Long-term Operating Costs Research Articles

Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency

Adopting a closed-loop supply chain enhances spare part provisioning through repair, remanufacturing, and recycling. However, poor maintenance of components can have severe consequences. Unlike traditional opportunistic maintenance methods that assume regular inspections or precise degradation monitoring, we propose a model that leverages historical repair data to replace worn components preventively. It considers the real-world workflow where parts are often restored only to a functional level. We study maintenance strategies for repeatedly repaired multi-component systems by applying preventive operations only during corrective repairs. Our model considers component ages, failure time distributions, and structural and economic dependencies, favoring collective over individual replacements for cost efficiency. Stochastic dependencies are mapped using Nataf transformation for component subsets, and a genetic algorithm identifies optimal maintenance strategies to reduce long-term operational costs by balancing maintenance against potential failure penalties. We demonstrate the effectiveness of our approach with a case study on MRI power supply machines, showing that preventive actions can cut early life failures by up to 50% and extend useful life by over a year. Sensitivity analysis reveals that logistic costs, interest rates, and planning horizons influence decisions. Opportunistic maintenance can reduce logistic costs and increase the lifetime of spare parts after repair. Integrating stochastic dependency is computationally efficient for industrial systems and can help predict failures more accurately.

مقارن تحليلة للتصميم الأمثل مقابل التصميم المناسب للمنشآت المستدامة

The growing urgency for sustainable construction practices has necessitated a reevaluation of traditional design approaches in favor of more environmentally responsible methodologies. This paper presents a comparative analysis between optimal and adequate structural design within the context of sustainable construction. Focusing on two case studies—a cutting-edge, sustainably designed commercial building (the Bullitt Center) and a conventional mid-rise residential building in Chicago—the research explores how different design philosophies impact material efficiency, environmental footprint, economic performance, and occupant satisfaction.Through detailed life cycle assessments (LCA) and life cycle energy analyses (LCEA), the study quantifies the advantages of optimal design, which integrates advanced materials, renewable energy systems, and water conservation technologies. The findings demonstrate that while optimal design requires a higher initial investment, it significantly reduces long-term operational costs and environmental impact, achieving a net-zero energy status and greatly improving occupant well-being. In contrast, the conventional building, which adheres to standard design practices, exhibits higher embodied energy, greater environmental degradation, and lower occupant satisfaction.The paper concludes that optimal structural design is not only more sustainable but also economically viable over the building's life span. It emphasizes the importance of integrating sustainability from the earliest stages of design to achieve meaningful environmental and economic benefits. The study calls for the adoption of policy incentives and advanced modeling tools to facilitate the widespread implementation of optimal design principles in the construction industry. Keywords: Sustainable Construction, Optimal Structural Design, Adequate Design, Building Performance, Embodied Energy, Environmental Impact, Economic Viability, Material Efficiency

Determining the Location of Solar Power Plant in Indonesia Using Fuzzy-AHP TOPSIS

Solar is a promising renewable energy source for Indonesia's increasing electricity demand, which grows at the rate of 3.3% annually. However, high investment costs and unclear policies hinder Solar Power Plant (SPP) development. Considering the potential for growth in energy demand and the low long-term operational costs, it is imperative to foster SPP expansion in Indonesia. This study aims to identify location criteria and potential SPP development sites in Indonesia. We employ Multi-Criteria Decision-Making (MCDM) combining Fuzzy-AHP and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies to assign criteria weights and prioritize alternative SPP locations. Results show that, from all respondents using the geometric mean in the Fuzzy Analytic Hierarchy Process (AHP), it is found that the Economic criteria give the highest weight at 31%, and subcriteria of Land Availability, Peak Sun Hours, Geographic Location, Distance from Transportation Networks, Construction Costs, and Government Regulations contribute significantly. The ranking of alternative solutions indicates that South Sumatra Province holds the highest priority for SPP development with a 0.572 score, closely followed by North Sumatra at 0.571

