibility to adjust to rapidly changing fashion trends in these market segments. Accordingly, choosing the most beneficial supply chain strategy has become an established research topic. However, apparel markets are complex systems. Therefore, attempts to reduce the underlying complexity in order to model these markets have limited existing research to the consideration of only selected aspects of markets (e.g., considering only homogeneous buyers, a single period, a single product, or a single manufacturer in the absence of competition) rather than taking a more comprehensive view. These limitations can be overcome by applying an agent-based simulation approach—an approach that can account for a wider range of factors, including several competing manufacturers utilizing different supply chain strategies, individual consumer preferences and behavior, word-of-mouth communication, normative social influence, and first-hand experience, as well as advertising. In this paper, the capability potential of such agent-based market simulation is demonstrated by investigating two supply chain strategies (fast fashion vs. traditional fashion) with varying product and communication strategies (product attributes and advertising) in several market scenarios. [ABSTRACT FROM AUTHOR]

Edge Computing in 5G and Beyond is a promising solution for ultra-low latency applications (e.g., Autonomous Vehicle, Augmented Reality, and Remote Surgery), which have an extraordinarily low tolerance for delay and require fast data processing for a very high volume of data. The requirements of delay-sensitive applications (e.g., Low latency, proximity, and Location/Context-awareness) cannot be satisfied by Cloud Computing due to the high latency between User Equipment and Cloud. Nevertheless, Edge Computing in 5G and beyond can promise ultra-high-speed data processing thanks to the placement of computation capabilities closer to endpoint devices, where 5G encourages the speed rate that is 10 times faster than 4G LTE-Advanced. This paper deeply investigates Edge Computing in 5G and characterizes it based on the requirements of ultra-low latency applications. As a contribution, we propose a hybrid architecture that takes advantage of novel and sustainable technologies (e.g., D2D communication, Massive MIMO, SDN, and NFV) and has major features such as scalability, reliability, and ultra-low latency support. The proposed architecture is evaluated based on agent-based simulations demonstrating that our proposal can satisfy requirements and has the ability to respond to high volume demands with low latency. [ABSTRACT FROM AUTHOR]

• A data-driven agent-based model compared charging station roll-out strategies. • Increasing size, due to return to scale effects result in utilisation efficiency. • Policy makers face a trade-off between providing accessibility & convenience. • Mixed roll-out strategies (Level 2, hubs, Fast) satisfies EV driver demand best. On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology based on agent-based simulation to define the new optimum airport level-of-service (LOS) that guides cost-efficient terminal design while providing passengers with a reasonable LOS. It aims to allow at least 50% of passengers to experience the new optimum LOS range while cutting off LOS values during peak and off-peak periods. The simulation also helps to adopt the standard cut-off combination and test the applicability of the methodology. The test reveals that the proposed methodology is useful for establishing an alternative LOS standard that is more cost-efficient and flexible than the conventional IATA standard and could be applicable for other public transport terminals. [ABSTRACT FROM AUTHOR]

Abstract Effective management of demand information is a critical factor in the successful operation of autonomous mobility-on-demand (AMoD) systems. This paper classifies, measures and evaluates the demand information for an AMoD system. First, the paper studies demand information at both individual and aggregate levels and measures two critical attributes: dynamism and granularity. We identify the trade-offs between both attributes during the data collection and information inference processes and discuss the compatibility of the AMoD dispatching algorithms with different types of information. Second, the paper assesses the value of demand information through agent-based simulation experiments with the actual road network and travel demand in a major European city, where we assume a single operator monopolizes the AMoD service in the case study area but competes with other transportation modes. The performance of the AMoD system is evaluated from the perspectives of travelers, AMoD operators, and transportation authority in terms of the overall system performance. The paper tests multiple scenarios, combining different information levels, information dynamism, and information granularity, as well as various fleet sizes. Results show that aggregate demand information leads to more served requests, shorter wait time and higher profit through effective rebalancing, especially when supply is high and demand information is spatially granular. Individual demand information from in-advance requests also improves the system performance, the degree of which depends on the spatial disparity of requests and their coupled service priority. By designing hailing policies accordingly, the operator is able to maximize the potential benefits. The paper concludes that the strategic trade-offs of demand information need to be made regarding the information level, information dynamism, and information granularity. It also offers a broader discussion on the benefits and costs of demand information for key stakeholders including the users, the operator, and the society. [ABSTRACT FROM AUTHOR]

