Cost Schedule Research Articles

Project Performance Analysis Based on Earned Value Method on Infrastructure & Infrastructure Improvement of Yohanis Kapiyau Timika Airstrip

This study analyzes the performance of construction projects using the Earned Value Method (EVM) on the Infrastructure and Facilities Improvement Project at Yohanis Kapiyau Airbase in Timika. Utilizing key performance indicators such as Cost Variance (CV), Cost Performance Index (CPI), Schedule Variance (SV), and Schedule Performance Index (SPI), the research evaluates cost and time deviations to provide a comprehensive understanding of project performance. EVM offers precise insights into the project's current status, including estimates for total costs and completion timelines, which are critical for effective decision-making and ensuring adherence to project plans. The findings demonstrate that EVM is a reliable tool for accurately monitoring and managing construction projects, enabling stakeholders to identify inefficiencies and implement corrective measures in a timely manner. By providing an in-depth evaluation of cost efficiency and schedule performance, the study contributes to the broader application of EVM in enhancing construction project management. This research is expected to benefit academics by advancing theoretical knowledge, assist contractors in optimizing project execution, and inform public stakeholders on best practices for infrastructure development, ultimately contributing to more effective and efficient management of construction projects in Indonesia.

Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas.

Application of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization

The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization. First, the work analyzes the complexity and uncertainty challenges faced in clean energy scheduling within the current power system, highlighting the limitations of traditional methods in handling multi-objective optimization and real-time response. Consequently, the work introduces the ESG big data platform and leverages its abundant data resources and computational power to improve the scheduling decision-making process. Next, PSO is used for initial scheduling optimization and a mathematical model for clean energy scheduling optimization is constructed. To further enhance the dynamic response capability of the scheduling process, this work designs a dual-layer architecture. Among this architecture, DQN is responsible for adjusting the PSO initially optimized scheduling plan based on real-time data during the actual scheduling process, adapting it to the instantaneous change demand and renewable energy output characteristics. Finally, to verify the model’s effectiveness, this work conducts simulation analyses using real data from the state grid ESG big data platform. The results indicate that this method significantly improves clean energy utilization, increasing it from 62.4% (with traditional methods) to 87.7%, while reducing scheduling costs by 22%. With the increase in communication delay, the generation time of scheduling instruction increases significantly. This is because the algorithm needs to wait longer to receive the complete grid status information, which affects the generation speed of scheduling instructions. Moreover, this method demonstrates good adaptability and stability under different load demands and clean energy supply conditions. This work introduces a novel and efficient method for clean energy scheduling optimization by integrating PSO and DQN. It contributes to the overall improvement of clean energy utilization within the State Grid and provides a theoretical and empirical foundation for further research in related fields.

Fuzzy repetitive scheduling for construction projects considering risk preferences and time-cost tradeoffs

PurposeThe purpose of this study is to develop a novel approach that addresses time-cost tradeoffs in repetitive construction projects while considering the uncertainty in activity durations and the risk preferences of planners.Design/methodology/approachOur study involves work in three aspects. Firstly, it employs triangular fuzzy numbers to represent activity durations in different units, which facilitates the management of scenarios characterized by limited historical data or the presence of ambiguous information. Secondly, it introduces a fuzzy chance-constrained programming model, which is aimed at minimizing the project budget while ensuring that the risks associated with cost overruns and schedule delays are confined to specified limits. Thirdly, it advances an enhanced genetic algorithm, integrating an electromagnetism-like mechanism and a scheduling repair process, to improve the efficiency and effectiveness of the optimization process.FindingsA real-life street renovation project was analyzed to demonstrate the applicability of the proposed algorithm. The analysis explored three common types of risk preferences: risk-averse, risk-neutral and risk-loving. The results indicate that the proposed algorithm surpasses existing fuzzy repetitive scheduling methods in terms of risk management. It effectively generates schedules that align with the risk preferences of planners and provides worst-case estimates of project performance.Originality/valueThis research makes a significant contribution to the field by developing a fuzzy chance-constrained programming model and an associated optimization algorithm that is specifically designed for time-cost tradeoffs in repetitive construction projects. A key distinction is that this study considers the risk preferences of planners, which sets it apart from previously developed models. As a result, it provides a practical approach for effective risk management.

