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This study aims to provide data on the optimal staff, materials, space and time resources required to operate a regional hub reception center, a “short-term facility with the goal to process and transport displaced survivors (evacuees) to temporary or permanent shelters following a catastrophic incident” (Bonabeau, 2002). The facility will process approximately 20,000 evacuees over its entire seven-day duration following a disaster to assist in community resilience.

The study was performed using a model created using the computer simulation software, AnyLogic. The software allowed for simulations to be performed on each of the three criteria: minimizing the space needed to run the hub, minimizing individual throughput time and minimizing total operation time.

The results of the study demonstrated that the goals set forth by the Illinois-Indiana-Wisconsin Regional Catastrophic Planning Team could be improved upon and that the largest contributing factor to optimizing the regional hub reception center (RHRC) is finding the optimal number of total staff members to operate the facility.

The value of the study lies in creating a life-saving environment for evacuees who could otherwise not evacuate themselves. The assistance provided by the RHRC gives displaced survivors a safe and organized method for evacuating a large city after a disaster. The study shows how computer models can help improve resilience in an urban area by planning the most efficient methods for evacuating it should it be necessary.

The purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the task priorities in the software development and investigates the possible strategies to manage them effectively.

In this paper, a software development supply chain has been simulated. This includes modeling of the various sizes of software requirement, different priorities, variations in development times, quality defects, etc. The model assumes a fixed set of resources of various skills. The model is studied for the bottlenecks, throughput, work in progress (WIP), etc. under various work preemption scenarios.

The results indicate that job priorities impact the bottleneck formulation, throughput and WIP of the software development. The work interruption policies to accommodate priority jobs adversely impact the throughput. Selective introduction of interruptions by leaving the bottlenecks from interruptions helps balancing the throughput and priorities.

The impact of the learning curve and knowledge acquisition time needed by the resources to restart the interrupted work has not been considered in this paper, which can be a future area of research.

The paper helps the practicing managers evaluate the dynamics of the bottlenecks with various task management approaches and comprehend the possible tradeoffs between priority and throughout.

The paper looks at software development from a perspective of workflow dynamics. This is a pioneer effort, as it utilizes simulation and modeling approach in understanding the software supply chains better.

Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.

This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.

The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.

This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.

This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.

This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.

This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

Higher education institutions are undergoing a change in their teaching–learning practices, with the core goal of giving students the necessary skills and competencies to succeed in a complex and uncertain society. This study aims to evaluate the effectiveness of business simulation as a pedagogical strategy for teaching 21st-century competencies to undergraduate students. The study looks at students’ self-perception on how business simulation impacts future skills such as entrepreneurship, employability and sustainability.

The research incorporates a one-week workshop for undergraduate business students using AnyLogic business simulation. For this study, a 24-item skills-based survey was used as the instrument for eliciting input about students’ self-perceptions. To measure the impact of business simulation on overall student learning, a theoretical framework was developed and tested using SmartPLS version 4 for construct reliability, validity and hypotheses testing.

Based on the students’ feedback, the finding shows that most of the 24 soft skills were facilitated by the business simulation used. The simulation significantly affects the development of entrepreneurial and employable skills. On the contrary, it has little effect on enhancing sustainability skills. In addition, the study suggests that factors like gender and expertise had little overall impact on the results.

The most apparent practical implication of this study is that business schools should focus more on skill development by stressing on experiential teaching methods like business simulation to help students build various skills and become more prepared for the actual world of business.

The research presents fresh empirical data that add to the continuing discussion on active learning in business education and assist educators in avoiding some potential drawbacks of these innovative teaching techniques. With the right direction and criticism throughout the simulation, this learning experience has shown to be useful for everyone involved.

This research aims to develop a new generic framework for estimating different maintenance costs (preventive, corrective and conditional based) and its distribution to original equipment manufacturer (OEM), customer and supply chain due to no fault found (NFF) events. The study extend the domain of NFF to military aircraft maintenance, repair and overhaul (MRO) by including broader range of cost drivers than are normally found in maintenance NFF literature.

The research applies the soft system methodology involving 80 field surveys and five in depth semi-structured interviews with practicing experts having background in military aircraft NFF MRO. For impact analysis, authors have used an agent-based model to represent and prioritize the critical NFF cost drivers during aircraft MRO based on the cost inputs of 21 technology transfer cases.

The paper provides imperial insights about how NFF cost drivers affect the OEM, customer and supply chain. It suggests that NFF cost need to be part of the commercial MRO contract, depending on its frequency pattern in different types of maintenances.

The context of the current research is military aircrafts industry and may lack generalizability to commercial aircrafts. Therefore, researchers are encouraged to test the proposed propositions further.

