Optimization-based Scheduling Research Articles

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %–26.3 %, and increases Charging pile revenue.

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

User-defined weight based multi objective task scheduling in cloud using whale optimization algorithm

Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.

Optimization-based Scheduling of Construction Projects with Generalized Precedence Relationships: A Real-Life Case Study

Optimization-based Scheduling of Construction Projects with Generalized Precedence Relationships: A Real-Life Case Study

Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.

Day-Ahead Distributionally Robust Optimization-Based Scheduling for Distribution Systems With Electric Vehicles

In this paper, a day-ahead optimal scheduling strategy for the DSO based on EV aggregation is proposed. Generally, the uncertain parameters of EVs, i.e., arrival and departure times as well as the charging demands, can be effectively modeled by a Gaussian mixture model (GMM) with historical charging records. The empirical data of net load error, i.e., mean values and covariance, can be obtained from historical data, while the explicit probability distribution function may not be fully known. Therefore, a distributionally robust optimization (DRO) model is formulated for the DSO to characterize the net load uncertainty. Then, an EV aggregation-based reservation capacity prearrangement method is proposed to specifically address the net load error. Next, with the reservation capacity from EVs, the DRO model is convexified by transforming into two subproblems with deterministic forms. Finally, simulation and comparison tests are conducted on the 33-bus distribution network to demonstrate that the proposed approach achieves a tradeoff between economic performance and computation efficiency compared with the scenario-based stochastic optimization method and achieves a tradeoff between operation costs and system reliability compared with the chance constraint method.

Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

Multi-Job Intelligent Scheduling With Cross-Device Federated Learning

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this article, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).

Knowledge-Based Optimal Irrigation Scheduling of Agro-Hydrological Systems

Agricultural irrigation consumes about 70% of freshwater globally every year. To improve the water-use efficiency in agricultural irrigation is critical as we move toward water sustainability. An irrigation scheduler determines how much water to irrigate and when to irrigate for an agricultural field. To get a high-resolution irrigation-scheduling solution for a large-scale agricultural field is still an open research problem. In this work, we propose a knowledge-based optimal irrigation-scheduling approach for large-scale agricultural fields that are equipped with center pivot irrigation systems. The proposed scheduler is designed in the framework of model predictive control. The objective of the proposed scheduler is to maximize crop yield while minimizing irrigation water consumption and the associated electricity usage. First, we introduce a structure-preserving model reduction technique to significantly reduce the dimensionality of agro-hydrological systems. Then, based on the reduced model, an optimization-based scheduler is designed. In the design of the scheduler, knowledge from farmers is taken into account to further reduce the computational complexity of the scheduler. The proposed approach explicitly considers both the irrigation time and the irrigation amount as decision variables to keep the crop within the stress-free zone considering the weather uncertainty and heterogeneous soil types for large agricultural fields. The proposed approach is applied to three different scenarios with different soil types, crops, and weather uncertainty. The results show that in all the conditions, the scheduler is capable of keeping the crops stress-free, which results in maximum yield and, at the same time, minimizes water consumption and irrigation events.

Knowledge-based optimal irrigation scheduling of three-dimensional agro-hydrological systems

An agricultural irrigation scheduler determines how much to irrigate and when to irrigate for a field. The accurate and effective scheduler decision for large agricultural fields is still an open research problem. In this work, we address the high dimensionality of the agricultural field and propose a knowledge-based approach to provide optimal irrigation amount and irrigation time for three-dimensional agro-hydrological systems. First, we introduce a structure-preserving model reduction technique to decrease the dimension of the system. Then, based on the reduced model, an optimization-based scheduler is designed. In the design of the scheduler, empirical knowledge from farmers is considered to significantly reduce the computational complexity. The proposed scheduler is designed in the framework of model predictive control. The objective of the proposed scheduler is to maximize crop yield while minimizing irrigation water consumption and the associated electricity usage. The proposed approach is applied to a field to show the effectiveness and superiority of the proposed framework.

Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant

Recent studies have indicated a great potential for applying predictive methodologies to the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. Particularly for Centralized Chiller Plants with Thermal Energy Storage (TES) used for District Cooling, there is a substantial opportunity for cost savings when responding to variations in electricity price and ambient temperature. The present work addresses the problem of closed-loop scheduling of a large-scale chiller plant with TES tank under a Day-Ahead (DA) electricity price program. The main contributions include: (i) formulating the problem for a real large-scale complex system; (ii) comparing different dispatching policies with varying degrees of optimality, constraint satisfaction and operational complexity; and (iii) designing an optimization-based scheduling tool and describing its implementation and results. The proposed Mixed-Integer Linear Programming (MILP) formulation extends existing models by explicitly accounting for the effect of chilled water return temperature on the energy balances, considering real plant features, and performing pre-calculation of bounds on equipment and storage capacity. The study is performed with real process data from the central chiller plant located on the campus of the University of California, Davis. The comparison between different dispatching policies highlights the benefits of adopting an optimization-based operational strategy, indicating a cost reduction of up to 32% compared to other suboptimal policies. The study also demonstrates how additional constraints that reduce operational complexity affect the closed-loop performance of the optimization-based method, serving as a guideline for designing the optimization tool.

