Execution Schedule Research Articles

Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy

Hydropower, characterized by its quick ramping speed and energy buffering capacity, has the ability to respond to the flexibility demand caused by the forecast uncertainty of variable renewable energy (VRE). However, how to determine the flexibility supply of each hydropower station to ensure the reliable execution of the day-ahead scheduling in the intraday operation is a challenging issue for a hydro–wind–solar hybrid system (HWSHS). Due to complex characteristics of cascade hydropower stations, there is a mutual influence between the day-ahead scheduling and its execution. Therefore, flexibility response mode (FRM) is proposed to determine the flexibility supply of each hydropower station according to the flexibility demand of VRE, and a co-optimization model is constructed to optimize the day-ahead scheduling and the parameters of FRM considering the forecast uncertainty of VRE. A HWSHS in Guizhou province, China, is employed as a case study. The results suggest that the FRM can ensure the reliable execution of the day-ahead scheduling in the intraday operation. Compared to a contrast model without FRM, the execution deviation and its duration are decreased by more than 22% and 37%, respectively, in both long-run and out-of-sample analyses.

Dynamic Spatiotemporal Scheduling for Construction Building Projects

For building projects, the manager is responsible for coordinating the work of subcontractors at the construction site. This includes operations, material flows, and storage. In summary, one of their main roles is to ensure smooth team rotation, maintain fluid circulation, and avoid congestion or relaxation on the site. However, traditional tools lack the ability to consider the planning and management of worksite spaces when calculating the execution schedule and critical path. Consequently, three-week planning is usually carried out separately on independent plans, often using spreadsheets. In addition, a construction site is highly dynamic and mobile in nature, and the positioning of resources and workers can change daily. This makes the management of available space even more complex, and effective space management becomes an imperative. To address this challenge, this paper develops visual dynamic artifacts that present different operation types. The methodology and the conceptual framework facilitate the calculation of the Occupancy Rate (OR) that enables construction project managers to create simple yet dynamic spatiotemporal models of the construction schedule. By incorporating factors such as crew turnover and occupancy evolution, managers can simplify the calculation process and effectively optimize construction work by utilizing site occupancy rates. In summary, this paper presents the Dynamic Model of the Occupancy Rate Schedule (DMORS), a methodology developed through design science. This model utilizes created artifacts representing various operation types to ensure accurate calculations of dynamic occupancy by floor and sector in a site. Consequently, it enables the construction of a more realistic schedule based on critical space ideologies. The DMORS enables managers to use the OR for different floors and sectors of a site, allowing for better space management. A proof of concept demonstrates that this tool can enhance the efficiency and productivity of construction projects by optimizing crew schedules and resource allocation based on site OR.

Optimized Dynamic Service Placement for Enhanced Scheduling in Fog-Edge Computing Environments

The traditional cloud computing model struggles to efficiently handle the vast number of Internet of Things (IoT) services due to its centralized nature and physical distance from end-users. In contrast, edge and fog computing have emerged as promising solutions for supporting latency-sensitive IoT applications by distributing computational resources closer to the data source. However, these technologies are limited by their size and computational capacities, making optimal service placement a critical challenge. This paper addresses this challenge by introducing a dynamic and distributed service placement policy tailored for edge and fog environments. By leveraging the inherent advantages of proximity in fog and edge nodes, the proposed policy seeks to enhance service delivery efficiency, reduce latency, and improve resource utilization. The proposed method focuses on optimizing the placement of high-demand services closer to the data generation sources to enhance scheduling efficiency in fog computing environments. Our method is divided into three interconnected modules. The first module is the service type estimator, which is responsible for distributing services to appropriate nodes. Here, we use the Political Optimizer (PO) as a new metaheuristic algorithm for deploying IoT services. The second module is service dependency estimator, which manages service dependencies. Here, we load dependent services near the data using a path matrix based on the Warshall algorithm. Finally, the third module is resource demand scheduling, which estimates resource demand to facilitate optimal scheduling. Here, we use a popularity-based policy to manage resource demand and service execution scheduling. Implementation results demonstrate significant improvements over existing state-of-the-art policies, highlighting the efficacy of the proposed policy in enhancing service delivery within fog-edge computing frameworks.