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

ANALISIS EFISIENSI BIAYA DALAM PENYEDIAAN AIR LAUT UNTUK OCEANARIUM KOTA: STUDI KASUS BXSea BINTARO

This research focuses on assessing how to procure seawater for the BXSea Bintaro Oceanarium, which is located far from natural seawater sources. The two main options analyzed were the purchase of seawater directly from the coast and the creation of artificial seawater. The research method used in this study was conducted through a qualitative, case study approach by combining literature study, field observation, and the researcher's experience as an oceanarium designer, to gain an in-depth understanding of the technical, economic, and environmental aspects associated with both ways of seawater procurement. The results show that the use of native seawater is more economical and environmentally friendly than artificial seawater. Analysis of the initial costs, long-term operational costs, and logistical and environmental implications of both methods showed the superiority of direct seawater procurement. This method is not only efficient in reducing logistical complexity and initial investment, but also has minimal environmental impact. The conclusions of this study confirm that natural seawater procurement is a cheaper option than the use of artificial seawater. Artificial seawater is significantly up to 5.58 times higher than natural seawater. This study makes an important contribution to the literature of seawater resource management in educational and recreational facilities, and underscores the importance of a sustainable approach in oceanarium development.

Thermal, Electrical, and Economic Performance of a Hybrid Solar-Wind-Geothermal System: Case Study of a Detached House in Hamburg and Sylt, Germany

Germany is undergoing an energy transition. By 2045, fossil fuels will be gradually replaced by clean energy. An alternative option is to use geothermal, solar and wind energy to generate heat or electricity. Currently, an economic model that considers these three energy sources and incorporates the design and installation of the energy system as well as operational costing focusing on the local market is lacking. In this study, we present a concept for a hybrid energy system combining solar, wind and geothermal energy for small, detached houses. We also develop a simplified economic model for the German market and local energy subsidy policies. The model was applied to two different cities in northern Germany, calculating the installation and long-term operating costs of different energy systems and combinations over a period of 100 years, including the consideration of the lifespan of variable equipment. The calculations show that for this small hybrid energy system the initial installation costs can vary from EUR 20,344 to EUR 70,186 depending on different portfolios. Long-term operating costs come mainly from electricity purchased from the grid to compensate for periods of low solar or wind production. In addition, the study included a calculation of the payback period for retrofitting a natural gas heating system. Results show that combining a photovoltaic system with a ground source heat pump, especially in the form of a near-surface heat exchanger, yields a shorter payback period (5 to 10 years). However, the incorporation of on-roof wind turbines into the hybrid energy system may significantly prolong the payback period and is therefore not recommended for use in low wind speed areas.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

SISTEM APLIKASI POIN OF SALE (POS) UNTUK PENJUALAN TOKO NAILA COOKIES BERBASIS JAVA

The Java-based Point of Sale (POS) application system was created to increase operational efficiency and transaction accuracy at Naila Cookies Shop. This system automates sales processes, inventory management, and financial reporting, aiming to speed up the checkout process, minimize errors, and provide real-time sales data. Utilizing Java as the main programming language with an object-oriented approach ensures scalability and ease of maintenance. Additionally, the system has a user-friendly interface to facilitate fast and efficient transactions for store staff. Testing shows important improvements in transaction speed and accuracy, potentially reducing long-term operational costs. These findings show the positive impact of the appropriate use of information technology on retail store operations.

Design and Techno-economic Analysis of an Improved Multipurpose Cooker Stove

In response to the growing concerns surrounding the adverse health and environmental impacts of inefficient and hazardous traditional cooking fuels and stoves, this research aims to design and assess the performance of an improved multipurpose cooker stove. The study focuses on Zambia, a country deeply affected by air pollution and deforestation due to charcoal and firewood production using inefficient cooking stoves called Mbaula. The primary objective is to create an economically viable and environmentally friendly cooking stove that addresses the sustainability challenges of traditional cooking practices. The research is guided by key objectives. The study outlines the design process of the improved multipurpose cooker stove, emphasizing the incorporation of sustainable design principles. The improved multipurpose cooker stove demonstrated a thermal efficiency of 87.49%, significantly higher than the 11.88% thermal efficiency of traditional stoves. This improvement in efficiency is crucial for reducing fuel consumption and maximizing heat transfer during cooking processes. In addition, the research findings showed that the improved multipurpose cooker stove consumed 0.3643 kg/L less fuel compared to traditional stoves. This fuel savings not only reduces the cost of raw materials but also contributes to environmental sustainability by lowering deforestation rates and air pollution. Despite the initial investment cost of the improved cooker stove, the long-term operation costs are lower due to reduced fuel consumption and maintenance expenses. The study's findings and recommendations hold promise for addressing critical challenges related to clean cooking solutions, environmental preservation, and the well-being of households in Zambia and similar contexts.

