Optimal Planning Research Articles

Mapping Tomorrow’s Cities: GeoAI Strategies for Sustainable Urban Planning and Land Use Optimization

Mapping Tomorrow’s Cities: GeoAI Strategies for Sustainable Urban Planning and Land Use Optimization

자동차 서비스 복합단지의 최적 입지 연구

With the advent of the 4th Industrial Revolution, automobiles have changed in a consumer-led way, the aftermarket market for automobiles has grown rapidly, and regulations have been tightened worldwide and nationally to protect people from carbon emissions, the main culprit of environmental destruction. Therefore, this study attempted to derive and present the optimal plan in terms of development, operation, and use as a process for analyzing the optimal location of automobile service complexes under the Automobile Management Act. For this, it is necessary to select the optimal location, and in-depth analysis and results were derived based on three key aspects of development, operation, and use from various aspects. In the analysis, it is necessary to complete the questionnaire by reflecting expert opinions and proceed with the questionnaire results to present specific strategies for stable development, efficient operation plans, and rational management environment based on the AHP analysis results. According to the analysis, the relative importance of automobile exhibition space, trial space size, connection to automobile maintenance and parts makers, access to sufficient population density hinterland, practical experience and ability, and financial soundness were shown.

Enhancing affordability and profit in a non-cooperative, coordinated, hypothetical pediatric vaccine market via sequential optimization.

This study considers a hypothetical global pediatric vaccine market where multiple coordinating entities make optimal procurement decisions on behalf of countries with different purchasing power. Each entity aims to improve affordability for its countries while maintaining a profitable market for vaccine producers. This study analyzes the effect of several factors on affordability and profitability, including the number of non-cooperative coordinating entities making procuring decisions, the number of market segments in which countries are grouped for tiered pricing purposes, how producers recover fixed production costs, and the procuring order of the coordinating entities. The study relies on a framework where entities negotiate sequentially with vaccine producers using a three-stage optimization process that solves a MIP and two LP problems to determine the optimal procurement plans and prices per dose that maximize savings for the entities' countries and profit for the vaccine producers. The study's results challenge current vaccine market dynamics and contribute novel alternative strategies to orchestrate the interaction of buyers, producers, and coordinating entities for enhancing affordability in a non-cooperative market. Key results show that the order in which the coordinating entities negotiate with vaccine producers and how the latter recuperate their fixed cost investments can significantly affect profitability and affordability. Furthermore, low-income countries can meet their demands more affordably by procuring vaccines through tiered pricing via entities coordinating many market segments. In contrast, upper-middle and high-income countries increase their affordability by procuring through entities with fewer and more extensive market segments. A procurement order that prioritizes entities based on the descending income level of their countries offers higher opportunities to increase affordability and profit when producers offer volume discounts.

Investigation on construction characteristics and indoor environmental quality of Fangcang shelter hospitals during the COVID-19 pandemic in Shanghai, China

Since the rapid spread of COVID-19 in late 2019, Fangcang shelter hospitals have become crucial components of urban healthcare facilities. However, there remains a lack of systematic research on the performance assessment of Fangcang shelter hospitals. This study evaluated the construction efficiency, facility allocation, and indoor environmental quality (IEQ) of 115 Fangcang shelter hospitals in Shanghai. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, 51,040 social media reviews were processed to evaluate the prevalence of IEQ complaints. The results show that Fangcang shelter hospitals renovated from exhibition halls have the fastest median construction speed (58.2 bed/h), closely followed by office buildings (41.7 bed/h), while containers were the slowest (9.1 bed/h). Notably, there is a power function relationship between the bed capacity and construction speed (R2 = 0.81), indicating a scale effect. As for the indoor environment, toilet comfort issues comprise 31.2 % of the total complaints, followed by complaints about acoustics (22.8 %), hygiene (12.0 %), and thermal (11.2 %). Container-built Fangcang shelter hospitals face significant challenges to the acoustic (40 %) and thermal (15 %) environment, while renovated facilities encounter more problems with toilet facilities and privacy. We also found that the proportion of indoor air quality (IAQ) issues increases as the average bed area decreases across facilities renovated from factories, exhibition halls, and office buildings. These findings offer valuable insights into Fangcang shelter hospitals, which can be considered in future planning and spatial optimization of emergency shelters.