• An agent-based simulation structure is developed for parking policymaking. • A simulation model of Kingsford town centre, Sydney, Australia is implemented. • A pricing formulation is proposed which accounts for parkers utilities. • An IPS system is introduced to quantify potential improvements of a parking zone. Car parking policies often aim to relieve or better distribute parking demand in central urban areas. Evaluating their effectiveness requires the investigation of traveller behaviour and responses to parking attributes such as availability, cost, walking distance, and time restriction. Parking policies, which are usually cost-intensive to implement, are crucial in solving parking problems and transportation system problems in general. To investigate parking policy properly, developing agent-based parking systems is beneficial. Motivated by a recent parking management system designed for Kingsford, Sydney, this paper introduces an agent-based simulation model to analyse the effects of different parking policies in a realistic dynamic framework. Furthermore, a novel behavioural pricing formulation is integrated into the simulation model, which dynamically seeks to maximise the total utility of all agents in the system with consideration of their travel behaviours. The model is implemented for the Kingsford town centre, to investigate the effects of different parking policies and demand scenarios on parking utilisation and system performance. [ABSTRACT FROM AUTHOR]

With the growing demand for hospital resources worldwide, healthcare facilities, including Emergency Departments (EDs), look for opportunities to enhance patient care efficiency and streamline healthcare processes. To achieve this, simulation techniques, including Discrete-Event Simulation (DES) and Agent-Based Simulation (ABS), started gaining awareness; however, little is known regarding their applications and integration with various optimization methods and scenario-generation techniques in EDs. To address this research gap, we used bibliometric analysis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to narrow the publications to 62 articles and conduct a comprehensive review. During the evaluation of each article, three dimensions, namely the area of application, outcomes, and Key Performance Indicators (KPIs), were reviewed to understand the development and implementation of simulation techniques in EDs. The results shed light on the vital impact of process and time variations on simulation quality and the importance of involving healthcare professionals in simulation validation and verification. Furthermore, this study provided a conceptual framework that identified gaps and solutions related to the model scopes, approaches, technologies, and implementation of the DES and ABS in EDs. These findings could prove useful for healthcare researchers, practitioners, and managers in advancing the future research directions of DES and ABS and practical implications in the decision-making process. [Display omitted] • Discrete-Event and Agent-Based Simulation in Emergency Departments are reviewed. • PRISMA framework, bibliometric analysis and comprehensive review are applied. • Growing significance of simulation analysis for improving ED service is observed. • A structured three-dimensional reviewing approach is applied to selected papers. • Framework of gaps and future directions for DES and ABS in EDs is proposed. [ABSTRACT FROM AUTHOR]

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number 1.0). [ABSTRACT FROM AUTHOR]

Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides , defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS. [ABSTRACT FROM AUTHOR]

Road network microsimulation is computationally expensive, and existing state of the art commercial tools use task parallelism and coarse-grained data-parallelism for multi-core processors to achieve improved levels of performance. An alternative is to use Graphics Processing Units (GPUs) and fine-grained data parallelism. This paper describes a GPU accelerated agent based microsimulation model of a road network transport system. The performance for a procedurally generated grid network is evaluated against that of an equivalent multi-core CPU simulation. In order to utilise GPU architectures effectively the paper describes an approach for graph traversal of neighbouring information which is vital to providing high levels of computational performance. The graph traversal approach has been integrated within a GPU agent based simulation framework as a generalised message traversal technique for graph-based communication. Speed-ups of up to 43 ×  are demonstrated with increased performance scaling behaviour. Simulation of over half a million vehicles and nearly two million detectors at a rate of 25 ×  faster than real-time is obtained on a single GPU. [ABSTRACT FROM AUTHOR]