Minimizing Coflow Completion Time in Optical Circuit Switched Networks

Nowadays, optical circuit switching is becoming an increasingly favored technology in scaling data center networks for its definitive advantages in data rate, power consumption, and device cost. Concurrently, reducing coflow completion time (CCT) is of great significance for improving application-level performance. However, minimizing CCT in circuit switched networks is totally different from that in traditional packet switched networks due to port constraints and circuit reconfiguration delays. To address this issue, this article proposes Grouped Optimization-based Scheduling (GOS), a CCT minimization algorithm for circuit switched networks integrating circuit and coflow scheduling. We first formalize the CCT minimization problem into a 0-1 programming problem, then relax and solve the problem in 2 steps to obtain the coflow order and flow grouping decisions on each circuit. Thus intra-group reconfiguration delays are saved, and small coflows can be prioritized at the group level. Theoretical analysis proves GOS is a 4-approximation algorithm in average CCT. To reduce computing overheads, we further propose a heuristic approximation algorithm. Extensive simulations show that the heuristic algorithm has satisfactory CCT performance (0.12× Varys, 0.36× Sunflow) as well as high throughput (16.74× Varys, 1.32× Sunflow), and well adapts to a wide range of reconfiguration delays and algorithm decision time.

Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments

Optimal scheduling of workflows in cloud computing environments is an essential element to maximize the utilization of Virtual Machines (VMs). In practice, scheduling of dependent tasks in a workflow requires distributing the tasks to the available VMs on the cloud. This paper introduces a discrete variation of the Distributed Grey Wolf Optimizer (DGWO) for scheduling dependent tasks to VMs. The scheduling process in DGWO is modeled as a minimization problem for two objectives: computation and data transmission costs. DGWO uses the largest order value (LOV) method to convert the continuous candidate solutions produced by DGWO to discrete candidate solutions. DGWO was experimentally tested and compared to well-known optimization-based scheduling algorithms (Particle Swarm Optimization (PSO), Grey Wolf Optimizer). The experimental results suggest that DGWO distributes tasks to VMs faster than the other tested algorithms. Besides, DGWO was compared to PSO and Binary PSO (BPSO) using WorkflowSim and scientific workflows of different sizes. The obtained simulation results suggest that DGWO provides the best makespan compared to the other algorithms.

Discrete Island-Based Cuckoo Search with Highly Disruptive Polynomial Mutation and Opposition-Based Learning Strategy for Scheduling of Workflow Applications in Cloud Environments

The optimization-based scheduling algorithms used for scheduling workflows in cloud computing environments may easily get trapped in local optima, especially in the beginning of their simulation processes because of some limitations in their exploration methods. Moreover, the performance of some optimization-based scheduling algorithms may severely degrade when dealing with medium- or large-size scheduling problems. The Island-based Cuckoo Search with highly disruptive polynomial mutation (iCSPM) algorithm is a parallel version of the Cuckoo Search (CS) algorithm. The iCSPM algorithm incorporates the island model into CS and uses an exploration function based on the highly disruptive polynomial mutation. It has been empirically proven that iCSPM performs better than popular optimization algorithms (e.g., CS and island-based Genetic algorithm). This paper presents a variation of iCSPM called Discrete iCSPM with opposition-based learning strategy (DiCSPM) for scheduling workflows in cloud computing environments based on two objectives: computation and data transmission costs. DiCSPM includes two new features compared to iCSPM. First, it uses the opposition-based learning approach (OBL) in the initialization step at the level of islands, where each island in the island model contains the opposite population of another island. Second, the smallest position value method is used in the DiCSPM algorithm to determine the correct values of the decision variables in the candidate solutions. The proposed algorithm was experimentally evaluated and compared to well-known scheduling algorithms [Best Resource Selection, Particle Swarm Optimization (PSO) and Grey Wolf Optimizer] using two types of workflows: balanced and imbalanced workflows. The overall experimental and statistical results indicate that DiCSPM provides solutions for the scheduling problem of workflows in cloud computing environment faster than the other compared algorithms. Moreover, DiCSPM was evaluated and compared to state-of-the-art algorithms, namely PSO, binary PSO and discrete binary cat swarm optimization using scientific workflows of different sizes using WorkflowSim. The obtained results suggest that DiCSPM provides the best makespan compared to the other algorithms.

