Ferdi Joko Giyanto, Dyah Lestari Widaningrum

Simulation-Based Optimization of Resource Allocation in Seasonal Recreational Facilities Using Discrete Event Simulation and Machine Learning

Introduction

Simulation-based optimization of resource allocation in seasonal recreational facilities using discrete event simulation and machine learning. Optimize seasonal recreational facilities using DES & ML. Enhance resource allocation, resolve bottlenecks, and improve visitor experience with dynamic scheduling and spatial redesign.

Abstract

The study proposes a simulation-based optimization framework to surmount recreational facility operational inefficiencies via spatial design, guest flow, and staff allocation. Adopting Discrete Event Simulation (DES) and Machine Learning (ML), the research optimizes capacity planning and resource allocation in the face of dynamic seasonal demands. A year's worth of operations data was utilized for statistical distribution modeling of visitor interarrival times in RStudio, categorized into low, regular, and high seasons. The simulation model, developed in AnyLogic, uncovered service bottlenecks—particularly at ticketing counters and photo points. Validation results indicated close alignment with real-world operational metrics, ensuring model validity. Actionable suggestions are provided in terms of dynamic employee scheduling and spatial reconfiguration for improved efficiency and visitor experience. By integrating DES and ML, the study contributes to sustainable operations and provides a transferable method for the optimization of service systems in weather-dependent recreational environments.

Review

The submitted work, "Simulation-Based Optimization of Resource Allocation in Seasonal Recreational Facilities Using Discrete Event Simulation and Machine Learning," presents a highly relevant and timely approach to a pervasive challenge in the service industry. The study effectively addresses operational inefficiencies stemming from dynamic seasonal demands in recreational facilities, a context often overlooked by generic optimization models. The proposed framework, which intelligently integrates Discrete Event Simulation (DES) with Machine Learning (ML), is particularly commendable for its innovative methodological synergy, promising a robust and adaptive solution for complex operational environments. This interdisciplinary approach positions the research as a significant contribution to both operations management and simulation science. The methodology described appears sound and well-structured. The utilization of a full year's operational data for statistical distribution modeling of visitor interarrival times, segmented by seasonal variations (low, regular, high), demonstrates a rigorous approach to capturing real-world dynamics. The choice of RStudio for data analysis and AnyLogic for simulation model development are standard and appropriate tools, reinforcing the credibility of the technical execution. The identification of specific service bottlenecks, such as ticketing counters and photo points, is a crucial finding that directly supports the practical applicability of the model. Furthermore, the abstract explicitly mentions validation results indicating close alignment with real-world metrics, which is essential for ensuring the reliability and trustworthiness of the simulation outputs and subsequent recommendations. The study's actionable suggestions, particularly regarding dynamic employee scheduling and spatial reconfiguration, offer clear pathways for immediate operational improvements and enhanced visitor experience. The emphasis on contributing to sustainable operations and providing a transferable method for weather-dependent recreational environments underscores the broader impact and generalizability of this research. This transferability is a key strength, suggesting that the developed framework could be adapted to a wide array of service systems facing similar seasonal fluctuations. While the abstract is concise, the work demonstrates strong potential for practical implementation and scholarly impact. I look forward to reviewing the full paper to understand the specifics of the ML integration, the optimization algorithms employed, and the detailed validation process, which will undoubtedly further solidify its contributions.

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