Stochastic Optimization for Long-Term Planning of a Mining Complex with In-Pit Crushing and Conveying Systems

Semi-mobile in-pit crushing and conveying (IPCC) systems can help reduce truck haulage in open-pit mines by bringing the crusher closer to the excavation areas. Optimizing a production schedule with semi-mobile IPCC requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. A mining complex with multiple mines and IPCC systems should be optimized simultaneously to find an optimal schedule for the entire value chain. An integrated stochastic optimization framework is proposed to produce long-term production schedules for mining complexes using multiple semi-mobile IPCC systems. The optimization model has flexibility to select the crusher locations and conveyor routes from anywhere inside the pits. The framework uses simulated orebody realizations to consider multi-element grade uncertainty and manage associated risk. A hybrid metaheuristic solution approach based on simulated annealing and evolutionary algorithms is implemented. The method is demonstrated using an iron ore mining complex.

A generative design-based optimization model for multi-objective construction site layout planning

PurposeGiven the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.Design/methodology/approachA novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.FindingsThe results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.Practical implicationsThe findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.Originality/valueA new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

Long solution times or low solution quality: On trade-offs in choosing a power flow formulation for the Optimal Power Shutoff problem

The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in the related contexts of optimal transmission switching or grid restoration planning. This paper explores the accuracy of the DC OPS formulation and the ability to recover an AC-feasible power flow solution after de-energization decisions are made. We also extend the OPS problem to include variants with the AC, Second-Order-Cone, and Network-Flow power flow equations, and compare them to the DC approximation with respect to solution quality and time. The results highlight that the DC approximation overestimates the amount of load that can be served, leading to poor de-energization decisions. The AC and SOC-based formulations are better, but prohibitively slow to solve for even modestly sized networks thus demonstrating the need for new solution methods with better trade-offs between computational time and solution quality.

Multicriteria optimization of radiation therapy: Towards empowerment and standardization of reverse planning for head and neck squamous cell carcinoma

PurposeThe purpose of this study was to assess if multicriteria optimization could limit interoperator variability in radiation therapy planning and assess if this method could contribute to target volume coverage and sparing of organ at risk for intensity-modulated curative radiation therapy of head and neck cancers. Material and methodsWe performed a retrospective analysis on 20 patients treated for an oropharyngeal or oral cavity squamous cell carcinoma. We carried out a comparative dosimetric study of manual plans produced with Precision® software, compared with the plans proposed using the multicriteria optimization method (RayStation®). We assessed interoperator reproducibility on the first six patients, and dosimetric contribution in sparing organs at risk using the multicriteria optimization method. ResultsMedian age was 69 years, most lesions were oropharyngeal carcinoma (65%), and 35% lesions were stage T3. First, we obtained a high degree of similarity between the four operator measurements for each patient at the level of each organ. Intraclass correlation coefficients were greater than 0.85. Second, we observed a significant dosimetric benefit for contralateral parotid gland, homolateral and contralateral masseter muscles, homolateral and contralateral pterygoid muscles and for the larynx (P<0.05). For the contralateral parotid gland, the mean dose difference between the multicriteria optimization and manual plans was −2.0Gy (P=0.01). Regarding the larynx, the mean dose difference between the two plans was −4.6Gy (P<0.001). ConclusionMulticriteria optimization is a reproducible technique and faster than manual optimization. It allows dosimetric advantages on organs at risk, especially for those not usually taken into consideration in manual dosimetry. This may lead to improved quality of life.

Optimized transportation scheduling for precast concrete components considering heterogeneous vehicle-size matching

Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds from multiple projects arises. Unlike conventional cargo, PC components are characterized by shape heterogeneity, large volume, and strict delivery time limits. Based on three characteristics, a heterogeneous fixed fleet vehicle routing problem (HFFVRP) for PC components is introduced, where heterogeneous vehicles, allocation of PC components to size-matching vehicles, and hybrid time windows are considered. Then a two-stage solution strategy based on the improved ant colony optimization (ACO) and Dijkstra algorithm is designed to obtain optimal vehicle routes under minimum transportation costs. The results indicate that the improved ACO-Dijkstra algorithm outperforms in obtaining optimal transportation plans for heterogeneous vehicles compared with manual decision-making and other heuristic algorithms. Sensitivity analysis denotes that utilizing heterogeneous vehicles contributes to reductions in transportation costs, and vehicle configuration should be adjusted along with demand scales. The proposed model and algorithm extend the theoretical basis of construction industrial applications.