Data-driven robust optimization scheduling for microgrid day-ahead to intra-day operations based on renewable energy interval prediction

The uncertainty in renewable energy forecasting significantly impacts microgrid scheduling, and traditional scheduling schemes are often overly conservative and limited by a single time scale, resulting in unreasonable strategies that fail to balance reliability and cost-effectiveness. Based on this, a comprehensive two-stage day-ahead and intra-day microgrid scheduling framework is proposed integrating forecasting, regulation, and decision-making. Based on historical power data of renewable energy, a multi-kernel covariance function is applied to improve Gaussian process regression (GPR) for adaptively generating renewable energy power prediction intervals at various confidence levels and verifies the construction of robust optimization uncertainty scenario sets. In the day-ahead scheduling phase, a two-stage adaptive robust optimization model based on interval probability uncertainty sets is established to ensure minimal scheduling costs under the worst-case scenario. Meanwhile, a modified deep Q network (MDQN) algorithm based on a k-priority sampling strategy is proposed to transform the two-stage iterative process into a discrete Markov decision process for solution. In the intra-day scheduling phase, the day-ahead scheduling scheme is followed, and the intra-day scheduling scheme is optimized through multi-time-scale rolling optimization to reduce the impact of renewable energy power fluctuations. Case studies validate that the proposed scheduling method ensures robustness against uncertainties in renewable energy output while also maintaining the economic efficiency of system operations.

Bus Schedule Time Prediction Based on LSTM-SVR Model

With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model.

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Abstract Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment. In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified. We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Evaluating potential program cost savings with a single-dose HPV vaccination schedule: a modeling study.

There is limited evidence on the magnitude of the potential program cost savings associated with the World Health Organization-endorsed single-dose schedule for the human papillomavirus (HPV) vaccine. The objective of this analysis was to model the delivery and vaccine procurement cost implications of the new schedule. The analysis leveraged primary data during a study evaluating the HPV vaccine delivery costs and operational context in 5 countries (Ethiopia, Guyana, Rwanda, Sri Lanka, and Uganda) implementing a two-dose schedule. To estimate the cost for the single-dose schedule, we adjusted the two-dose schedule cost estimates to account for differences in the frequency of activities, whether activities differed by HPV vaccine dose or session, and differences in relative quantity or storage volume of HPV vaccines delivered. We estimated the cost per dose and cost per adolescent receiving the full (single-dose or two-dose) vaccination schedule in 2019 US dollars from a health system perspective. Modeled results found that cost per dose would increase under a single-dose schedule, whereas cost per adolescent receiving the full schedule would decrease. The financial cost for vaccine procurement and delivery per adolescent receiving the full schedule ranged from $9.64 (Sri Lanka) to $23.43 (Guyana) under a two-dose schedule and decreased to $4.84 and $12.34, respectively, under a single-dose schedule, reflecting savings up to 50%. For economic costs, the range for a single-dose schedule was $7.86 (Rwanda) to $28.53 (Guyana). A single-dose HPV vaccination schedule could provide cost savings to immunization programs and enhance program affordability and sustainability.

Ferric derisomaltose versus usual care in patients with heart failure, reduced ejection fraction, and iron deficiency: a short-term UK cost-utility analysis based on the IRONMAN RCT

