Multi-objective Optimal Scheduling Research Articles

High-Dimensional Robust Multi-Objective Optimization for Order Scheduling: A Decision Variable Classification Approach

This paper tackles the high-dimensional robust order scheduling problem. A multi-objective evolutionary algorithm called constrained nondominated sorting differential evolution based on decision variable classification is developed to search for robust order schedules. The decision variables are classified into highly and weakly robustness-related variables according to their contributions to the robustness of candidate solutions. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust optimization problem. It is also unveiled that the order scheduling is greatly affected by the uncertain daily production quantities. The robust order schedules are able to provide more information on earliness/tardiness of the orders, which enhances the flexibility of the production.

Study on Energy Consumption Optimization Scheduling for Internet of Things

In the complex working environment of the Internet of Things (IoT), there are many differences in the work between many devices, as well as complex associations and energy constraints. The entire task scheduling system needs to consume a large amount of energy for communication. In order to describe its relevance and constraint relationship, traditional scheduling modeling methods need to add a large number of constraints. In this paper, an energy loss optimization scheduling modeling method based on the multi-objective fuzzy algorithm is proposed. Based on the equipment scheduling energy cost and equipment scheduling time in the IoT environment, the multi-objective equipment scheduling optimization equation in the IoT environment is constructed, and the fuzzy algorithm is integrated into the single-target energy loss problem. The algorithm searches for the idle time of the device and optimizes the device scheduling energy consumption model to reduce the overall energy consumption of the device scheduling in the IoT environment. The experimental results show that based on the multi-objective fuzzy algorithm, the equipment scheduling modeling method in the IoT environment considering energy loss has the characteristics of high precision and energy saving.

Interval Multi-Objective Optimal Scheduling for Redundant Residential Microgrid With VESS

Optimal scheduling of building microgrid could yield economical savings and reduce the pollutants emission, while ensuring the comfort level of users. A multi-objective optimal scheduling problem is studied for one kind of building microgrid-redundant residential microgrid (RR-microgrid). The radiant floor heating/cooling system widely utilized in residential buildings is treated as the virtual energy storage system (VESS) to dig up the considerable thermal storage capacity. Interval number is employed to describe the random fluctuation of uncertainty factors, such as renewable energy power, electric load, and weather conditions in the process of microgrid operation, while the exchange power with external grid and the output power of micro-gas turbines are, respectively, taken as the mean to smooth the electric power fluctuation, and two different optimization scheduling models which take the operating cost (OC), thermal comfort level (TCL), and pollutant emission (PE) as the optimization objectives are developed, and an improved non-dominate sorting genetic algorithm II (NSGA-II) is proposed to search the Pareto front of the scheduling models. The case study for heating in winter is performed and the results demonstrate the effectiveness of the proposed optimal scheduling method.

A multi-scenario and multi-objective scheduling optimization model for liquefied light hydrocarbon pipeline system

The mixed transportation mode based pipeline system plays a significant role in the supply chain of liquefied light hydrocarbon. Inefficient scheduling may lead to problems of frequent pump stoppage/restart, high energy consumption, and frequent pressure fluctuation. To avoid these problems, this paper implements the discrete-time representation to develop a multi-scenario and multi-objective mixed integer nonlinear programming model to minimize the pump operation cost and the number of the switching operation. In the model, several factors, such as transportation mode, inventory limits of injection stations, flow rate and pressure limits of pipeline segments, are taken into consideration. Additionally, the uncertainty of the liquefied light hydrocarbon production is described by scenarios. The improved augmented ε-constraint method is implemented to solve this model. Finally, the model is successfully applied to a multi-source single-sink liquefied light hydrocarbon pipeline system in Daqing oilfield in China. The experimental results show that the proposed model outperforms another two available models in profit, safety, and robustness aspects.

An empirical study on solving an integrated production and distribution problem with a hybrid strategy.