Serum C-reactive protein to albumin ratio as a potential risk indicator of pneumonia caused by Chlamydia psittaci: a multicenter retrospective study.

The aim of the study was to describe psittacosis pneumonia and to assess the predictive value of the C-reactive protein/albumin ratio in psittacosis pneumonia for severity. Data on psittacosis pneumonia cases diagnosed using metagenomic sequencing were collected from three hospitals in Shanghai, China from Oct. 2019 to Oct. 2022. Serum levels of C-reactive protein and albumin were measured and the C-reactive protein to albumin ratio (CAR) was calculated. Spearman's correlation analysis, ordered logistic regression analysis, and receiver operating characteristic curve analysis were conducted to examine the correlation and predictive ability of the three indicators on the severity of the disease. A total of 27 patients with psittacosis pneumonia were enrolled, with an average age of 62 years and 70.4% being male. 44.4% of patients had a clear history of contact with poultry or birds. The predominant symptom was fever (100%). Patients treated in the respiratory intensive care unit (RICU) had a higher likelihood of experiencing wheezing (88.9% versus 33.3%, P=0.013) and chest tightness (88.9% vs. 33.3%, P=0.013) than those in the general ward (Non-RICU). The proportion of patients with pleural effusion was significantly higher in the RICU compared to the Non-RICU (88.9% vs. 38.9%, P=0.019). The RICU group had a significantly higher CAR than the Non-RICU group (9.41 vs. 4.05, P=0.017). This result was accompanied by higher intubation and ventilator support (33.3% vs. 0.0%, P=0.029), higher PCT and CRP levels and lower albumin and PaCO2 levels in the RICU than in the Non-RICU. Logistic regression analysis indicated that CAR (OR 1.49; 95% CI 1.07-2.06, P=0.017) was risk factor for prolonged hospitalization (> 14 days). Elevated serum CAR levels were found to be associated with a greater risk of severe psittacosis pneumonia. Consequently, it may serve as an uncomplicated and useful diagnostic tool for clinicians to promptly and precisely ascertain the severity of psittacosis pneumonia, ultimately aiding them in devising the most optimal therapeutic plan.

Feasibility of the Northern Sea Route: Impact of Sea Ice Thickness Uncertainty on Navigation

With the accelerated melting of the Arctic sea ice, the opening of the Northern Sea Route (NSR) of the Arctic is becoming increasingly accessible. The purpose of this paper was to examine the impact of uncertainty in sea ice thickness (SIT) data on the opening of the NSR and to quantify the extent of this impact, which is essential to the regularized operation of polar shipping. A quantitative assessment framework was proposed to examine the influence of SIT data uncertainty on navigational uncertainty through three aspects: the navigational spatiotemporal windows, the distribution of safe sailing speeds, and the uncertainty in optimal route planning by employing four distinct SIT datasets. Furthermore, the sensitivity of navigational spatiotemporal windows, the distribution of safe sailing speeds, and route planning to variations in SIT were also evaluated. Results show that experiment results based on CS2SMOS exhibit a more aggressive profile, while results based on CPOM are more conservative. The difference in SIT data has a significant impact on the proportion of operations subject to special consideration areas, such as safety speed, sailing time, and distance in NEP. A 0.1 m discrepancy in sea ice thickness data results in an approximate 0.067 change in the proportion of operations within special consideration areas. This discrepancy also leads to an approximate speed change of 0.89 knots, a navigation duration change of approximately 4 days, and a distance change of 152 km within specified limits.