On the Importance of the Execution Schedule for Bayesian Inference

On the Importance of the Execution Schedule for Bayesian Inference

Analysis of the local economic project, future Route

The objective of this research was to analyze the implementation of an economic project for local development, which aims to contribute to the introduction of Alternative Energy outside the national electrical interconnection with the use of renewable sources, as well as, use Computer and Communications Technologies in the different sectors of society through scientific-technical services with high added value. To fulfill the research objective, four stages were developed: Characterization, analysis of the environment, economic viability and programming of activities. The methods and techniques used to meet the proposed objective were: study of the environment and the feasibility of the project, direct observation and interviews. Among the main results are the determination of the net flow of benefits and feasibility, with an Internal Rate of Return of 15.2% and a recovery period of 1.13 years, demonstrating the feasibility of the project, which subsequently allowed us to propose the execution schedule of theeconomic local development project Route Future.

SA-Based QoS Aware Workflow Scheduling of Collaborative Tasks in Grid Computing

Scheduling workflow tasks in grid computing is a complex process, especially if it is associated with satisfying the user's requirements to complete tasks within a specified time, with lowest possible cost. This paper presents a proposed Simulated Annealing (SA) based Grid Workflow Tasks Scheduling Approach (SA-GWTSA) that takes into account users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time is within the time constraints (deadline) imposed by the user. In SA-GWTSA, the workflow tasks, which are modeled as a DAG, are divided into task divisions, each of which consists of a set of sequential tasks. Then, the optimal sub-schedules of all task divisions are computed applying SA algorithm, and used to obtain the execution schedule of the entire workflow. In the proposed algorithm, the sub-schedule of each branch division is represented by a vector, in which each element holds the ID of the service provider chosen from a list of service providers capable of executing the corresponding task in the branch. The algorithm uses a fitness function that is formulated as a multi-objective function of time and cost, which gives users the ability to determine their requirements of time against cost, by changing the weighting coefficients in the objective function. The paper also exhibits the experimental results of assessing the performance of SA-GWTSA with workflows samples of different sizes, compared to different scheduling algorithms: Greedy-Time, Greedy-Cost, and Modified Greedy-Cost.

A service-oriented orchestration and planning tool for plug and produce manufacturing: a deformable object handling supervision paradigm

ABSTRACT Recurrent production changes due to shortened product lifecycles besides the increased complexity, that the industry 4.0 paradigm brings, highlight the need for tools that are able to seamlessly orchestrate intelligent resource agents and guarantee rapid commissioning times. This paper presents a complete package for the creation, debugging, editing, management and execution of complex work schedules of robotic systems, named Workcell Controller. The controller is capable of simultaneously orchestrating multiple resources and modules or managing errors based on the real-time shopfloor’s state. For greater applicability and compatibility, it is completely integrated with the ROS framework, meaning that standard ROS interfaces, functionalities and practices can be deployed. An intuitive web-based graphical user interface is designed for offering high usability even for novice users during programming and execution phases. The proposed package has been deployed and tested in two use cases inspired from the composites industry, demonstrating the controller’s applicability, usability and short ramp up times.

Accelerating Patch Validation for Program Repair With Interception-Based Execution Scheduling

Accelerating Patch Validation for Program Repair With Interception-Based Execution Scheduling

GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

SimuQ: A Framework for Programming Quantum Hamiltonian Simulation with Analog Compilation

Quantum Hamiltonian simulation, which simulates the evolution of quantum systems and probes quantum phenomena, is one of the most promising applications of quantum computing. Recent experimental results suggest that Hamiltonian-oriented analog quantum simulation would be advantageous over circuit-oriented digital quantum simulation in the Noisy Intermediate-Scale Quantum (NISQ) machine era. However, programming analog quantum simulators is much more challenging due to the lack of a unified interface between hardware and software. In this paper, we design and implement SimuQ, the first framework for quantum Hamiltonian simulation that supports Hamiltonian programming and pulse-level compilation to heterogeneous analog quantum simulators. Specifically, in SimuQ, front-end users specify the target quantum system with Hamiltonian Modeling Language, and the Hamiltonian-level programmability of analog quantum simulators is specified through a new abstraction called the abstract analog instruction set (AAIS) and programmed in AAIS Specification Language by hardware providers. Through a solver-based compilation, SimuQ generates executable pulse schedules for real devices to simulate the evolution of desired quantum systems, which is demonstrated on superconducting (IBM), neutral-atom (QuEra), and trapped-ion (IonQ) quantum devices. Moreover, we demonstrate the advantages of exposing the Hamiltonian-level programmability of devices with native operations or interaction-based gates and establish a small benchmark of quantum simulation to evaluate SimuQ's compiler with the above analog quantum simulators.

