Scheduling Optimization Research Articles

Harnessing Senescence for Antitumor Immunity to Advance Cancer Treatment.

Considering the limitations and complexities of the cell-killing-based cancer treatment approaches, one could aim to integrate symbiotic advances in many energy delivery technologies and transformational pieces of evidence in research on senescence and immunomodulators to advance cancer treatment. Although senescent cells contribute to drug tolerance, resistance to therapy, tumorigenesis, maladapting cancer phenotypes, tumor relapse, recurrence, and metastasis, emerging pieces of evidence also demonstrate that acutely induced senescent cells in tumors can elicit a strong and lasting antitumor immune response juxtaposed to the immunologically silent apoptotic cells. This commentary is to help develop an unconventional conceptual framework to advance cancer treatment. Accordingly, it will involve transiently inducing senescent cells in tumors at optimal levels to prime the immune system with radiation, then eliminating senescent cells with senolytics (drugs that specifically eliminate senescent cells) to disrupt their positive feedback accumulation (to prevent tumor maladaptation and adverse effects in healthy cells) and unleash long-lasting antitumor immunity with immunomodulators. The approach is reasonably speculative and will require scientifically rigorous "fit-for-purpose," well-controlled preclinical research and development involving dose and schedule optimization of radiation and drugs, using representative in vitro and invivo cancer models to obtain high-quality data to proceed to clinical studies.

Integration and capacity optimization of molten-salt heat storage in coal-fired power plant with carbon capture system

Developing a new generation of economical, low-carbon, and flexible coal-fired power plants (CFPP) is crucial for achieving the low-carbon transformation of the energy industry. Deploying molten salt heat storage (MSHS) in the CFPP can effectively enhance its peak shaving capability and reduce the limitations of carbon capture on wide range operation of the plant. To this end, this paper proposes four integration schemes for MSHS into the power plant-carbon capture system. The thermal efficiencies, exergy efficiencies, and peak shaving capabilities of the entire plant under different integration schemes are evaluated and compared. The results indicate that the optimal integration scheme involves utilizing hot reheat steam to heat the cold molten salt and further provide heat for the carbon capture system during the heat charging process and utilizing hot molten salt to heat the outlet water of the feed water pump during the heat discharging process, which achieves the highest thermal efficiency of 38.00 % and exergy efficiency of 37.09 % across the entire cycle. A novel integrated design, scheduling, and control optimization (IDSCO) method is proposed to find the optimal capacity configuration of MSHS, in which the investment, maintenance, fuel, carbon emission, and long-timescale and short-timescale load tracking deviation penalty costs are fully considered. A design parameter-aware control system is developed based on a multi-parametric programming model predictive control approach to ensure a fair evaluation of closed-loop dynamic performance under different configuration capacities. The simulation results demonstrate that the deployment of MSHS can significantly enhance the flexibility of the power plant-carbon capture system and the proposed IDSCO approach can reduce 17.05 % of load tracking deviation penalty cost and 1.38 % of totalized cost compared to the conventional configuration approach. This paper effectively leverages the potential of MSHS in enhancing the operational flexibility of CFPP-PCC system, thereby providing useful guidance for future deployment of MSHS within CFPP-PCC system.

Tri-objective lot-streaming scheduling optimization for hybrid flow shops with uncertainties in machine breakdowns and job arrivals using an enhanced genetic programming hyper-heuristic

Lot-streaming scheduling has been widely recognized as a means to improve shop productivity, but there is few research on lot-streaming scheduling problems under dynamic disturbances. To fill the gap, lot-streaming scheduling optimization approach for hybrid flow shops with uncertainties in machine breakdowns and job arrivals is proposed. A mathematical model is formulated with objectives of minimizing maximum tardiness, total idle energy consumption of the machine, and maximum makespan. Since the Genetic Programming Hyper Heuristic algorithm has better results in solving dynamic scheduling problems, a Collaborative Harmony Search-based Genetic Programming Hyper Heuristic (CHS-GPHH) is presented to solve the dynamic lot-streaming hybrid flow shop scheduling problem (DLS-HFSSP) with the two dynamic events occurring simultaneously. In the improved algorithm, a neighborhood structure based on harmony search is developed for lot splitting. To verify the effectiveness of the proposed approach, the various comparative studies c are conducted on the lot-streaming dynamic hybrid flow shop scheduling. The results demonstrate the effectiveness of each improvement component of the CHS-GPHH, and verify that CHS-GPHH is an effective approach to deal with DLS-HFSSP with the in all the scenarios.