The elderly population is increasing and the response of the society was to provide them with services directed to them to cope with their needs. One of the oldest solutions is the retirement home, providing housing and permanent assistance for the elderly. Furthermore, most of the retirement homes are inhabited by multiple elderly people, thus creating a community of people who are somewhat related in age and medical issues. The ambient assisted living (AAL) area tries to solve some of the elderly issues by producing technological products, some of them dedicated to elderly homes. One of the identified problem is that elderly people are sometimes discontent about the activities that consume most of their day promoted by the retirement home social workers. The work presented in this paper attempts to improve how these activities are scheduled taking into account the elderlies' emotional response to these activities. The aim is to maximize the group happiness by promoting the activities the group likes, minding if they are bored due to activities repetition. In this sense, this paper presents an extension of the Cognitive Life Assistant platform incorporating a social emotional model. The proposed system has been modelled as a free time activity manager which is in charge of suggesting activities to the social workers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel probabilistic framework to quantitatively evaluate the resilience of communities comprising buildings and various interdependent infrastructure systems. To this aim, the proposed framework seamlessly integrates risk models and agent-based simulation in a Monte Carlo sampling scheme. The risk module includes models that evaluate the initial posthazard state of the community by probabilistic simulation of the hazard event, the structural response and damage of buildings and infrastructure systems, and cascading consequences that arise from interdependencies. Subsequently, the agent-based module simulates the recovery of the community from those consequences in which decentralized autonomous decision-making entities called "agents" undertake recovery operations. The agents prioritize buildings and infrastructure components for recovery and schedule operations as discrete events with uncertain duration and cost. Consequently, the probability distribution of the total cost incurred by the community and the total recovery time is evaluated. A resilience measure is then proposed as a function of the total community cost, which represents demand, and the gross regional product of the community, which represents the capacity to cope with that demand. The framework is showcased by a comprehensive application to a community comprising a portfolio of residential and commercial buildings, an electric power system, a water system, and a healthcare system subject to seismic hazard. [ABSTRACT FROM AUTHOR]

Parking spaces are insufficient and are plagued by over-consumption in hot areas. To assist drivers easily in identifying available parking spaces, parking variable message signs are commonly adopted to display information on space availability. This paper analyzes the performance of various information provision strategies. To achieve this objective, we first present the mechanisms of the information provision strategies. Then, the information provision strategies are classified into three categories: regular, symmetric, and discriminative. The regular strategies provide the collected parking information directly to drivers; the symmetric schemes employ the equal threshold values for all parking lots; and the discriminative schedules adopt an independent threshold value for each parking lot. The threshold value provides an upper limit for the Space Occupancy Percentage (SOP): when the SOP is larger than the threshold value, the parking lot status becomes FULL; otherwise, it is displayed having available spaces. Finally, an agent-based simulation model is introduced to describe the parking and traffic conditions. The results indicate that both the symmetric and discriminative strategies significantly decrease the highest failure rate and average travel time, whereas the latter performs better. The results of this comparative analysis can assist in the configuration and operation of an urban parking guidance and information system. [ABSTRACT FROM AUTHOR]

This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the need for two separate fleets into a single one by combining the services. This research provides an extensive literature review and discusses policy measures supporting such a service. To evaluate the potential of such a mobility-on-demand service, this paper uses an agent-based simulation framework and integrates three heuristic parcel assignment strategies into a ride-pooling fleet control algorithm. Two integration scenarios (moderate and full) are set up. While in both scenarios passengers and parcels share rides in one vehicle, in the moderate scenario no stops for parcel pick-up and delivery are allowed during a passenger ride to decrease customer inconvenience. Using real-world demand data for a case study of Munich, Germany, the two integration scenarios together with the three assignment strategies are compared to the status quo, which uses two separate vehicle fleets for passenger and logistics transport. The results indicate that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacities without deteriorating passenger transportation. Depending on the assignment strategies nearly all parcels can be served until a parcel to passenger demand ratio of 1:10 while the overall fleet kilometers can be decreased compared to the status quo. [ABSTRACT FROM AUTHOR]