Receding Horizon Re-Ordering of Multi-Agent Execution Schedules

Receding Horizon Re-Ordering of Multi-Agent Execution Schedules

Hybrid traffic scheduling in time‐sensitive networking for the support of automotive applications

AbstractTime‐sensitive networking (TSN) is considered one of the most promising solutions to address real‐time scheduling in in‐vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time‐aware multi‐cyclicqueuing and forwarding scheduling model, that integrates the no‐wait enabled time‐aware shaper and multi‐cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

A Design Flow for Scheduling Spiking Deep Convolutional Neural Networks on Heterogeneous Neuromorphic System-on-Chip

Neuromorphic systems-on-chip (NSoCs) integrate CPU cores and neuromorphic hardware accelerators on the same chip. These platforms can execute spiking deep convolutional neural networks (SDCNNs) with a low energy footprint. Modern NSoCs are heterogeneous in terms of their computing, communication, and storage resources. This makes scheduling SDCNN operations a combinatorial problem of exploring an exponentially-large state space in determining mapping, ordering, and timing of operations to achieve a target hardware performance, e.g., throughput. We propose a systematic design flow to schedule SDCNNs on an NSoC. Our scheduler, called SMART ( S DCNN MA pping, Orde R ing, and T iming), branches the combinatorial optimization problem into computationally-relaxed sub-problems that generate fast solutions without significantly compromising the solution quality. SMART improves performance by efficiently incorporating the heterogeneity in computing, communication, and storage resources. SMART operates in four steps. First, it creates a self-timed execution schedule to map operations to compute resources, maximizing throughput. Second, it uses an optimization strategy to distribute activation and synaptic weights to storage resources, minimizing data communication-related overhead. Third, it constructs an inter-processor communication (IPC) graph with a transaction order for its communication actors. This transaction order is created using a transaction partial order algorithm, which minimizes contention on the shared communication resources. Finally, it schedules this IPC graph to hardware by overlapping communication with the computation, and leveraging operation, pipeline, and batch parallelism. We evaluate SMART using 10 representative image, object, and language-based SDCNNs. Results show that SMART increases throughput by an average 23%, compared to a state-of-the-art scheduler. SMART is implemented entirely in software as a compiler extension. It doesn’t require any change in a neuromorphic hardware or its interface to CPUs. It improves throughput with only a marginal increase in the compilation time. SMART is released under the open-source MIT licensing at https://github.com/drexel-DISCO/SMART to foster future research.

Joint maintenance and spare-parts inventory models: a review and discussion of practical stock-keeping rules

Abstract Accepted by: M. Zied Babai It is natural to coordinate spare-parts inventory planning and maintenance. However, work in the former area often neglects part utilization, and work in the latter neglects the fact that effective execution of maintenance schedules is conditioned to the availability of the necessary spare parts. This paper is a call for further integration between the two areas, and to that end, we review the literature on mathematical modelling and analysis of inventory-maintenance-planning. We are not the first to address this issue (though we take a fresh perspective to the problem), but we are the first to complement such review with a discussion of simple stock keeping rules that may be used effectively in practice. We identify a growing gap between modelling and application, between theory and practice, which justifies the presentation of these simple stock keeping rules for the joint planning of inventory and maintenance. Thus, our work should be of interest not only to researchers who are looking for promising avenues for future research but also to practitioners who are seeking to improve inventory-maintenance operations.

Consistency Constraints for Mapping Dataflow Graphs to Hybrid Dataflow/von Neumann Architectures

Dataflow process networks (DPNs) provide a convenient model of computation that is often used to model system behavior in model-based designs. With fixed sets of nodes, they are also used as dataflow graphs as an intermediate program representation by compilers to uncover instruction-level parallelism of sequential programs. Many recent processor architectures, which are still von Neumann architectures, also use dataflow computing to increase their exploitation of instruction-level parallelism by exposing their datapaths so that the compiler can take care of the allocation of processing units (PUs), the execution schedules of instructions on the PUs, and the communication of intermediate values between PUs. If the communication paths are buffered, these architectures can be abstracted into a DPN architecture whose PUs and interconnection network are DPN nodes. In this article, we introduce a DPN abstraction of hybrid dataflow/von Neumann architectures and consider the mapping of the nodes of a given dataflow graph to the PUs of such a DPN architecture such that there are no conflicts due to the mapping of different nodes to the same PU. We express the allocation and scheduling constraints in terms of propositional logic for the original dataflow graph and for a modified version of the dataflow graph that simplifies the constraints by introducing levels using copy nodes, such that all nodes receive inputs only from nodes of the previous level. We also formulate equisatisfiable SMT constraints using integer variables to reason directly about the parallel runtime. On this basis, we further present alternative SAT constraints that explicitly encode concurrency, and discuss variants of the constraints for a better understanding of the same.