Analyzing the Impact of Economic Performance on the Interplay between Financial Management Strategies and Profitability in the Iraqi Context

Purpose - This study examines how economic performance affects financial management approaches and profitability in Iraq's construction business. This research examines how financial management strategies affect profitability and economic success. Design/Methodology/Approach—Construction project and site managers in Iraq were surveyed online for the study. Given the scope and magnitude of the research, this study's 85.3% response rate is impressive. In-depth statistical analysis was performed using Partial Least Squares Structural Equation Modelling (PLS-SEM), which examined economic performance, financial management, and profitability. Findings - The instance indicates that the lack of organization-wide integrated systems, common across enterprises, contributes to this problem. The major issue is that the organization's information systems strategy never expressly prioritizes KM-enabling schemes. Practical Implications—The study's conclusions have major practical ramifications. Iraqi enterprises should prioritize resource optimization, cost control, and strategic financial planning to improve economic performance. In addition, governments should promote building projects through incentives, subsidies, and tax policies that boost economic productivity and profitability. These policies are available here. Originality/Value—This study adds to the literature by stressing economic performance as a mediator between financial management methods and profitability in Iraq's construction sector. It examines the dynamics of this link in Iraq's building sector. With the new data, firms may improve financial performance by making informed judgments and using efficient financial management procedures.

Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.

Comparison of the Transcranial Approach and Transsphenoidal Approach Based on the Anatomical Location of Origin in Pediatric Craniopharyngiomas

BackgroundVarious clinical classifications of craniopharyngiomas (CRPs) have been proposed to suggest optimal surgical planning. We aimed to evaluate the clinical outcomes of pediatric CRPs and the clinical significance of anatomical classification in relation to the diaphragm sellae. MethodsA retrospective review was conducted on patients under 18 years of age who underwent surgery for CRPs from July 1998 to August 2022. The patients were divided into transcranial approach (TCA), and transsphenoidal approach (TSA) groups, which included microscopic TSA and endoscopic endonasal approach (EEA) groups. EEA has been adopted at our institute since 2011. CRPs were classified by their origin and relationship with the diaphragm sellae. ResultsA total of 132 pediatric CRP patients were included in this study, 117 of whom underwent surgery for primary CRP and 15 for recurrent CRP. Among them, 89 (67.4%) underwent TCA, 9 (6.8%) had microscopic TSA, and 34 (25.8%) had EEA. In subdiaphragmatic CRPs with competent diaphragm sellae, TSA tended to yield better outcomes than did TCA in terms of stalk preservation and ophthalmological outcomes. After the introduction of EEA, the proportion of supradiaphragmatic CRPs treated via the TSA increased from 0% to 50% (p < 0.001). Gross total resection (HR=0.194; 95% CI=0.102–0.367, p < 0.001) and adjuvant therapy (HR=0.208; 95% CI=0.048–0.897, p = 0.035) were found to be positive prognostic factors for long-term tumor control. ConclusionsOver time, with the adoption of EEA at our institute, the impact of anatomical classification on the surgical apprpoach has decreased. Nevertheless, an individualized surgical approach should be employed to improve long-term outcomes and minimize complications for pediatric CRPs.

Boosting optimal symbolic planning: Operator-potential heuristics

Heuristic search guides the exploration of states via heuristic functions h estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both approaches together constitute the state of the art. Yet combinations of the two have so far not been an unqualified success, because (i) h must be applicable to sets of states rather than individual ones, and (ii) the different state partitioning induced by h may be detrimental for BDD size. Many competitive heuristic functions in planning do not qualify for (i), and it has been shown that even extremely informed heuristics can deteriorate search performance due to (ii).Here we show how to achieve (i) for a state-of-the-art family of heuristic functions, namely potential heuristics. These assign a fixed potential value to each state-variable/value pair, ensuring by LP constraints that the sum over these values, for any state, yields an admissible and consistent heuristic function. Our key observation is that we can express potential heuristics through fixed potential values for operators instead, capturing the change of heuristic value induced by each operator. These reformulated heuristics satisfy (i) because we can express the heuristic value change as part of the BDD transition relation in symbolic search steps. We run exhaustive experiments on IPC benchmarks, evaluating several different instantiations of potential heuristics in forward, backward, and bi-directional symbolic search. Our operator-potential heuristics turn out to be highly beneficial, in particular they hardly ever suffer from (ii). Our best configurations soundly beat previous optimal symbolic planning algorithms, bringing them on par with the state of the art in optimal heuristic explicit search planning in overall performance.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