Recently, the number and size of natural or manmade disasters have increased, leading to significant casualties and enormous economic losses. Disaster is any occurrence that causes damage, ecological disruption, human life or economic loss in a particular area. Massive disasters such as a nuclear disaster can cause a massive damage to the environment and human life. Disaster evacuation is the only way to save the human life after a disaster occurrence. However, the development of an evacuation plan requires many considerations on several factors such as the number of people, the number of shelters, the road network, the traffic congestions and more. The simulation for disaster evacuation can be a useful decision-making tool for the evacuation planner by conducting various evacuation scenarios for analyzing the best evacuation plan. This paper presents a formal model of the agent-based simulation (ABS) for the development of a nuclear disaster evacuation planning tool. In this effort, the agent represents the people that evacuate during the nuclear disaster through the road network. The agent movement behavior is determined by the knowledge and the reward function by implementing the Markov Decision Process (MDP) on the road network. A simulation tool is developed with experiments to demonstrate the effectiveness of the proposed formal model. The simulation results provide an insight for the evacuation planner in designing an efficient evacuation plan in a certain disaster event. The proposed formal model can be used for a useful tool to develop the evacuation strategy. [ABSTRACT FROM AUTHOR]

This paper presents the first results of a large-scale-Agent-Based Simulation devoted to simulate individual behaviour inside a medium sized city (600,000 inhabitants). Humans are simulated as Intelligent Individual entities characterized by several attributes created from the Open Data available by means of a multi-layer approach. The work presented is divided into two main parts: the first part aims to describe the multi-layer approach adopted with the inclusion of the social network layer devoted to capture how social networks can be correlated with human activities and how an "Individual Opinion" can changes based on social interactions. The second part is devoted to present a preliminary case study for simulating the propagation dynamics of the individual opinion in the form of an ethical value function. The basic idea is to capture the changes in the individual opinion based on the social interactions predicted by the simulation. Finally, a food choice model for predicting individual choices based on the individual opinion function is presented; the model is based on three parameters: accessibility of ethical shops, price difference with standard products, and ethical value propagation. [ABSTRACT FROM AUTHOR]

Many studies show that the human energy-related behaviors have a significant impact on the return of Energy Efficiency Programs (EEPs). However, studies that aimed at increasing the energy savings from the EEPs are still limited. In this paper, a Genetic Agent-Based (GAB) framework has been proposed to enhance the return of a typical EEP by simulating social network and energy behavior attributes and finding the best participants among a target community. Several attributes are considered for creating the agent-based model of households and numerically representing their interactions with the EEP or within their social network. The improvement of the EEP using the GAB framework is tested on a social network consisting of 56 households. The simulation results show that by accurately selecting participants using the presented framework, the amount of energy saving could increase up to ten times. This ultimately indicates the considerable impact of the social network on the EEP performance. In other words, to have an efficient EEP in the long term, the social network attributes such as network degree and strength of connections should be also considered in decision-making along with the energy-related attributes. [ABSTRACT FROM AUTHOR]

With growing city density and mass gatherings held all over the world in urban spaces, crowd disasters have been happening each year. In considering the avoidance of crowd disasters and the reduction of fatalities, it is important to analyze the efficient spatial layout of the public space in situations of high crowd density. Compared with traditional empirical design methods, computational approaches have better abilities for quantitative analysis and are gradually being adopted in the planning and management of the urban public space. In this paper, we investigated the official documents, publicly available videos, and materials of the Shanghai waterfront crowd disaster which happened on December 31, 2014. Based on the investigation, a detailed site survey was conducted and pedestrian flow data were acquired. To test the influence of different spatial layouts, an agent-based simulator is built, following the ASPFver4.0 (Agent Simulator of Pedestrian Flow) pedestrian walking rules. With the surveyed pedestrian flow data, the original spatial layout of the Shanghai Bund waterfront together with five other comparison scenarios are tested, including both space design and crowd management improvements. In the simulation results, the efficiencies of different space design and crowd management solutions are compared. The results show that even simple crowd control measures such as capacity reserve and more proper route planning will allow for a positive improvement in crowd safety. The results also compare the efficiency of different spatial operations and give general suggestions to the problems urban public space designers should consider in high-density environments. [ABSTRACT FROM AUTHOR]

The domain of urban freight transport is becoming increasingly complex. Many urban supply chains are composed of small and independent actors that cannot efficiently organize their highly fragmented supply chains, thereby negatively affecting the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper presents an agent-based simulation model to quantify the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation in the form of a Markov decision process. Based on an extensive literature study, we aggregate data to represent various actors in typical Western European cities. We perform numerical experiments to obtain insights into urban logistics schemes. The results show that most schemes yield significant environmental improvements but that achieving long-term financial viability is challenging for urban consolidation centers in particular. We also demonstrate that interventions, such as subsidies and access restrictions, do not always yield the intended effect. In a backcasting experiment, we identify conditions and schemes to achieve a financially viable urban consolidation center. [ABSTRACT FROM AUTHOR]