The Impact of Considering State-of-Charge-Dependent Maximum Charging Powers on the Optimal Electric Vehicle Charging Scheduling

Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.

COAC: Cross-Layer Optimization of Accelerator Configurability for Efficient CNN Processing

To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to efficiently deploy such networks on custom processor architectures for resource-scarce edge devices. Existing mapping exploration frameworks enable searching for the optimal execution schedules or hardware mappings of individual network layers, by optimizing each layer’s spatial (dataflow parallelization) and temporal unrolling (TU, execution order). However, these tools fail to take into account the overhead of supporting different unrolling schemes within a common hardware architecture. Using a fixed unrolling scheme across all layers is also not ideal, as this misses significant opportunities for energy and latency savings from optimizing the mapping of diverse layer types. A balanced approach assesses the right amount of mapping flexibility needed across target neural networks, while taking into account the overhead to support multiple unrollings. This article, therefore, presents cross-layer optimization of accelerator configurability (COAC), a cross-layer design space exploration and mapping framework to optimize the flexibility of neural processing architectures by balancing configurability overhead against resulting energy and latency savings for end-to-end inference. COAC does not only provide a systematical analysis of the architectural overhead in function of the supported spatial unrollings (SUs), but also builds an automated flow to find the best unrolling combination(s) for efficient end-to-end inference with limited hardware overhead. Results demonstrate that architectures with carefully optimized flexibility can achieve up to 38% energy-delay-product (EDP) savings for a set of six neural networks at the expense of a relative area increase of 9.5%.

Construction planning and scheduling of a precast house extension using a multi-objective genetic algorithm and 4D building information modelling

Background: As a result of the rising costs of housing in Kuwait, several families find it increasingly challenging to purchase new homes. Typically, Kuwaitis carry out extensions of the existing housing spaces they possess, to accommodate their growing families. In the management and execution of house extension projects, construction planning and scheduling are complex albeit critical tasks. Building information modeling (BIM) and optimization techniques have become crucial tools for improving these two critical processes. Methods: This study aimed to integrate 4D BIM and multi- objective optimization using a genetic algorithm (MOGA) for construction planning and scheduling of a precast house extension. A case study was undertaken for a two-storey family house in Kuwait, which has been extended by two additional floors using the precast method. The extension of the house has its own foundation and support, with a design that adheres to Le Corbusier's five points of architecture. The Autodesk Revit software was used to generate a three-dimensional architectural model of the house extension. Results and Discussion: After running the MOGA based on the developed schedule, optimal results were obtained as a Pareto front with 70 combinations across workers' cost and construction time. The preferred schedule was selected and fed into Autodesk Navisworks to generate a 4D BIM model. Navisworks was used to simulate the house extension, accompanied by its scheduling information. Conclusion: The amalgamation of a multi-objective algorithm with 4D BIM may be employed to efficaciously plan and schedule building projects, allowing for quality decision-making.

An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment

Abstract Practical manufacturing system operates in highly dynamic and uncertain environments, where stochastic disturbances disrupt the execution of the production schedule as originally developed. Previous dynamic scheduling mainly focuses on the constructing predictive models for machine unavailability, with little studies on the adaptive and self-learning capacities for changing scheduling environments. Therefore, a digital twin (DT) driven scheduling with a dynamic feedback mechanism is proposed, in which a reinforcement learning (RL) based adaptive scheduling is developed in DT to make corrective decisions for the disturbances during production runs. In the proposed architecture, the happening disturbance is first detected in the virtual layer by the status continuously updating in accordance with the physical workshop. Furthermore, the reschedule triggering condition is determined in real-time through the calculation of the progress deviations resulting from disturbances. For the scheduling approach, the distributed RL (DRL) based adaptive scheduling method is built to perceive the dynamic production status from virtual environment and implement corrective strategies to hedge against the occurred disturbances. Finally, the proposed method is verified by a practical job shop case and the corresponding DT system is developed to show the effectiveness and advantages after a practical implementation.

A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' deadlines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Besides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.