Abstract Background In the IRONMAN trial, patients with heart failure (HF), a reduced ejection fraction and iron deficiency who were randomly assigned to ferric derisomaltose (FDI), rather than usual care, had a lower rate of the composite of hospitalization for heart failure (HHF) and cardiovascular (CV) death (rate ratio, RR, 0.82; 95% CI 0.66 to 1.02; p=0.07). A COVID-19 sensitivity analysis, censoring data at 1 year of follow-up showed a greater magnitude of treatment effect (RR 0.66; 95% CI 0.48 to 0.91; p=0.011). Purpose Patient-level data from IRONMAN were used to evaluate the effectiveness and cost-utility of FDI compared to usual care for patients with a current or recent HHF, or raised plasma natriuretic peptide concentrations, from a UK National Health Service (NHS) perspective. Methods A cost-utility analysis (CUA) was conducted covering the first year after randomization. Numbers-needed-to-treat (NNTs) were calculated for the primary endpoint censored at 1 year. In the CUA model, patient survival and rates of (re)hospitalization due to HF, other CV events, infections, respiratory conditions, and other non-CV events were modeled based directly on data from the IRONMAN RCT. UK-specific costs were obtained from the national schedule of NHS costs. Quality of life was estimated based on EQ-5D-5L data collected from patients included in the IRONMAN RCT. Results were undiscounted and reported as absolute and incremental costs in 2023 pounds sterling, and quality-adjusted life expectancy in quality-adjusted life years (QALYs). A willingness-to-pay threshold of £20,000 per QALY was adopted to allow calculation of a net monetary benefit (NMB), a summary measure of the overall value of FDI in monetary terms, capturing changes in both cost and QALY outcomes. Results The NNT analysis showed that 9 patients would need to be treated with FDI to avoid one composite primary endpoint event (CV death and HHF) over 1 year. Relative to usual care, FDI was associated with an improvement of 0.01 QALYs per patient over 1 year (0.58 versus 0.57 QALYs). Total average costs per-patient were £1,672 for FDI, versus £1,975 for usual care, leading to a saving of £302 per patient with FDI. The cost of iron procurement was more than offset by reductions in costs of (re)hospitalizations (Figure). FDI was therefore dominant over usual care, yielding an NMB of £483 over 1 year. Conclusions This short-term, within-trial CUA suggests that FDI both improves QALYs and reduces healthcare expenditure versus usual care when administered to patients with HF and iron deficiency in the UK. Iron repletion with FDI may be a prudent allocation of healthcare resources in this patient population.Breakdown of costs

Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.

Meta-heuristic-based home energy management system for optimizing smart appliance scheduling and electricity cost reduction in residential complexes

Meta-heuristic-based home energy management system for optimizing smart appliance scheduling and electricity cost reduction in residential complexes

Primjena ekspertnih sustava u građevinarstvu

Expert systems represent “IF-THEN” algorithms that integrate human expert knowledge to solve complex problems. This area of research focuses on finding solutions to challenging situations that are often difficult to resolve using traditional methods. Expert systems have widespread applications in the field of construction. The paper presents some of these applications, including their use in building maintenance, inspection of water and sewage systems, geotechnical engineering, road maintenance, and the selection of appropriate construction materials. Artificial neural networks are effective in optimizing financial plans, estimating the duration of activities, assessing the productivity of work teams, and selecting contractors. Genetic algorithms find their application in resource allocation, selecting optimal solutions for task scheduling and project costs, as well as in building maintenance, while fuzzy logic aids in risk analysis, project management, and cost estimation. Expert methods can be integrated into information systems to provide consistent information necessary for the effective management of construction projects. A review of published papers on the application of expert methods in construction shows that these methods are already significantly represented across all sectors of the construction industry, but also indicates that there are still substantial opportunities for their future application.

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

Study on Single-Machine Common/Slack Due-Window Assignment Scheduling with Delivery Times, Variable Processing Times and Outsourcing

Single-machine due-window assignment scheduling with delivery times and variable processing times is investigated, where the variable processing time of a job means that the processing time is a function of its position in a sequence and its resource allocation. Currently, there are multiple optimization objectives for the due-window assignment problem, and there is a small amount of research on optimization problems where the window starting time, the rejected cost and the optimal scheduling are jointly required. The goal of this paper is to minimize the weighed sum of scheduling cost, resource consumption cost and outsourcing measure under the optional job outsourcing (rejection). Under two resource allocation models (i.e., linear and convex resource allocation models), the scheduling cost is the weighted sum of the number of early–tardy jobs, earliness–tardiness penalties and due-window starting time and size, where the weights are positional-dependent. The main contributions of this paper include the study and data simulation of single-machine scheduling with learning effects, delivery times and outsourcing cost. For the weighed sum of scheduling cost, resource consumption cost and outsourcing measure, we prove the polynomial solvability of the problem. Under the common and slack due-window assignments, through the theoretical analysis of the optimal solution, we reveal that four problems can be solved in O(n6) time, where n is the number of jobs.