Coordination is essential for improving supply chain performance, and one of the most critical factors in achieving the coordination of a supply chain is the integrated research of production and distribution. In this paper, a novel two-stage hybrid solution methodology is proposed. In the first stage, products are processed on the serial machines of multiple manufacturers located in two industrial parks. A fuzzy multi-objective scheduling optimization is performed using a modified non-dominated sorting genetic algorithm II (NSGA-II). The result obtained in the first stage is used in the second stage to optimize the distribution scheduling problem using a modified genetic annealing algorithm (GAA). Finally, simulation results verify both the feasibility and efficiency of the proposed solution methodology.

A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem

The blocking flow-shop scheduling problem (BFSP) has been aroused general attention due to its broad industrial applications. However, most researches about it mainly focus on optimization of single objective. Multiple objectives are less considered simultaneously. Actually, in the practical production, the consideration of multiple objectives simultaneously could give more realistic solutions to the decision maker. Therefore, in this paper, we propose a novel multi-objective discrete water wave optimization (MODWWO) algorithm to solve a multi-objective BFSP (MOBFSP) that minimizes both makespan and total flow time. In the proposed algorithm, a decomposition-based initialization strategy is developed to generate a population with high quality and diversity. Then, a ranking-based propagation operator is designed to guide the global exploration and local exploitation of algorithm. Afterwards, a local intensification-based breaking operator is applied to improve the quality of the new created waves. Furthermore, a problem-specific refraction operator is incorporated to avoid being trapped in local optimum. The proposed algorithm is evaluated based on the benchmark instances, and compared with several state-of-the-art multi-objective scheduling optimization approaches. The comparison results show that the proposed MODWWO is a high-performing method for the considered MOBFSP.

A multi-objective scheduling optimization algorithm of a camera network for directional road network coverage.

Effective video monitoring systems require optimization of camera and road network coverage, to exploit fully the hardware and software solutions in smart city traffic applications. Monitoring requirements have grown increasingly diverse as scenes are becoming increasingly complex, thereby transforming the camera and road network coverage optimization issue into a nonlinear, high-dimension, and multi-objective problem. Previous research on this topic however, has focused on a single, specific optimization objective, which may result in invalid optimization results in actual applications. To extend this research, we propose a multi-objective scheduling optimization algorithm for a camera network that addresses the problem of directional road network coverage. In this solution, we incorporate an expanding parameter of main optical axes into particle swarm optimization algorithm. Our new strategy divides the range of main optical axes of all the cameras to control the scheduling number, achieving collaborative optimization of multiple objectives. In a simulated camera and road network, an experiment was designed for evaluating the effectiveness of the proposed method, comparing the distribution of optimization results with the global and local optimal solutions of the true value. A second experiment compared the distribution, performance and running time of the optimization results with different values of expanding parameter of main optical axes. A third experiment compared the performance of the optimization solutions with different values of camera parameters. The results showed that the proposed method can adapt to user application preference, and is effective and robust to schedule and allocate monitoring resources in different scenarios.

Multi-Objective Optimization for scientific workflow task scheduling in IaaS Cloud

The use of scientific applications on cloud networks increases day by day generating volumes of data and consuming large computational power. These scientific applications find its importance in the field of astronomy, geology, genetics and bio-technology etc. Complex and mission critical scientific applications can be modeled as scientific workflows and can be executed in cloud. The tasks of the scientific applications are generally data intensive and compute intensive. Traditional computer networks are not suitable for handling scientific applications and hence ubiquitous distributed networks like cloud are prominent in hosting scientific applications. The cloud hosted scientific applications and the cloud network need to satisfy many objectives to the interest of its users. This paper explores the multi-objective optimization applications in scientific workflow task scheduling in IaaS cloud and the related algorithms employed.

Optimal Scheduling of Power System Incorporating the Flexibility of Thermal Units

Due to the randomness, volatility and intermittent nature of wind power, power systems with significant wind penetration face serious “curtailment” problems. The flexibility of a power system is an important factor that affects the large-scale consumption of wind power. Based on this fact, this paper takes into account the economics and flexibility of the system, and proposes an optimal scheduling method that takes the flexibility of each thermal power unit into account. Firstly, a comprehensive evaluation index system of thermal power unit flexibility is designed by an analytic hierarchy process and entropy method. The system covers the technical indexes and economic characteristics of thermal power units and is able to quantitatively evaluate the different types of thermal power units in the system. Secondly, a multi-objective optimization scheduling model involving the overall flexibility of the unit and the total power generation cost is established. Finally, the correctness and effectiveness of the proposed indicators and models are verified by a case study.