Composing MPC With LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and is capable of improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amortized</i> running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the need to reduce the amortized cost of MPC in repetitive industrial robotic applications, where long-term operational cost is important and safety is critical. Examples of such applications include factory robotic arm manipulation and fixed-route quadcopter payload transport. Unlike explicit MPC or function approximation, our approach does not require any pre-computation or pre-training. Rather, it attains task proficiency over time by learning a surrogate neural network on the spot and by gradually replacing the costly MPC with the more efficient surrogate model so long as safety permits. Consequently, the proposed scheme incurs a learning cost during the initial phase of the deployment but usually becomes more adept on the task afterwards, leading to amortized efficiency.

Long-term cost planning of data-driven wind-storage hybrid systems

—To address the long-term cost planning problem accurately, typically a significant number of formulas are required. However, this increases computational complexity and limits the generalizability of the approach to practical problems. To overcome these challenges, this study adopts a data-driven approach that considers uncertainties to evaluate the long-term cost planning problem accurately for wind power generation with hybrid energy storage. A method for predicting wind power output is proposed using temporal convolutional networks to handle long-term uncertainties. Additionally, an instantaneous phase-synchronous wind power output feature extraction method based on Hilbert transform theory is introduced to capture time-series fluctuation characteristics and address short-term uncertainties. Furthermore, the long-term operational costs and planning of high-percentage new energy wind farms with hybrid energy storage are discussed by combining the wind power output characteristics with the operating characteristics and lifespan of hybrid energy storage devices. The experimental results demonstrate the significant advantages of the deep learning probabilistic prediction model combined with wind power feature extraction. The algorithm validates the effectiveness of considering multiple uncertainties in the planning of microgrids with hybrid energy storage, particularly in considering the uncertainty of the decreasing trend in the investment cost of battery energy storage.

Deep reinforcement learning for demand fulfillment in online retail

A distinctive feature of online retail is the flexibility to ship items to customers from different distribution centers (DCs). This creates interdependence between DCs and poses new challenges in demand fulfillment to decide from which DC to satisfy each customer demand. This paper addresses a demand fulfillment problem in a multi-DC online retail environment where demand and replenishment lead time are stochastic. The objective of the problem is to minimize the long-term operational costs by determining the source DC for each customer demand. We formulate the problem as a semi-Markov decision process and develop a deep reinforcement learning (DRL) algorithm to solve the problem. To evaluate the performance of the DRL algorithm, we compare it with a set of heuristic rules and exact solutions obtained by linear programming. Numerical results show that the DRL policy performs equally well with the most competitive heuristic on complete pooling DC networks and outperforms all the heuristics on partial pooling DC networks. Additionally, by analyzing the transshipment ratio of the best-observed policies, we provide managerial insights regarding the circumstances in which transshipment is more favorable.

Studying the small extracellular vesicle capture efficiency of magnetic beads coated with tannic acid.

The isolation of small extracellular vesicles (sEVs), including those secreted by pathological cells, with high efficiency and purity is highly demanded for research studies and practical applications. Conventional sEV isolation methods suffer from low yield, presence of contaminants, long-term operation and high costs. Bead-assisted platforms are considered to be effective for trapping sEVs with high recovery yield and sufficient purity for further molecular profiling. In this study, magnetically responsive beads made of calcium carbonate (CaCO3) particles impregnated with iron oxide (Fe3O4) nanoparticles are fabricated using a freezing-induced loading (FIL) method. The developed magnetic beads demonstrate sufficient magnetization and can be collected by a permanent magnet, ensuring their rapid and gentle capture from an aqueous solution. The tannic acid on the surface of magnetic beads is formed by a layer-by-layer (LbL) method and is used to induce coupling of sEVs with the surface of magnetic beads. These tannic acid coated magnetic beads (TAMB) were applied to capture sEVs derived from MCF7 and HCT116 cell lines. Quantitative data derived from nanoparticle tracking analysis (NTA) and BCA methods revealed the capture efficiency and recovery yield of about 60%. High-resolution transmission electron microscopy (HRTEM) imaging of sEVs on the surface of TAMBs indicated their structural integrity. Compared with the size exclusion chromatography (SEC) method, the proposed approach demonstrated comparable efficiency in terms of recovery yield and purity, while offering a relatively short operation time. These results highlight the high potential of the TAMB approach for the enrichment of sEVs from biological fluids, such as cell culture media.