SP6.14 - The Burden of Penetrating Junctional Injuries in a UK Major Trauma Centre: a Retrospective Cohort Study

Abstract Background Junctional injuries occur at the join of ‘anatomically distinct zones’. The emergence of junctional injuries in trauma care has been a consequence of recent battlefield casualties; accounting for 21% of potentially survivable deaths from haemorrhage. Although well described in military literature, there are no civilian studies into junctional injuries. This study aims to examine the epidemiology and resource burden of junctional penetrating injuries. Methods Retrospective study of adult and paediatric patients with penetrating injuries to a junctional zone presenting to an urban MTC in one year. Electronic patient records were searched and data on anatomical distribution of injury, imaging and theatre utilisation, inpatient stay, and discharge codes extracted. Discharge codes were referenced with the National Schedule of NHS costs to produce a cost. Results In 132 patients 92.4% (n=122) were male with median age 27.5 and in hospital mortality 1.6% (n=2). Saturday and Sunday were most frequent 37.9% (n=50) and 81.8% (n=108) attended after 1700 and before 0800. 37.9% (n=50) of injuries were buttock; 22% (n=29) neck; 18.9% (n=25) multiply-injured; 8.3% (n=11) axilla; 6.8% (n=9) diaphragm; and 6.1% (n=8) groin. Median Injury Severity Score (ISS) 8. 46.2% (n=61) underwent surgery; 50.8% (n=67) were admitted, 7.6% (n=10) to ITU. Median length of ward stay was 3 days. 15.9% (n=21) received blood products. The overall cost was £914,076. Conclusion Junctional injuries are a significant resource burden. Buttock wounds were the most common but had the lowest median ISS and cost. Diaphragmatic injuries appear distinct from junctional wounds in terms of ISS and cost, and may be separate to other junctional zones.

SP5.9 - A cost-benefit analysis of a digital post-operative integrated care plan app for surgical patients

Abstract Aims To study the cost-benefit analysis of a digital post-operative integrated care plan app for surgical patients. Methods 1. Preliminary data from patient survey - we designed a questionnaire to assess patients’ views of the Post Op app. 2. Estimates for NHS services cost - derived from the King’s Fund latest report (2023) and the NHS Digital National schedule of reference costs (2017, 2021-22). 3.Estimates for travel-associated costs - We assumed a total cost per mile of 16p, and also assumed the patients would need to pay an average cost of £1.6/h for hospital parking for an average of 3h per visit. We assumed a carbon emission of 221.4 grams per mile (Department for Transport data). 4.Modelling approach - We used a conservative modelling approach simulating different patient groups with varying levels of postoperative anxiety/complications. Results Taking into account avoidable presentations to ED, costs borne by the patients, lost productivity, environmental costs, avoidable re-operations and the costs related to antibiotic tolerance/resistance, the lower boundary of total savings from a digital post-operative follow-up tool equates to £58,739 per 100 patients and the upper boundary is £79,949 per 100 patients. Conclusion Post Op application provides excellent support for most patients in the immediate postoperative period and is likely to result in a significant reduction in the number of contacts patients make with the GP or hospital, and with the Emergency Department resulting in significant cost saving for both the service and the patients. Furthermore, earlier recognition, monitoring and appropriate treatment of SSIs provides further savings whose magnitude is likely underestimated.

A Profit Maximization Problem of Equipment Rental and Scheduling with Split Services

This study addresses a scheduling problem arising from the “Rent Instead of Buy” business model where rental service platforms rent equipment from suppliers to satisfy customer requests in exchange for a lump-sum payment. Suppliers often implement piecewise linear concave charging policies to encourage longer rental duration. This policy prompts the platform to consolidate service periods into longer rental agreements for lower rental rates, potentially necessitating the splitting of a customer request among multiple facilities. We establish an integer programming model to maximize the platform’s profit, which is equal to the total revenue from serving customers minus the total rental and scheduling costs. To solve the problem, we propose two Dantzig-Wolfe decomposition reformulations and design branch-and-price algorithms accordingly. Furthermore, by exploiting the property of the concave rental cost functions, we derive the optimal structure regarding the start and end times of services, significantly reducing the subproblem’s search space. Numerical experiments based on real-life data validate the efficiency of the designed algorithms compared to commercial solvers and highlight the benefits of split services. Sensitivity analysis reveals that as the rental cost function becomes more segmented, more requests are split-serviced, but the times for splitting a request are seldom more than once.

Voyage scheduling and energy management co-optimization in hydrogen-powered ships

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance.