Optimal Scheduling Problem Research Articles

Optimization of drug scheduling for cancer chemotherapy with considering reducing cumulative drug toxicity

An improved optimal drug scheduling model with considering two control drugs is proposed and the Gauss pseudospectral-based optimization method is studied to decrease the tumor size and drug toxicity in this work. Firstly, the Dexrazoxane drug, which has significant clinical effect to reduce the toxicity of the anticancer drug, is introduced. By analyzing the growth kinetics model of cancer chemotherapy, the toxicity reduction drug is regarded as the second input in the cancer dynamic equations. Correspondingly, the drug scheduling optimization problem with particular optimization goal and necessary constraints is established. Next, a model transformation technique is proposed to reduce the complexity of dynamic equations. With deriving the Gaussian time grid discretization detailly, the Gauss pseudospectral method (GPM)-based cancer chemotherapy drug scheduling algorithm is presented to test the performance of the proposed model within different rates. Finally, the implementation structure of drug scheduling optimization is given in detail. To test and validate the performance of proposed chemotherapy model, extensive simulation results and comparative evaluation are carried out on a specific mathematical model. Simulation results show that the improved optimization model is superior to other literature studies, resulting in the average improvement of performance index by 66.54% and revealing the significant guiding property for cancer chemotherapy.

Mathematical Programming and Metaheuristics for Solving Continuous-Time Scheduling Optimization Problems in Low-Volume Low-Variety Production Systems

Mathematical Programming and Metaheuristics for Solving Continuous-Time Scheduling Optimization Problems in Low-Volume Low-Variety Production Systems

Joint Optimization of Relay Selection and Transmission Scheduling for UAV-Aided mmWave Vehicular Networks

To deal with the explosive growth of mobile traffic, millimeter-wave (mmWave) communications with abundant bandwidth resources have been applied to vehicular networks. As mmWave signal is sensitive to blockage, we introduce the unmanned aerial vehicle (UAV)-aided two-way relaying system for vehicular connection enhancement and coverage expansion. How to improve transmission efficiency and to reduce latency time in such a dynamic scenario is a challenging problem. In this paper, we formulate the joint optimization problem of relay selection and transmission scheduling, aiming to reduce transmission time while meeting the throughput requirements. To solve this problem, two schemes are proposed. The first one is the random relay selection with concurrent scheduling (RCS), a low-complexity algorithm implemented in two steps. The second one is the joint relay selection with dynamic scheduling (JRDS), which fully avoids relay contentions and exploits potential concurrent ability, to obtain further performance enhancement over RCS. Through extensive simulations under different environments with various flow numbers and vehicle speeds, we demonstrate that both RCS and JRDS schemes outperform the existing schemes significantly in terms of transmission time and network throughput. We also analyze the impact of threshold selection on achievable performance.

Optimal Cyclic Scheduling of Wafer-Residency-Time-Constrained Dual-Arm Cluster Tools by Configuring Processing Modules and Robot Waiting Time

Optimal cyclic scheduling problems of wafer-residency-time-constrained dual-arm cluster tools in wafer fabrication are challenging and remain to be fully solved. Existing studies assume that all processing modules (PMs) of a required type are used to process the same type of wafers. This sometimes brings unneeded conservation to scheduling results, because we may be able to make a tool schedulable by reducing the number of PMs in some steps if the original one is not. In some cases, we may use fewer PMs to reach the same result if the original one is schedulable. This work selects a proper number of PMs of needed types to process wafers with the highest productivity. It proposes the necessary and sufficient conditions under which a tool is schedulable and develops a polynomial-complexity algorithm that finds an optimal cyclic schedule. Examples are given to show its superiority over existing ones, thus advancing this field greatly.

Collaborative optimization method of the regional integrated energy system for energy cascade utilization

Energy is an important resource, and its main reserves are obviously insufficient compared with consumption. The energy structure is mainly dominated by fossil energy. The pollution emissions from energy utilization are becoming more and more serious. The energy problem has seriously affected the development of new urbanization. This paper studies the optimal scheduling problem of typical urban integrated energy systems participating in different demand response methods. Based on the traditional power demand response model, the demand response model is constructed according to the equivalent thermal parameters. Its model can optimize the electricity and gas load curves and realize the balance of electricity, heat, and gas in an economical and environmentally friendly way. It can also reduce the total cost of the economic operation of the system and reduce the overall carbon emissions of the system. Through the optimization and regulation of multi-type “source-storage-load”, multi-time scale, and multi-objective joint for the distributed thermal power, the matching of thermal and electric hybrid energy and the peak and valley of the power grid can be realized.