Dancing Viennese Waltz in one of the great historic ballrooms is an important and indispensable part of Austrian culture. This dance, while being tradition, is quite difficult to perform, especially if the dance-floor is crowded. There, it is additionally challenging to avoid collisions with other dancers, as they pace through the ballroom at a high velocity. Dependent on the dancer's skill level, spinning speed can be adjusted to succeed. This paper presents an agent-based waltz model which makes it possible to investigate the influence of heterogeneously skilled dancers on the movement smoothness of the dancing crowd. Herein, each agent represents one dancing couple in reality and it moves on the dance-floor by a rotatory motion with periodically switching rotation axes. Interaction between agents occurs via inelastic collisions. By performing a couple of case studies, we analyse and quantify the widespread rumour that the presence of only a few unskilled dancers disturbs the flow of the dancing crowd. [ABSTRACT FROM AUTHOR]

In public transport disruptions, a key relation between the services of operating company and passengers' satisfaction is the disseminated information. This paper proposes rigorous mathematical descriptions to describe the effects of information availability to passengers, including the user equilibrium and non-equilibrium solutions. The information availability in disruption is summarised in a "who-when-where-what" four-dimensional framework. Based on different information, passengers' behaviours are assumed and simulated to evaluate the benefits of information availability in public transport disruption. An agent-based micro-simulation model (MATSim) is applied for the city of Zürich, Switzerland, for the benefit of activity-based simulation in a multi-modal network. We use an existing day-to-day replanning method, and extend the study with a within-day replanning approach in MATSim, to study agents' route choices responding to public transport disruption in one single iteration. The disruption is assumed as a rail track blockage between Zürich HB and Zürich Oerlikon via both Zürich Wipkingen and Zürich Hardbrücke. One benchmark of agents' behaviours without disruption and other three scenarios based on different information are simulated. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents' flow in related transit routes and transport modes shows their adaptations to the corresponding information availability. Agents' delays and scores reveal that information significantly impacts agents' satisfaction in public transport disruption. [ABSTRACT FROM AUTHOR]

In vitro biological experiments and in silico individual-based computational models are widely used to understand the low-level behavior of cells and cellular functions. Many of these functions can not be directly observed, however, may be deduced from other properties that can be well measured and modeled. In this paper, we present a procedure to evaluate synthetic cell colony formation generated by an off-lattice individual-based model. The calculated shape features of the artificial cell aggregates can be related to the parameter values of the simulated agents, therefore this data can be used to quantify properties of real-life cells such as motility or binding affinity that can not be easily determined otherwise. Our experiments showed that only a few of these parameters are responsible for the difference in shape features of the colonies. [ABSTRACT FROM AUTHOR]

The increasing rate of hospital admissions has resulted in a commensurate increase in the number of treatments and surgeries performed, as well as resource and material usage, and requires planners to improve hospitals' internal logistics. Logistics modelling of internal goods and corresponding material handling systems and simulating future scenarios can provide planners with necessary decision support. Introducing an agent-based simulation model using historical data generated by an automated guided vehicle (AGV) in a case hospital facilitates analysing the goods delivery system's current status and potential countermeasures to improve internal logistics. In comparison with other industries, such as manufacturing, AGVs utilised in hospitals have to interact with persons, patients and elevators, transport several different types of goods and cover a sizeable multi-floor area. By including these factors, the simulation model represents an appropriate method to test different scenarios and improve delivery performance and AGV utilisation. The study highlights the constraints related to operating AGVs in dynamic environments, such as those encountered in hospitals. (Received in August 2019, accepted in October 2019. This paper was with the authors 1 month for 1 revision.) [ABSTRACT FROM AUTHOR]