How much hydrogen could we need in New Zealand? Understanding the diverse hydrogen applications and their regional mapping

ABSTRACT Green hydrogen could become a key solution for the decarbonisation of diverse sectors in the energy transition. Beyond supplying the current demand for ammonia, methanol, and steel, new applications like aviation fuels, high-temperature heat and seasonal storage are promising. While initial efforts have been made to understand the future of hydrogen in New Zealand, there has been no comprehensive quantification of hydrogen demands. In this study, using systems analysis, we quantify the potential demand for 20 hydrogen applications in the country, including their spatial allocation to each of the 16 regions. We sort these applications using the so-called Clean Hydrogen Ladder concept. The associated database is open source. The results show that New Zealand's total demand for green hydrogen would be around 2.8 Mt if all technically feasible applications switched to hydrogen. Prioritising the demand for applications in rungs A and B of the Clean Hydrogen Ladder concept, we estimate approximately 0.24 and 0.77 metric tons, respectively. The findings of this research contribute to the development of a hydrogen strategy and serve as inputs for optimal planning of integrated energy pathways for New Zealand.

Proposed Content Marketing Strategy Improvement of Leap-Telkom Digital to Increase Brand Awareness

With the movement of IndiHome as the previous main product of Telkom Indonesia to Telkomsel, Telkom Indonesia is currently struggling with enhancing their digital products performance. Telkom implements its transformation from telecommunication to digital telecommunication state-owned enterprise. With that, Telkom wants to focus on enhancing its digital products. However, in its implementation, Leap faces the effort of increasing its brand awareness. Based on the author’s findings from initial survey, the potential target customers are still not familiar with Leap nor Telkom’s digital products. Moreover, this problem is also supported by the previous performance of Leap’s website from its total users and engagement. The spread of Telkom’s digital products knowledge to internal Telkom employees is also not distributed equally. With this, the author conducts research to improve Leap-Telkom Digital in hopes of improving its brand awareness. The research is using Gap analysis, Brand Building Blocks, Customer-Based Brand Equity (CBBE) pyramid, and internal interview to find the deeper problem of what’s causing low brand awareness of Leap. And then, using theory of Integrated Marketing Communication (IMC), as well as weighting and rating method, the author proposed a Content Marketing strategy to help Leap improving their website traffic and impression performance. With the result of both IMC and weighting rating method, the author proposed content marketing implementation plan for Search Engine Optimization (SEO), Social Media, and Email Marketing.

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

An exact algorithm for the multi-trip vehicle routing problem with time windows and multi-skilled manpower

Motivated by the challenges of non-emergency patient transportation services in the healthcare industry, this study investigated a multi-trip vehicle routing problem incorporating multi-skilled manpower with downgrading. We aimed to find an optimal plan for vehicle routing and multi-skilled manpower scheduling in tandem with the objective of minimizing the total cost, including travel and staff costs, without violating time windows and lunch break constraints. To address this, two mathematical models were formulated: an arc-flow model and a trip-based set-covering model. In addition, a branch-and-price-and-cut algorithm, based on the set-covering model, was proposed to solve practical-scale instances. To determine the feasibility of the integer solutions, we introduce a feasibility check model. To address the multi-trip characteristics of the proposed problem, a novel two-phase column generation algorithm was introduced to solve the subproblem. This approach differs from traditional one-phase labeling algorithms and involves a tailored labeling algorithm for obtaining non-dominated labels in the first phase and a strategy to identify the trip with the minimum reduced cost for each label in the second phase. Furthermore, novel and efficient staff-based inequalities were developed by improving the k-path inequalities. Extensive numerical experiments were conducted to demonstrate the solution performance of the proposed algorithm and reveal managerial insights for non-emergency ambulance operations. The results demonstrate that our algorithm can successfully solve instances with up to 50 patients to optimality within two hours. Moreover, we demonstrated the value of jointly optimizing vehicle routing and staff planning, which can result in significant cost savings of up to 19.4%.