A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

A Research on Multi-objective Generation Optimization Scheduling of the TGC Reservoirs Considering Ecological and Shipping Benefits

Due to the increasing energy demand and the lack of comprehensive utilization of water resources, this paper has established a generation-shipping-ecological optimization (GSEO) model to Balance the benefits between shipping, power generation and ecology. then, the Three Gorges Cascade (TGC) is taken as the research object, and the efficiency evaluation methods of shipping and ecological benefits are proposed. Finally, the actual inflows of the abundant, average and dry years are studied as the samples to solve the GSEO model. The simulation results indicate that the model and the benefit evaluation methods proposed in this paper are feasible and effective.

Robust multi-objective optimization for energy production scheduling in microgrids

ABSTRACTIn order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.

Probabilistic multi-objective optimal scheduling of plug-in hybrid electric vehicles in the distribution system

Unlike previous papers that hypothesize specific inputs and present fixed outputs for optimal charging of electric vehicles, this paper offers a multi-objective probabilistic charging algorithm based on the point estimate method (PEM) for aggregation of plug-in hybrid electric vehicles and investigates its impacts on the distribution system. In other words, the uncertainty related to the outputs of different charging algorithms is determined using the PEM. Moreover, from the perspective of an aggregator, simultaneous optimization of the minimum charging cost and minimum charging time is proposed to satisfy the customers. The proposed algorithm is optimized through multi-objective scheduling and applying distribution network constraints. Finally, the proposed method is applied to the IEEE-37 node test feeder, and simulation results are presented to illustrate its performance.

Research on Short-term Multi-objective Optimization Scheduling oriented Peak Regulation of Power Network

With the succession of river basins and inter-basin hydropower stations, the joint optimal operation of cascade hydropower stations in the river basin has large-scale, nonlinear, strong coupling, and multi-target characteristics, and must consider the effects of hydrometeorology, water demand, and power grid security. Focusing on the preparation of short-term power generation plans for cascade hydropower stations on the Qingjiang River, a comprehensive multi-objetive power generation planning model with the largest total power generation and the least load variance on the power grid is established. Based on the constraint processing method of multi-objective optimization scheduling in long-term, the optimal flow distribution technology is adopted to improve the accuracy of power generation planning. The above model is solved by using SMPSO. The results show that the improved algorithm can effectively overcome the shortcomings of slow convergence speed and easy convergence to local optimum. It can improve the power generation efficiency of the whole cascade while responding to the peaking demand of the power grid and provide a new solution to the short-term power generation planning ideas.

Multi-objective optimization for Train scheduling of lines with transmit based signaling system

In this research, we developed a pattern-diagram optimization algorithm considering overtaking by express trains in suburban railroads. We first developed a traveling simulation algorithm that takes into consideration various constraints and existence of express trains, and optimized pattern-diagrams in suburban railroad by combining the constructed algorithm and genetic algorithm.

Multi-Objective Day-Ahead Optimal Scheduling of Isolated Microgrid Considering Flexibility

In isolated microgrid, renewable energy sources including photovoltaic and wind power, have the nature of intermittence and variability. Making a reasonable day-ahead generation schedule could improve system ability to cope with uncertainty. Therefore, based on day-ahead generation schedule, flexibility insufficiency rate is proposed considering economy and flexibility. Aiming at the lowest flexibility insufficiency rate and optimal operating cost, a day-ahead generation schedule optimizing model of isolated microgrid is established. Under multi-objective particle swarm optimization, Pareto optimal solution set of the day-ahead generation schedule is found. Simulation results of an isolated microgrid show that, day-ahead generation schedule made with the proposed method can improve ability of power system to cope with uncertainty and reduce economic losses.