Urban consolidation centers (UCCs) have a key role in many initiatives in urban logistics, yet few of them are successful in the long run. The high costs often prevent attracting a sufficient number of UCC users. In this paper, we study sustainable business models and the supporting role of administrative policies. We perform an agent-based simulation applied to the city of Copenhagen and collect data from a variety of sources to model the agents. Both the data and case setup are validated by means of expert interviews. We test 1,458 schemes that combine several administrative measures and cost settings. Most schemes yield significant environmental benefits; many of them reduce the truck kilometers driven by about 65% and emissions by about 70%. The key challenge is to identify schemes that are also financially sustainable. We show the importance of committing carriers to the UCC as soon as possible, as carriers potentially generate the bulk of the revenue. Subsequent revenues may be generated by offering value-adding services to receivers. Based on the numerical experiments, we pose various propositions that aid in providing favorable conditions for a UCC, improving its chances of long-term success. [ABSTRACT FROM AUTHOR]

Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems, as it involves balancing the demands of patients, the availability of resources, and the need to provide high-quality health in resource-bounded settings. Traditional approaches to HSRA have relied on manual planning and ad-hoc adjustments, which can be time-consuming and usually lead to sub-optimal outcomes. Recent studies show that machine learning solutions are able to produce better HSRA results compared to manual planning. However, these outcomes usually focused on a single hospital and objective. In this paper, we solve the HSRA task using a novel agent-based simulation with a deep reinforcement learning agent. We used real-world data to generate a wide range of synthetic instances that were used to train the HSRA agent. Our results show that the proposed model is able to achieve better outcomes in terms of patient treatment success and cost-effectiveness compared to previous resource allocation algorithms. We show that different planning horizons obtain similar performance in handling anomalies. In addition, we show a second-order polynomial connection between the patient treatment success and both the hospital's initial budget and funding over time. These results suggest that our approach has the potential to improve the efficiency and effectiveness of HSRA in healthcare systems. [ABSTRACT FROM AUTHOR]

Epidemiological-Mathematical models are powerful tools for estimating the course of a pandemic and exploring different scenarios through pandemic intervention policies (PIPs). These models are commonly developed to provide decision-support tools for policymakers who are forced to make difficult decisions in a timely manner. Done properly, these models are able to provide a safe, quick, and cheap solution for this challenge. There are numerous types of mathematical models for epidemiological disease spread and control. However, in order to become applicative tools for decision-making, the modelers of these models are required to overcome three computational challenges: efficiently define the model, develop it as computer software, and fit it into historical data. Performed efficiently, one can use the obtained tool to explore possible scenarios and PIPs. In this paper, we present a critical review of models that extend the Susceptible–Infected–Recovered (SIR) model and explore the efficiency of these models, their software characteristics, and model performance on real-world data. We further provide a guide for epidemiological-mathematical model development and implementation, exploring several modeling approaches and their respective implementation options. Lastly, we outline the current trends, limitations, and opportunities in this field. In particular, we find that the spatial properties of a model play a critical role in its accuracy and ability to explain historical pandemic spread, especially in the context of airborne diseases. Moreover, we show that agent-based simulations are preferable over partial/ordinary differential solvers when considering a highly-realistic pandemic model or focusing on a relatively small population size. • Spatio-temporal extended SIR models are useful tools to predict pandemic spread. • Intervention policies investigated by extended SIR have to take economics into account. • Agent-based simulation outperforms ODE-based modeling for most of the cases. [ABSTRACT FROM AUTHOR]

This paper presents an agent-based human behavioral modeling framework to analyze probable human actions, in emergency situations, considering both physical and psychological dimensions, in emergency situations. Human’s prospective controls suggest that the environment can offer certain physical and psychological conditions to opt for a finite number of feasible human actions that lead to desired system states. A set of possible human actions is then generated and updated from the affordance-effectivity duals in a spatial-temporal dimension. In this paper, a reward and cost-based dynamic affordance-based agent model is built upon physical and psychological constraints that are inserted for the agents’ decision-making processes. The model employs Markov Decision Process (MDP), and NASA-TLX (Task Load Index) is used as cost and reward estimates. The action selection process of human agents, i.e., triggering of state transitions, is stochastically modeled in accordance with the action-state cost (load) values. A series of affordance-based numerical values are calculated for predicting prospective actions in the system. Finally, an evacuation simulation example based on the proposed model is illustrated to verify the proposed human behavioral modeling framework. [ABSTRACT FROM AUTHOR]