THE DEVELOPMENT OF MOBILE APPLICATION FOR DENGUE SITE INSPECTION IN JOHOR BAHRU

This study focuses on developing a user-friendly mobile application for dengue site inspection, aiming to address the persistent threat of vector-borne diseases in Malaysia. The current practice of dengue site inspection is still using paper-based method, which is inefficient, prompting the need for a mobile app solution. Existing methods for identifying and destroying breeding grounds, known as Destruction Action the Breeding Place (Pemusnahan Tempat Pembiakan, PTP), face constraints due to limited technology and information facilities. This leads to incomplete eradication and the need for repeated operations in the same areas, resulting increased in operational costs. To address these issues, a simple and user-friendly mobile application is designed to enhance field data entry, enable location tracking, and provide easy access to operational data from the inspection site. To reduce the effort on site record-keeping, the application is developed on the cost-effective open-source platform - QGIS, and QField. The aim is to improve the accuracy of PTP activities based on mobile application data and increase productivity through direct data entry in the field. The objectives of this study are to identify the requirements of a mobile application for dengue site inspection, develop a mobile application with location tracking enablement, and validate the results and effectiveness of the developed mobile application. The implemented apps were tested on the field by the health officer and 98% of them agree that the application does help the process of dengue site inspection and has significantly improved their workflow. The mobile application provided them with a user-friendly interface and streamlined data entry process, allowing them to easily capture and update information during dengue site inspections. The integration of location tracking capabilities also enabled them to accurately identify and record the precise coordinates of potential breeding sites. Overall, the positive feedback from the health officers demonstrates the effectiveness and usefulness of the developed application in enhancing the efficiency and effectiveness of dengue site inspections. The application aligns with the Sustainable Development Goal of Good Health and Well-Being and contributes to the digitalization process of the Fourth Industrial Revolution (Industry 4.0), aiming at reducing long-term operating costs.

PENGARUH LIABILITAS JANGKA PANJANG DAN OPERASIONAL COST TERHADAP NET PROFIT PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BEI

The purpose of this research is to understand the influence of long-term liabilities and operational costs on net profit in manufacturing companies listed on the Indonesian Stock Exchange (BEI) from 2017 to 2021. This research is a quantitative descriptive study, which involves numerical data. The sample size for this study consists of 11 companies out of a total population of 17, resulting in a total of 55 financial reports as samples. In practice, this research uses SPSS 21 software, and the data analysis technique employed is multiple linear regression analysis. The t-test results show that long-term liabilities significantly affect net profit with a significance level of 0.041. Similarly, operational costs significantly impact net profit with a significance level of 0.000. The results of the F-test indicate that long-term liabilities and operational costs collectively influence net profit significantly at a significance level of 0.000. The coefficient of determination test results show that 91% of the variability in net profit can be explained by long-term liabilities and operational costs, while the remaining 9% is explained by other variables.

Maintenance policy of degradation components based on the two-phase Wiener process

This paper proposes a condition-based maintenance policy for the two-phase Wiener degradation process components. The main contribution of this article is to provide the time distribution of degradation failures for the two-phase Wiener process degradation component, as well as the modeling and solving methods for two-phase maintenance. The two-phase maintenance policy includes two-phase inspection and preventive replacement maintenance operations. The established optimization maintenance policy model aims to minimize long-term operation costs. The specific cost calculation equation and the solution method of the maintenance model are given. The feasibility of the maintenance policy model is verified using the two-phase degradation data of the Liquid Coupling Devices. The Particle swarm optimization algorithm can stably solve the described problem, and the results show that the two-phase maintenance policy can be more economical and improve components availability. After that, we also analyzed the impact of the cost parameters on the maintenance policy through sensitivity analysis.