A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers

A nested transactive energy (TE) market methodology is presented in this paper for the effective utilization of demand-side flexibility of small-scale residential consumers. The consumers' flexibilities are traded in a local flexibility market to prevent transformer overloading, whereas the demand-side flexibilities are traded in an event-triggered central wholesale demand response market after successive aggregation in the intermediate layers. A two-stage optimization-based scheduling model is presented to optimize the transactive bidding of residential consumers with on-site distributed energy resources and controllable loads. The optimal market methodologies are presented for the integrated TE markets to ensure economic trading for all involved stakeholders. The proposed methodology is numerically validated by simulation studies for different consumer participation levels, and the case studies indicate the efficacy of the proposed methodology for economically feasible procurement of consumer flexibility for transformer overloading and wholesale peak-price events. Results also illustrate that the proposed method offers 2.8-14 times more profits to the participating consumers than the energy-supply incentives according to existing retail tariff structures even considering their thermal discomfort and cycle-aging of storage units for the flexibility support.

Hybrid optimization-based pilot scheduling for reducing pilot contamination in massive MIMO systems

Massive multiple-input multiple-output (MIMO) is an emerging technology used in next-generation cellular networks. The major challenge in the massive MIMO system is the pilot contamination. The contamination of the pilot sequences causes inaccurate channel estimation leading to huge errors in the transmissions. This paper proposes an approach for pilot contamination reduction in massive MIMO systems. In order to reduce the pilot contamination, a pilot scheduling algorithm is devised by proposing an optimization algorithm named Elephant-based Spider Monkey Optimization (ESMO) for scheduling the pilots. The proposed ESMO is designed by combining Elephant Herding Optimization (EHO) into Spider Monkey Optimization (SMO). The pilot scheduling approach employs proposed ESMO and user degradation for scheduling the pilots. Moreover, the optimal pilot scheduling is carried out using the newly devised fitness function that considers achievable rate using various user grouping parameters, such as utility function, and grouping parameter. Thus, the proposed ESMO-based pilot scheduling and fitness function are responsible for initiating optimal pilot scheduling. The performance of the proposed method is compared with the existing methods, and the proposed ESMO outperformed the existing methods with maximal achievable rate value of 39.257[Formula: see text]bps/Hz, and maximal SINR with value 118.75[Formula: see text]dB, respectively.

Combining the advantages of discrete- and continuous-time scheduling models: Part 3. General algorithm

One of the main challenges in applying optimization-based scheduling techniques in process industries stems from the different process characteristics and constraints that need to be taken into account when generating a schedule. For instance, consideration of sequence-dependent changeovers may easily make the resulting optimization model computationally expensive. Accordingly, building upon the recently proposed Discrete-Continuous Algorithm (Lee and Maravelias, 2018), we propose generalized algorithm that enables accurate and fast solution of difficult instances while efficiently handling a wide range of process characteristics. The algorithm combines modeling versatility with computational tractability while guaranteeing the feasibility and accuracy of the final solution. Through a case study inspired by a real-world brewing process, we show that our algorithm provides accurate and high quality solutions to industrial-scale instances in reasonable time.

A RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA Based 5G Wireless Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.

A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading

Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the traditionally passive end-users of electricity to play an active role in the local power balance of the grid. Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility) when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energy-quantity bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and distributed energy resources (DERs) for comfort reservation, minimization of consumers’ inconveniences and degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear bi-level problem as a mixed-integer linear programming (MILP) problem and solved using ‘of the shelf’ commercial software. The effectiveness of the proposed BEMS model is verified via case studies for a residential prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the proposed method for overall financial savings is also validated by comparing its performance with state-of-the-art day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and 22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based methods respectively. Results also indicate that the proposed method offers better economic performance than standard cost minimization models and multi-objective methods for simultaneous minimization of energy cost and user inconveniences.

A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage

Energy storage can address the mismatch of the ratio of heat to electricity between a combined cooling, heating, and power (CCHP) system and its users, and thus, it can significantly improve energy efficiency. However, energy storage also increases the complexity of the operation optimization of the system. Existing heuristic optimization algorithms such as genetic algorithm (GA) and particle swarm optimization can hardly obtain the optimal scheduling scheme. In this paper, a hybrid optimization method that combines the GA and dynamic programming (DP) is proposed. The GA is the main optimization framework and is used to optimize the hourly set points of the power generation unit in a day. In the optimization process, the GA generates a feasible solution set, and calls the DP to calculate the optimal energy storage set points for each solution. The DP defines an hour as a decision step, and enumerates all energy storage states in each decision step. This process loops until the optimal solution is obtained. To reduce the computing time, the DP is implemented as a vectorized code. Case studies are conducted to verify the effectiveness of the proposed method. The results demonstrate that the overall performance using the proposed method increases by 1.92% in summer and by 1.91% in winter compared with that using the traditional GA method. Furthermore, the computing time is acceptable for the scheduling of the energy system. The proposed method can also be applied to the operation optimization of the CCHP system considering the demand side response.