This paper studies the effects of learning and risk aversion on generation company (GenCo) bidding behavior in an oligopolistic electricity market. To this end, a flexible agent-based simulation model is developed in which GenCo agents bid prices in each period. Taking transmission grid constraints into account, the ISO solves a DC-OPF problem to determine locational prices and dispatch quantities. Our simulations show how, due to competition and learning, the change in the risk aversion level of even one GenCo can have a significant impact on all GenCo bids and profits. In particular, some level of risk aversion is observed to be beneficial to GenCos, whereas excessive risk aversion degrades profits by causing intense price competition. Our comprehensive study on the effects of Q -learning parameters finds the level of exploration to have a large impact on the outcome. The results of this paper can help GenCos develop bidding strategies that consider their rivals’ as well as their own learning behavior and risk aversion levels. Likewise, the results can help regulators in designing market rules that take realistic GenCo behavior into account. [ABSTRACT FROM AUTHOR]

In contemporary workplace design, human performance is still limited to systemic exploration and rational trade-offs due to methodological issues. To resolve this problem, this paper investigates the effects of an agent-based simulation, equipped with anthropomorphic virtual users, on the design exploration of novice architects when designing collaborative workplaces. To achieve this aim, this paper developed a collaborative behaviour model of agents that computes visual inspiration and serendipitous encounters to conduct comparative lab experiments between non-use and use of this simulation. The resulting statistical analysis reveals that this simulation better facilitates novice architects' design exploration in terms of visual connectivity, collaboration supportability, workability, user satisfaction, and optimal trade-off. This paper addresses future studies for developing design methodology and advancing the agent-based simulation model in designing a highly collaborative workplace. • In designing highly collaborative workplaces, effects of agent-based simulation on the design exploration of novice architects were investigated. • A collaborative behaviour model of agents that computes visual inspiration and serendipitous encounters was developed. • Comparative lab experiments between nonuse and use of simulation were conducted. • Analysis revealed that agent-based simulation better facilitates novice architect design exploration and optimal trade-offs related to human factor-centric performances in workplaces. • Discovery- and data-driven design, facilitated by direct observation and holistic analytics using an agent-based simulation was interpreted as the reason. [ABSTRACT FROM AUTHOR]

Multimodal logistics (ML), which involves multiple transportation modes, has been increasingly used in many Modular integrated Construction (MiC) projects. However, the literature lacks decision support systems (DSS) to simulate, analyze, and optimize ML in MiC (ML-MiC). This paper fills this gap by achieving the following objectives: 1) simulate the internal operations of ML-MiC stakeholders (e.g., manufacturers, logistics service providers, contractors) and their interactions; 2) identify the significant decisions that impact the key performance measures (KPMs) of ML-MiC; and 3) obtain the near-optimum decisions that improve the sustainability of ML-MiC. These objectives are achieved by developing a holistic modelling approach that integrates three methods. First, hybrid multi-agent simulation models the communications between ML-MiC stakeholders and their internal operations. Second, design of experiments (DOE) reveals the main and interaction effects between logistics and construction decisions that significantly affect KPMs, such as the project duration, total costs, and carbon emissions. Third, metamodeling finds the near-optimum logistics and construction decisions (e.g., trucks' number, their dispatching time, ship capacity, inventory, resource planning) that enhance KPMs. The developed approach is applied to a real case study. The DOE analysis indicates that some logistics decisions significantly influence construction KPMs (e.g., project duration, construction costs, construction emissions) and vice versa, calling for more collaboration between stakeholders. Also, the optimized solutions reduce the project duration, total costs, and emissions by 28%, 50%, and 17%, respectively. This paper contributes by integrating three methods to model ML-MiC and enable its stakeholders to discern the impact of their decisions on multiple KPMs and optimize them toward more sustainable MiC. Given this paper's findings, future researchers are urged to investigate the success factors and barriers to applying ML in MiC. Also, the paper emphasizes the need to develop DSS that achieve a win-win collaboration and enhance communication between ML-MiC stakeholders. • Multimodal logistics (ML) are used to import modular units from overseas factories. • The literature lacks models to assess, analyze and optimize ML-related decisions. • A developed multi-agent model evaluates multiple project performance measures (PMs). • A diagnostic analysis reveals the significant ML decisions that impact each of PMs. • Metamodeling finds the optimum ML decisions to improve PMs of modular construction. [ABSTRACT FROM AUTHOR]