Integration of Solar Energy Technology in Improving Energy Sustainability and Efficiency in the Fisheries Sector (Case Study of Rokan Hilir Regency, Riau Province)

Improving sustainability and energy efficiency in the fisheries sector has emerged as an essential objective in addressing current environmental and economic concerns. One promising solution is the integration of solar energy technology in the fisheries sector. The purpose of the study was to analyse the potential and benefits of incorporating solar energy technology in improving sustainability and energy efficiency in the fisheries sector. This research included quantitative and qualitative analysis of the usage of solar energy technology in numerous aspects of the fisheries sector, such as fishing vessels, aquaculture, processing facilities, and related infrastructure. Data was collected from different locations with various characteristics during field research. The study found that integrating solar energy technology can lessen reliance on fossil fuels while also saving long-term operational costs. One of the potentials that can be used to encourage the growth of Rokan Hilir Regency, particularly in the field of fisheries, is the incorporation of solar energy technology. The Rokan Hilir Regency has the potential for solar energy conversion into electrical energy that can be used for a variety of purposes. The average peak power hour (Sun Peak Hour) for a year in Bangko District is 6.84 hours, with a minimum value of 6.23 hours in December and a maximum of 7.34 hours in August. The usage of integrated solar energy technology is utilised to support the activities of the fisheries sector, including catching, cultivating, or hatching, as well as fishing product processing. Obviously, the components used in each field must be adjusted to the level of electrical energy required. Several components that are used on fishing boats to facilitate fishing activities include spotlights, LED lights, fish cool boxes, and electric starters. Lighting lamps and DC water pumps can be used in the aquaculture industry. Solar energy is also used in the Fisheries Processing sector for LED lights and electric motors in shredded fish processing machines.

Progress in Isolation and Molecular Profiling of Small Extracellular Vesicles via Bead-Assisted Platforms.

Tremendous interest in research of small extracellular vesicles (sEVs) is driven by the participation of vesicles in a number of biological processes in the human body. Being released by almost all cells of the body, sEVs present in complex bodily fluids form the so-called intercellular communication network. The isolation and profiling of individual fractions of sEVs secreted by pathological cells are significant in revealing their physiological functions and clinical importance. Traditional methods for isolation and purification of sEVs from bodily fluids are facing a number of challenges, such as low yield, presence of contaminants, long-term operation and high costs, which restrict their routine practical applications. Methods providing a high yield of sEVs with a low content of impurities are actively developing. Bead-assisted platforms are very effective for trapping sEVs with high recovery yield and sufficient purity for further molecular profiling. Here, we review recent advances in the enrichment of sEVs via bead-assisted platforms emphasizing the type of binding sEVs to the bead surface, sort of capture and target ligands and isolation performance. Further, we discuss integration-based technologies for the capture and detection of sEVs as well as future research directions in this field.

Upscaling bio-based construction: challenges and opportunities

ABSTRACT Construction projects using emerging bio-based materials have been realized over the past ten to fifteen years within Europe. Bio-based buildings utilize properties of natural materials to regulate internal environments, particularly fluctuations in temperature and relative humidity. Despite individual exemplar projects demonstrating functional performance and long-term operational cost savings, there hasn’t been a proliferation of commercial or domestic bio-based projects. With a growing shift towards circular economy construction, bio-based buildings could be readily adopted to meet this development. This study evaluates barriers faced by bio-based materials, making the upscaling of production and a breakthrough into mainstream construction challenging. Evaluation was achieved through senior professionals with experience in bio-based construction participating in semi-structured interviews based on core categories of finance, knowledge, and policy. Challenges include the upscaling of production by manufacturers of emerging materials, inconsistencies in life cycle assessment, material certification and accreditation, vested interests in the construction industry, and concerns regarding initial costs, availability, and knowledge of products. Potential solutions for upscaling bio-based construction are identified and include increased case studies, positive legislation, regional economic regeneration, the wellness agenda, long-term economic sustainability, and engagement with established construction companies. This insight has informed the procurement process, material evaluation, and adoption of policy.

A multi-period emergency medical service location problem based on Wasserstein-metric approach using generalised benders decomposition method

This paper considers a multi-period location and sizing problem for an emergency medical service (EMS) system based on a distributionally robust optimisation (DRO) chance-constrained programming approach. The dynamic uncertain emergency medical requests are described in the ambiguity set, which is constructed based on Wasserstein-metric. The model of this problem focuses on minimising long-term operation costs. The chance constraints ensure the reliability of EMS system for the entire geographic areas. A reformulation of chance constraints is provided in Mixed Integer Linear Program form. For problem solution, a generalised Benders decomposition (GBD) implementation is proposed. A numerical simulation is conducted to illustrate the performance of two solution approaches in terms of computational convergence speed and optimality of the problem.