The developed framework will provide invaluable help in key financial decision-making during signing of MRO contract in technology transfer cases.

This paper proposed a new prediction model for NFF cost estimation across its shareholders and current status of NFF in military aircraft NFF MRO in India.

A system dynamics (SD)-based methodology is described for analysing the impact of lean manufacturing strategies on a company's business performance, using business model canvas (BMC) perspective.

A case study approach is used to describe the methodology which consists of conceptualising a SD model on the basis of BMC. The base SD model is elaborated to include variables and concepts that consider the effects of lean manufacturing metrics on business performance. In the modelling experimentation, the lean manufacturing metrics are made to take on likely values one would expect if certain lean practices are initiated or improved. The experimental results provide one with the likely impact on business performance, if one were to improve lean manufacturing practices.

The simulation results for the case study show that lean improvements, on the short-run, have a significant impact on business performance, but in the long run, the impact is only marginal.

The described methodology provides one with a structured format for investigating the impact of lean practices on business performance. Although the developed SD model was built with generality in mind, it remains to be reproduced in other settings to test its replicability.

The methodology enables an organisation target which lean improvements to initiate based on their strategic impact on the business.

Limited studies exist where SD and business models are combined to test the strategic impact of lean manufacturing.

The purpose of this paper is to provide a framework for analysing and modelling detailed workflow of image‐guided interventions to facilitate simulation and the re‐engineering process for the development of new procedures in multi‐modal imaging environments.

The methodology presented includes a literature review on workflow simulation in surgery, focussing on radiology environments, an assessment of simulation tools, a data gathering and management framework and research on methods for conceptual modelling of the processes.

The literature review reveals that few authors attempted to analyse the phases within image‐guided interventions, and those that did, only did so partially. The framework developed for this work intends to fill the gap found in the survey. It allows the maintenance and management of large amounts of data, one of the most critical factors when modelling detailed workflow. In addition, selecting the appropriate simulation software plays an important role, saving time in later stages of the project.

The framework presented for endovascular interventions can be extended to other types of image‐guided interventions. Moreover, modelling the workflow processes in a modular way facilitates the re‐engineering process when integrating different imaging modalities during the same procedure.

Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism.

The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments.

It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects.

The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.

Pallets ensure efficient processes in logistics and are exchanged between the different actors, while passing through various supply chains several times. In common practice, the exchange is often not directly carried out on site, e.g. due to a lack of time, so that additional trips and new pallet purchases become necessary. To reduce these negative effects, a digital cross-actor platform is designed, and its potential is investigated.

The authors developed an agent-based simulation model with mathematical optimization. Using experience from practitioners, as well as real-world datasets which were analyzed, the authors ensure a realistic model of the pallet exchange system in Germany.

The authors demonstrated that, with the help of this platform concept, transport routes can be shortened, debts and receivables can partly be equaled out through balancing, and the quantity of pallets in the overall system can be reduced.

The results are not directly transferable to pallet exchange systems in other countries without considering their general settings.

Digital networking increases the efficiency of the existing pallet exchange system. Even small collaborations prove to be reasonable.

The authors developed new mechanisms for a digital pallet exchange platform, which takes on the role of a central planning instance, in addition to recording pallet receivables and debts. It enables the planning of the commodity flow of empty pallets, which are transported by the forwarders on regular routes, and distributed between the platform participants.

The purpose of this paper is to formulate and simulate the model for vehicle routing problem (VRP) on a practical application in logistics distribution.

Based on the real data of a distribution center in Utica, Michigan, USA, the design of VRP is modeled as a multi‐objective optimization problem which considers three objectives. The non‐dominated sorting genetic algorithm II (NSGA‐II) is adopted to solve this multi‐objective problem. On the other hand, the VRP model is simulated and an object‐oriented idea is employed to analyze the classes, functions, and attributes of all involved objects on VRP. A modularized objectification model is established on AnyLogic software, which can simulate the practical distribution process by changing parameters dynamically and randomly. The simulation model automatically controls vehicles motion by programs, and has strong expansibility. Meanwhile, the model credibility is strengthened by introducing random traffic flow to simulate practical traffic conditions.

The computational results show that the NSGA‐II algorithm is effective in solving this practical problem. Moreover, the simulation results suggest that by analyzing and controlling specific key factors of VRP, the distribution center can get useful information for vehicle scheduling and routing.

Multi‐objective problems are seldom considered on VRPs, yet they are of great practical value in logistics distribution. This paper is mainly focused on multi‐objective VRP which is derived from a practical distribution center. The NSGA‐II algorithm is applied in this problem and the AnyLogic software is employed as the simulation tool. In addition, this paper deals with several key factors of VRP in order to control and simulate the distribution process. The computational and simulation results regarding VRPs constitute the main contribution of our paper.