A Scheduling Method Based on NSGA2 for Steelmaking and Continuous Casting Production Process

In this paper a new non-dominated sorting genetic algorithm with elite strategy (NSGA2) based production scheduling method is proposed for complex steelmaking and continuous casting production process which is consisted of multiple refining ways. At first, the multi-objective optimization scheduling model is established according to production process and schedule requirement. And then NSGA2 is used to solve the scheduling model. There are multiple Pareto non-inferior solutions from the solving phase, and decision maker may select one of solutions based on own preference, while it is not the most optimal. To fix this problem, the optimal decision making method is put forward, combining the fuzzy membership degree and the variance weighting. Simulation experiments are carried out with the actual industry production data, which shows that the proposed method is practical for industry production.

Multiobjective optimization for carbon market scheduling based on behavior learning

With advances of smart grid, the responsibility of carbon emission reduction can be fairly allocated to each participant in power networks through bidirectional communications. This paper proposes a hierarchical carbon market scheduling model to effectively realize carbon emission reduction. The policy makers in the upper level aim to maximize the effects of carbon emission reduction. They set out appropriate monetary incentives and emission allowances for both customers and generators. Considering restrictions from policy makers, both generators and customers in lower levels seek to minimize their operational costs and payment bills, respectively. To achieve these objectives, a multiobjective problem is formulated by forecasting market trends from a behavior learning model. The simulation results demonstrate that through the proposed approach the renewable penetration increases and the carbon emissions decrease. The benefits for each participant are analyzed as well.

Source-load joint multi-objective optimal scheduling for promoting wind power, photovoltaic accommodation

Improving the grid-regulating ability by coordinating resources is important when dealing with large-scale renewable energy (RE) accommodation contradictions. When considering the status quo of the resource endowment and the market mechanism in China by coordinating the bilateral resources of “source” and “load” at the grid-scheduling level, this study regarded flexible loads as “virtual interactive power plants” to participate in the power balance and reservation based on the scheduling structure of the load aggregatorse. With the objectives of reducing the curtailment, increasing effectiveness, and improving grid economy, it is essential to construct the OPF (Optimal Power Flow)-based source-load multi-objective scheduling model. Moreover, the model was decoupled into a two-stage optimization model that consisted of the “wind-photovoltaic-thermal-load” multi-coordinated optimization and the economic allocation of the cascade hydropower station. The improved multi-objective firefly algorithm (IMOFA) was proposed and adopted for the solving. Finally, by using the IEEE-30 standard system, the study in this paper compared the scheduling schemes of different RE access levels and scenes. The results verified the rationality of the model and the algorithm as well as the operating effectiveness of the source-load joint in improving the economic, social, and environmental benefits.

Distributed cooperative optimal control architecture for AC microgrid with renewable generation and storage

AC microgrid is a promising approach to integrate various distributed generators and energy storage into the power system, and provide renewable and reliable energy to the customers. However, highly intermittent distributed energy sources and varying load demands pose increasing challenges for optimal energy management in an AC microgrid. In this paper, a distributed cooperative optimal control method based on the discrete consistency algorithm is proposed to realize the large-scale penetration of renewable energy in an AC microgrid. The proposed approach is implemented through a multi-agent distributed hierarchical control architecture, which only requires a local communication network to exchange the information. The optimal control is achieved through a three-level control architecture. The third control level aims at multi-objective optimal scheduling, considering the factors such as fuel consumption, pollution in the form of emissions and operational maintenance. The second control level is based on the discrete consistency algorithm that optimizes the frequency and voltage references of droop control and also shares the active and reactive power accurately according to the particular demand. The first control level is responsible for reference tracking of the associated components. Compared with the conventional centralized and decentralized controls, the proposed control method offers increased robustness and flexibility in the AC microgrid control, and only requires limited communication between neighboring agents to realize a global optimization. Finally, the effectiveness of the proposed strategy is verified in a typical AC microgrid model by using the PSCAD/EMTDC software.