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This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.

This paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.

The findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.

The findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.

The insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.

Increasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.

This paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

The purpose of this paper is to explore the dynamic nature of behaviour change over time and to gain insights into the effectiveness of social marketing efforts at three different intervention points under three different delay time conditions.

A system dynamics simulation modelling approach was used.

The findings showed that the effectiveness of social marketing interventions at different points of intervention and delay times is dependent on complex dynamic system interactions and feedback loops.

As the dynamic simulation model was an abstraction or simplified representation, it was only useful to gain insights into generalised patterns of behaviour over time.

The paper provided practical guidance to social marketers’ intent on gaining insights into “where to do” and “when to do” social marketing rather than “how to do” social marketing.

The paper provided theoretical and practical insights into the temporal nature of behaviour change and the effectiveness of social marketing interventions in influencing behaviour over time.

Rapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP).

This paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making.

This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies.

Fast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.

This paper investigates the potential of dynamic process modelling as an approach for addressing the problem of information systems (ISs) evaluation in the context of organisational change.

A real‐life case study is discussed, showing how dynamic simulation models that incorporate the effects of a proposed IS on existing business processes can help analysts and decision makers arrive at more informed choices for system design and evaluation.

Based on the case findings, we postulate that the design and implementation of organisational systems could be augmented by the development of dynamic process models depicting business operations before and after the introduction of an IS, and the subsequent experimentation with such models to achieve maximum fit between organisational needs and system capabilities.

The study findings imply that dynamic process modelling may be of help in the endeavour of developing ISs that are aligned with the overall business strategy and objectives.

Shows how dynamic simulation models that incorporate the effects of a proposed IS on existing business processes can help analysts and decision makers arrive at more informed choices for system design and evaluation.

Continuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.

First, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.

Numerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.

To reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

This empirical study uncovers emotional sensemaking factors that cause changes in management perceptions about wicked strategic problems under dynamic complexity. These perception changes improve understanding of, and solutions to, the wicked problem.

Senior managers from three large organizations in different sectors participated in gaming simulation workshops. The strategic issues at stake were intractable and divisive. Qualitative methods captured participants' perceptions of the problems and the dynamic complexity that they faced and how they changed.

Flawed management perceptions were revised as sensemaking processes were catalyzed by emotions of shock/surprise that came from experiencing unexpected stakeholder conduct within a simulation. The plausibility of the conduct was strengthened because managers were role-playing stakeholders. The shock/surprise emotion uncoupled attachment to entrenched beliefs, leading to a willingness to revise the flawed perceptions. The changed perceptions created new insights for a solution to the wicked problem.

Practical implications are how management practitioners can improve the tackling of wicked strategic problems through the use of shock and surprise in a gaming simulation.

This research extends theory on the role of emotions in sensemaking under dynamic complexity. The authors uncover how a hierarchy of managers' emotions used in sensemaking explains the catalytic effect of the shock and surprise of unexpected stakeholder conduct on revisions to their perceptions of the outcomes of the dynamic complexity.

This paper focuses on a conceptual methodology for an integrated simulation model dubbed as dynamic simulation modelling system (DSMS) for proactive and optimal decision making within a project management framework. Due to the uncertainties in project environment, the technical and operational functionality of a facility needs to be assessed during development and operation phases of the project. The simulation model is used for optimising the investment decisions vis‐à‐vis evaluation of functionalities on project facilities in early stages of the project. Project life cycle objective functions (LCOFs) are employed as a set of decision criteria throughout the project’s life. The discussion is being extended on the need for setting up an integrated and user‐friendly model to encompass the processes in the entire life cycle of the project. Details of the system are described and a hypothetical case study is used to demonstrate its capabilities. Possible extensions are then outlined. The C++ programming language in association with the object‐oriented database management system is used to achieve the aforementioned objectives.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.