Joint optimization for energy efficient full-duplex UAV relaying with multiple user pairs

This paper investigates an unmanned aerial vehicle (UAV) assisted amplify-and-forward relaying, where a full-duplex (FD) fixed-wing UAV employs a time-division multiple access scheduling protocol to provide relay services for multiple source–destination user pairs. With the aim of maximizing energy efficiency (EE) of the system, a joint optimization problem is studied so as to jointly fulfill the communication scheduling of multiple user pairs, the transmit power control and the trajectory design of the UAV. Since the optimization variables of the problem are coupled, it is non-convex and hence hard to solve directly. To this end, the initial problem is decomposed into three subproblems corresponding to the optimization of communication scheduling, and transmit power and trajectory of the UAV, respectively. The three subproblems are solved by utilizing the linear programming, the successive convex approximation (SCA), and the Dinkelbach’s algorithm. Then an iterative algorithm based on the block coordinate descent technique is proposed to tackle the joint optimization problem by optimizing the three blocks of variables alternately. Simulation results demonstrate that the proposed algorithm converges efficiently, and the EE of the joint optimization scheme can be significantly improved compared to the benchmark schemes.

Joint Ship Scheduling and Speed Optimization for Naval Escort Operations to Ensure Maritime Security

Joint Ship Scheduling and Speed Optimization for Naval Escort Operations to Ensure Maritime Security

A multi-objective stochastic optimization model for combined heat and power virtual power plant considering carbon recycling and utilizing

In order to give full play to the energy supply potential of distributed energy resources, this paper studies the scheduling optimization of CHP-VPP. First, the CHP unit and various distributed energy sources are aggregated into VPP. Carbon recycling and utilizing are realized through carbon capture and power-to-gas devices. At the same time, carbon storage and hydrogen storage devices are added to decouple carbon capture and P2G procedures. Then, the risk of VPP real-time scheduling is quantified through uncertainty scenario generation and CVaR. Finally, with the goals of operating cost, carbon emission, and operation risk, a multi-objective stochastic scheduling optimization model of VPP is constructed, and the subjective and objective ensemble weighting method is used to solve the problem. The example results show that the proposed method can boost the wastage of wind and photovoltaic power, and also lower the carbon emissions of VPPs.

Multi-Agent DRL-Based Resource Scheduling and Energy Management for Electric Vehicles

With the emergence of vehicular edge computing (VEC) and electric vehicles (EVs), integrating computation and charging tasks presents challenges due to limited resources and dynamic vehicular networks. This research focuses on the joint optimization of computation offloading and charging scheduling in VEC networks. Specifically, we optimize the offloading factor, charging association variable, and charging rates to minimize the system delay and energy consumption by leveraging the multi-attributes of EVs in both information and energy networks. Considering the dynamic environment, we model the problem as a Markov Decision Process, and use the Multi-Agent Reinforcement Learning (MARL) algorithm MADDPG, with its centralized training and distributed execution mechanisms. Simulation results demonstrate that this approach significantly improves utility while reducing energy consumption and latency.

Depot Charging Schedule Optimization for Medium- and Heavy-Duty Battery-Electric Trucks

Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.

A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow scheduling

This study presents a novel multi-objective approach for NP-hard workflow scheduling in cloud computing environments. Traditional rule-based heuristics offer flexibility but lack consistent superiority across all criteria and scenarios. The development of specific rules usually requires tedious manual refinement by experts. To overcome this limitation, our method leverages evolutionary computation and simulation to automatically derive new rules. Moreover, workflow scheduling involves two crucial and related aspects: task scheduling and the allocation of virtual machines. Our contribution includes three distinct algorithms: two that address each decision separately and a third approach that coevolves priority rules for both decisions simultaneously. Computational tests demonstrate superior performance, with an exploration of rules yielding a 72.91% larger hypervolume for optimizing makespan and costs compared to benchmark heuristics from the literature. The validation on unseen instances shows a 90.26% improvement in hypervolume performance, highlighting the robustness of our approach.