A Multiple Objective Genetic Algorithm Approach for Stochastic Open Pit Production Scheduling Optimisation

ABSTRACT The conventional approach to mine planning is to use a single estimated orebody model as the basis for production scheduling. This approach, however, does not consider grade uncertainties associated with grade estimation. These uncertainties have a significant impact on the net present value (NPV) and can only be accounted for when modelled as part of the production scheduling optimisation problem. In this research, a set of equally probable simulated orebodies generated through Sequential Gaussian Simulation is used as input to a stochastic optimisation model solved with genetic algorithm (GA). Grade variability is considered as part of the stochastic model. The problem definition and resource constraints are formulated and optimised using a specially designed mining-specific GA. This GA is employed to handle partial block processing through a specialised chromosome encoding technique resulting in near-optimal solutions. Two case studies are presented which compare results from the stochastic model solved with GA (SGA) and a Stochastic Mixed Integer Linear Programming (SMILP) model solved with CPLEX. For the second case study, while the SMILP model was at an optimality gap of 101% after 28 days, the SGA model generated an NPV of $10,045 M at 10.16% optimality gap after 1.5 h.

Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles

With the development of the sixth-generation (6 G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.

Day-ahead scheduling of the electrothermal integrated system considering multiple thermal inertia

For the problem of optimal scheduling of integrated energy systems involving wind power consumption, by mining multiple thermal inertia, a stochastic optimal scheduling strategy with consideration of mixed uncertainties is proposed. Firstly, the network thermal inertia is explored by considering the dynamic characteristics and thermal storage of heat network. Then the heat load flexibility constraint is introduced to model the thermal inertia of buildings. Finally heat pumps and heat storage are used as time-shiftable loads or sources, which are jointly modelled with CHP units to solve the peak-valley mismatch of electric and heat loads, and on this basis, the model is established. In this paper, the Barry Island’s case which is composed of 33-node thermal network coupled with 9-node grid system is used as an example for analysis, and results show that by exploring the multiple thermal inertia, both the complementarity and the flexible dispatch between multiple heterogeneous energy sources in integrated energy system can be realized.

Optimal Dispatch of Multi-energy Microgrid Considering Flexible Resources Aggregation

Distributing energy has facilitated the interaction between electricity, heating, and gas systems. Aggregating flexible resources in the optimal scheduling process of multi-energy microgrids can promote the complementary coordination of multi-energy systems and reduce useless energy waste. In this paper, flexible resource aggregation analysis is added to the optimal scheduling problem of multi-energy microgrids. Firstly, a resource aggregation model based on Mincowski addition is proposed to fully characterize the schedulable potential of flexible resources. Then, a multi-energy microgrid collaborative optimal scheduling model is established to fully exploit the operational potential of flexible resources. Finally, a numerical example is simulated to verify the validity of the aggregation model and control algorithm above-mentioned, which improves the efficiency and accuracy of optimal control of multi-energy microgrids.

Application of stochastic ranking based evolutionary strategy in environmental management of microgrids

The microgrid environmental governance problem is a single objective function, and the biogeography algorithm traditionally used for the calculation has significant shortcomings. The algorithm has a limited search range at the beginning of the iteration and tends to fall into a local optimum at the end. In addition, it is slow to converge and poor in finding the best solution when solving microgrid scheduling optimization problems. In this paper, we adopt a typical microgrid environmental management framework model and use a stochastic ranking evolutionary strategy algorithm with high performance. The objective function is to minimize the ecological management cost and optimize the rational dispatch of electricity to gas and energy storage devices. The case study proves that the stochastic ranking evolutionary strategy algorithm is more efficient and converges faster. This algorithm can effectively solve the energy dispatching optimization problem in microgrids.

Morning commute in congested urban rail transit system: a macroscopic model for equilibrium distribution of passenger arrivals

ABSTRACT This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system. We use a macroscopic train operation sub-model developed by Seo, Wada, and Fukuda to express the interaction between the dynamics of passengers and trains in a simplified manner while maintaining their essential physical relations. The equilibrium conditions of the proposed model are derived and a solution method is provided. The characteristics of the equilibrium are then examined both analytically and numerically. As an application of the proposed model, we analyze a simple time-dependent timetable optimization problem with equilibrium constraints and reveal that a ‘capacity increasing paradox’, in which a higher dispatch frequency increases the equilibrium cost, exists. Furthermore, insights into the design of the timetable are obtained and its influence on passengers' equilibrium travel costs is evaluated.

Integrated scheduling optimization of production and transportation for precast component with delivery time window

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Adaptive Task Scheduling Algorithm for Multifunction Integrated System with Joint Radar–Communications Waveform

Joint radar–communications (JRC) waveform could enable simultaneously radar sensing and communication functions in a multifunction integrated system (MFIS). Because of the special JRC waveform characteristics of MFIS, the traditional task scheduling algorithm for multifunction phased array radar cannot be applied to the MFIS to release its full potential. Therefore, a novel adaptive task scheduling algorithm for JRC waveform enabled MFIS is proposed in the paper. Firstly, the modified JRC waveform scheduling criteria for MFIS are remodeled after establishing the multi-task models. Secondly, the task scheduling optimization problem, which can reflect the scheduling capability of the JRC waveform, is reformulated, and the proposed novel adaptive task scheduling algorithm based on the JRC waveform effectively schedules different kinds of tasks. Finally, the simulation results show that the proposed algorithm not only has a high JRC waveform scheduling capability but also provides better performance compared with the traditional ones.

Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem

This paper discusses several cases of the Offline Nanosatellite Task Scheduling (ONTS) optimization problem, which seeks to schedule the start and finish timings of payloads on a nanosatellite. Modeled after the FloripaSat-I mission, a nanosatellite, the examples were built expressly to test the performance of various solutions to the ONTS problem. Realistic input data for power harvesting calculations were used to generate the instances, and an instance creation procedure was employed to increase the instances’ difficulty. The instances are made accessible to the public to facilitate a fair comparison of various solutions and to aid in establishing a baseline for the ONTS problem. Additionally, the study discusses the various orbit types and their effects on energy harvesting and mission performance.

A DRL-based online VM scheduler for cost optimization in cloud brokers.

The virtual machine (VM) scheduling problem in cloud brokers that support cloud bursting is fraught with uncertainty due to the on-demand nature of Infrastructure as a Service (IaaS) VMs. Until a VM request is received, the scheduler does not know in advance when it will arrive or what configurations it demands. Even when a VM request is received, the scheduler does not know when the VM's lifecycle expires. Existing studies begin to use deep reinforcement learning (DRL) to solve such scheduling problems. However, they do not address how to guarantee the QoS of user requests. In this paper, we investigate a cost optimization problem for online VM scheduling in cloud brokers for cloud bursting to minimize the cost spent on public clouds while satisfying specified QoS restrictions. We propose DeepBS, a DRL-based online VM scheduler in a cloud broker which learns from experience to adaptively improve scheduling strategies in environments with non-smooth and uncertain user requests. We evaluate the performance of DeepBS under two request arrival patterns which are respectively based on Google and Alibaba cluster traces, and the experiments show that DeepBS has a significant advantage over other benchmark algorithms in terms of cost optimization.

Research on schedling optimization of four-way shuttle-based storage and retrieval systems

In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the four-way shuttle system. An improved genetic algorithm is used to solve the task planning, and an improved A* algorithm is used to solve the path optimization within the shelf level. The conflicts generated by the parallel operation of the four-way shuttle system are classified, and the improved A* algorithm based on the time window method is constructed for path optimization through the dynamic graph theory method to seek safe conflict-free paths. Through simulation example analysis, it is verified that the improved A* algorithm proposed in this paper has obvious optimization effect on the model of this paper.

Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.

Energy-Efficient Resource Allocation for Dual-NOMA-UAV Assisted Internet of Things

Employing unmanned aerial vehicles (UAVs) characterized by low cost, high maneuverability, and on-demand deployment as aerial base stations (BSs) of Internet of Things (IoT) can guarantee communication performance in the absence of terrestrial BSs. However, the limited energy budget of UAV constrains its development. In this paper, a dual-UAV-assisted IoT using non-orthogonal multiple access (NOMA) is proposed to improve IoT capacity. To reduce energy consumption of the UAVs while ensuring a certain throughput, a joint resource optimization problem of communication scheduling, transmit power and motion parameters of UAVs is formulated to maximize energy efficiency of UAVs. To solve the proposed non-convex optimization problem, we present an alternating iterative optimization algorithm to alternately optimize three sub-problems: communication scheduling optimization, UAV transmit power optimization and UAV motion parameters optimization, each of which can be converted into convex optimization and solved using Lagrange multiplier method, subgradient descent method and successive convex approximation (SCA). The numerical results show that optimizing UAV motion parameters can effectively improve energy efficiency of UAVs, and the proposed dual-NOMA-UAV assisted IoT can achieve higher energy efficiency than the orthogonal multiple access (OMA)-UAV assisted IoT.

Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

Optimal Charging Scheduling of Electric Vehicles: The Co-Charging Case

A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-charging</i> parking system is a charging system in which the chargers are automated, i.e., they can automatically connect to or disconnect from vehicles presented in the parking. This system makes it possible to have less chargers than the number of parking places. This paper studies an optimal scheduling problem aiming at charging all vehicles to required battery level while minimizing the total charging cost. We formulate this scheduling problem as a non-linear optimal control problem with mixed integer constraints and mixed discrete and continuous time. The problem is solved using a Branch and Bound Strategy coupled with Pontryagin Maximum Principle (PMP) and Interior Point Methods (IPMs) for optimal control. The presented method is tested on numerous configurations and shows promising results both in term of precision and in term of convergence rate.