Machine learning‐driven implementation of workflow optimization in cloud computing for IoT applications

AbstractThe optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML‐driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large‐scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

Research on Train Loading and Unloading Mode and Scheduling Optimization in Automated Container Terminals

In some automated container terminals, railway lines have been implemented into the port, saving container transfer time. However, the equipment scheduling level of the railway yard needs to be improved for managers. In the equipment scheduling of loading and unloading containers for railway trains, the operation modes “full unloading and full loading” and “synchronous loading and unloading” are often adopted. Due to the long length of the railway yard and the line of one train, there are two ways to arrange loading and unloading tasks for automated rail-mounted gantry cranes (ARMGs): one is to pre-assign tasks for ARMGs, and the other is to not pre-assign tasks for ARMGs. To investigate the efficacy of these different operation modes and methods of assigning tasks, this study formulated three mixed-integer linear programming (MILP) models with the goal of minimizing the ARMG task completion time. An adaptive large neighborhood search algorithm was used to tackle the scheduling problem. The scheduling effects of different operation modes and methods for assignment tasks were compared in terms of their calculation time and the completion time of ARMG tasks. Notably, the findings reveal that, with an increase in the number of tasks, the “pre-assign” task arrangement had a limited effect on the completion time of the ARMG tasks, made the calculation time shorter, and reduced the complexity of the problem. From the perspective of the completion time of ARMG tasks, the time under the “synchronous loading and unloading” operation mode was less than that of the “full unloading and full loading” operation mode. Therefore, it is recommended that the managers of the railway yard in an automated container terminal adopt the “synchronous loading and unloading” operation mode but determine the task assignment method according to decision time requirements. In addition, when the number of tasks is large, to decrease the time to complete ARMG tasks, the manager can adopt the “non-pre-assign” task distribution method.

Joint optimization of job scheduling, condition-based maintenance planning, and spare parts ordering for degrading production systems

The system degrades gradually due to intrinsic properties and the external environment while processing scheduling jobs. Maintenance plays an important role in restoring system performance during the job scheduling process. In contrast to traditional integration studies of scheduling and maintenance, this study further considers spare parts inventory in integrated decision-making. By integrating spare parts ordering information, engineers can meticulously organize maintenance and spare parts replenishment, thereby bolstering system reliability and ensuring smooth production scheduling. This paper presents a mathematical model that jointly optimizes job scheduling, condition-based maintenance planning, and spare parts ordering for deteriorating production systems. The system's degradation state and spare parts inventory state are identified and revealed at the end of the scheduling job. A condition-based maintenance and spare parts ordering policy is triggered based on the captured joint state. Subsequently, we developed a joint optimization model to determine the optimal maintenance thresholds, spare parts ordering thresholds, and job sequences, aimed at minimizing the total weighted expected cost within the scheduling horizon. Finally, an effective genetic algorithm is proposed, and a practical case study involving a rolling mill system is utilized to validate the proposed policy.

Improvement of Operational Reliability of Units and Elements of Dump Trucks Taking into Account the Least Reliable Elements of the System

The present work is devoted to the analysis of the most important reliability indicators of components of electrical devices of mining dump trucks, and analytical methods of their evaluation are proposed. A mathematical model for calculating the reliability of electrical devices integrated into the electrical systems of quarry dump trucks is presented. The model takes into account various loads arising in the process of operation and their influence on reliability reduction. Optimisation of maintenance and repair schedules of electrical equipment has revealed problems for research. One of them is the classification of electrical equipment by similar residual life, which allows the formation of effective repair and maintenance cycles. The analysis of statistical data on damages revealed the regularities of their occurrence, which is an important factor in assessing the reliability of electrical equipment in mining production. For quantitative assessment of reliability, it is proposed to use the parameter of the average expected operating time per failure. This parameter characterises the relative reliability of electrical equipment and is a determining factor of its reliability. The developed mathematical model of equipment failures with differentiation of maintained equipment by repeated service life allows flexible schedules of maintenance and repair to be created. The realisation of such cycles makes it possible to move from planned repairs to the system of repair according to the actual resource of the equipment.

Early Treatment Schedule Optimization of Trained-Immunity Mediated Immunotherapy of Non-Muscle Invasive Bladder Cancer (NMIBC): A Research Protocol

Early Treatment Schedule Optimization of Trained-Immunity Mediated Immunotherapy of Non-Muscle Invasive Bladder Cancer (NMIBC): A Research Protocol

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Optimal wind and solar sizing in a novel hybrid power system incorporating concentrating solar power and considering ultra-high voltage transmission

The coordinated operation of concentrating solar power (CSP) and traditional thermal power can facilitate the integration of variable wind and solar renewable energy (VRE) into the grid supported by ultra-high voltage (UHV) transmission line, forming a novel HRES. The optimal sizing of VRE in this novel HRES has been rarely investigated. This paper thus proposed a framework serving the capacity configuration optimization for VRE in aforementioned HRES. The lifecycle economic and technical performance of the HRES was evaluated to guide capacity planning. The operational characteristics of the thermal power and CSP were investigated via short-term scheduling optimization. The methodology was applied to a HRES base located in Qinghai Province, China. Results indicate a maximum capacity ratio of VRE to flexible power of 1.74:1 without power curtailments. Further expansion of VRE leads to an escalation in power curtailments. The optimal VRE sizing is determined as a combination of 4000 MW solar power and 1000 MW wind power, which achieves a levelized cost of energy as low as 0.363 CNY/kWh, corresponding to a capacity factor of 0.32. The role of CSP in mitigating VRE fluctuations in HRES is more significant than that of thermal power due to its superior flexibility and low operating costs. The inter-day heat transfer of CSP can further facilitate the stability of the HRES output between consecutive days. These findings provide valuable insights for capacity configuration and operation of the new HRES, which will promote the utilization of renewable energy and cleaner production of the power system.

Power allocation in NOMA using sum rate-based dwarf mongoose optimization

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.

Joint Client Scheduling and Quantization Optimization in Energy Harvesting-Enabled Federated Learning Networks

Joint Client Scheduling and Quantization Optimization in Energy Harvesting-Enabled Federated Learning Networks

Performance analysis and optimization of multi-energy complementary combined heat and power system considering the influence of time-of-use price

Abstract The multi-energy complementary system can meet the increasingly abundant and diversified energy consumption needs of users and, at the same time, help to improve energy utilization efficiency, reduce environmental pollution, and optimize the balance of energy supply and demand. In this paper, a multi-energy complementary cogeneration system with a gas turbine distributed photovoltaic, gas boiler, and heat storage tank is proposed, and the operation scheduling optimization model of the system is constructed. Aiming to minimize the comprehensive cost of economy and carbon emission, the model adopts a linear programming method to optimize the operation scheduling of multi-energy complementary systems. Taking a multi-energy complementary cogeneration system as an example, the key equipment of the system is optimized under three different scenarios with different time-of-use prices, and the influence of time division of time-of-use electricity price on the comprehensive cost of the system is analyzed. The results show that the time division method, which is more in line with the system net load curve, can effectively reduce the comprehensive cost of the complementary system and increase the consumption space of new energy.

A self-learning particle swarm optimization for bi-level assembly scheduling of material-sensitive orders

In modern assembly production, maximizing fulfillment becomes challenging amid resource scarcity and process unpredictability. This study addresses such issues with an effective approach to solving the integrated problem. This confluence presents a scenario where resource allocation mirrors a multidimensional knapsack and job sequencing corresponds to distributed assembly permutation flowshop scheduling, factoring in sequence-dependent setup times. Employing a mixed-integer mathematical model, we capture the configurations and objectives of the integrated problem. A special dual-level cooperative self-learning particle swarm optimization (SLPSO) metaheuristic is developed for improved hierarchical decision-making. Adaptive learning is harnessed to guide dynamic job selection under uncertainty, aiming for the highest total value. To minimize tardiness, a neighborhood search operator customizes sequences for each shop, considering changing setup times between jobs. Benchmark tests have been performed, showing that SLPSO surpasses established genetic algorithms and swarm techniques by 8%–15%. When implemented in an operational traditional pharmaceutical manufacturer, significant improvements were observed. The integrated structure harmonizes planning and scheduling stages, enhancing the algorithm via bidirectional feedback between decisions at various levels under uncertainty, thus providing guidance for real-world production.