Proactive Scheduling Research Articles

Proactive scheduling for mmWave Wireless LANs

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.

Resiliency-informed optimal scheduling of smart distribution network with urban distributed photovoltaic: A stochastic P-robust optimization

An increased worldwide emphasis on resilience has resulted in a greater concentration on the distribution networks' resiliency. Therefore, the conventional approach to advance scheduling for power distribution networks is no longer sufficient, thereby requiring the creation of resilient scheduling approaches. This paper examines a proactive day-ahead scheduling method for distribution networks that are linked with significant renewable energy sources. The suggested approach employs a probabilistic scenario-generating strategy to quantify the uncertainties related to wind speed, solar irradiation, catastrophe duration, and load fluctuations. This study utilizes a hybrid stochastic p-robust optimization technique to enhance the analysis and provide robust strategies against risks. The present methodology employs a regret-based metric to quantify the adverse impacts of uncertainty inside the proactive scheduling procedure. The suggested methodology is verified using an IEEE 33-bus test system that includes significant distributed photovoltaic sources. The findings indicate a significant decrease of 43% in the highest level of regret, accompanied by a relatively limited rise of 1.22% in the operational expenses of the distribution network. This framework efficiently manages natural occurrences in the day-ahead scheduling process, making it a very promising method for improving the robustness of power distribution networks.

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

AbstractA hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi‐agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance‐related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q‐Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem‐solving ability on test instances different from the training set.

A resilient scheduling framework for multi-robot multi-station welding flow shop scheduling against robot failures

With the development of intelligent manufacturing, robots are being increasingly applied in manufacturing systems due to their high flexibility. To avoid production disruptions caused by robot failures, higher requirements are imposed on the resilience of systems, specifically in terms of resistance, response, and recovery capabilities. In response to this, this paper investigates the resilient scheduling framework for multi-robot multi-station welding flow shop, thereby endowing and enhancing the resilience of the system. Within the resilient scheduling framework, a proactive scheduling method maximizing resistance capability is firstly proposed based on an improved NSGA-III with variable neighborhood search. Secondly, to improve the response and recovery capabilities of the system, a recovery scheduling method is presented. Therein, an adaptive trigger policy based on deep reinforcement learning is introduced to enhance the rapid response capability for disturbances, while the recovery optimization grants the system the ability to recover its performance that has been degraded due to the impact of disturbances. Finally, through simulation experiments and case study, it is verified that the proposed algorithms and framework possess superior performance of multi-objective optimization, which can endow the multi-robot multi-station welding flow shop with resilience to against uncertain robot failures.

A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios.

HPS: A Hybrid Proactive Scheduler with adaptive channel selection for industrial 6TiSCH networks

The Industrial Internet of Things (IIoT) plays a vital role in Industry 4.0, thanks to advanced wireless protocols like IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) and their incorporation in the IPv6 stack via 6TiSCH. These protocols rely on carefully constructed schedules generated by a Scheduling Function (SF). The Minimal Scheduling Function (MSF) serves as the standard SF established by the 6TiSCH WG, prescribing the management and adaptation of the communication schedule based on traffic patterns. However, MSF has a notable drawback in adapting to traffic variability and burstiness and fails to account for link quality when updating TSCH cells. To address these limitations, this paper introduces the Hybrid Proactive Scheduler with Adaptive Channel Selection (HPS). HPS calculates cell requirements by considering resource utilization, ETX (Expected Transmission Count), and buffer allocation. It incorporates a fast response mechanism for burst traffic, implemented through an autonomous scheduling mechanism. Additionally, HPS utilizes a whitelisting mechanism to select optimal communication channels. The proposed scheme is implemented in Contiki-ng and evaluated in both simulated environments and real-world testbed. Obtained results demonstrate the potential of HPS to significantly enhance reliability and reduce delays compared to state-of-the-art SFs. Specifically, HPS consistently demonstrates reliability levels surpassing 90% across diverse scenarios accompanied with a noticeable gain in latency and power consumption of approximately 70% and 25%, respectively. These findings highlight the effectiveness of HPS in improving network performance and addressing the limitations of existing SFs.

Proactive Scheduling of Mixed Energy Resources At Different Grid Levels

Proactive Scheduling of Mixed Energy Resources At Different Grid Levels

Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities.

A branch-and-bound algorithm for the proactive resource-constrained project scheduling problem with a robustness maximization objective

This paper presents a branch-and-bound (B&B) algorithm for the proactive scheduling problem, which incorporates a degree of anticipated variability in practical projects. First, the resource-constrained proactive scheduling model is formulated. Second, solving the resource-unconstrained version of the problem with an adapted Fulkerson algorithm provides an upper bound solution. Then, the subproblems of the model are derived by introducing additional precedence relationships to resolve the resource conflict in the upper bound solution. Third, we solve the resource-constrained proactive scheduling problem with the proposed B&B algorithm. The resource conflicts found in the upper bound solution are resolved by adding additional precedence relationships. According to the problem analysis, two dominance rules are proposed for additional node fathoming with the aim of improving the algorithm. Fourth, a genetic algorithm is designed to provide an effective heuristic method for solving the research problem. Finally, we conduct an extensive computational experiment on 6,000 randomly generated instances. The obtained results show that the B&B algorithm has advantages over CPLEX and the genetic algorithm. In addition, the impacts of the dominance rules and the three key parameters on the computational time and project robustness are analysed.

Machine Learning and Statistical Test–Based Culvert Condition Impact Factor Analysis

For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

Proactive scheduling for steel plants with unrelated parallel machines and time uncertainty

This paper deals with the steel plant proactive scheduling problem with unrelated parallel machines and time uncertainty to enhance the rationality of machine assignment and processing path, improve the schedule robustness and guarantee capability of energy. First, a scheduling model is formulated with the objectives of average process time, matching degree and oxygen consumption fluctuation. Then, an improved preference-based NSGA-II is designed to solve the problem. The algorithm introduces the preference information into the optimization process and designs an individual sorting method based on the technique for order preference by similarity to ideal solution (TOPSIS) to guide the evolution direction. Besides, to improve the local search ability, variable neighborhood search is incorporated to generate new solutions with high quality. Computational experiments prove that the algorithm can effectively address the proactive scheduling problem. The sensitivity analysis and results of different scheduling models verify the robustness and superiority in machine matching and energy synergy of the model.

A Simulation-Assisted Proactive Scheduling Method for Secure Microgrid Formation Under Static and Transient Islanding Constraints

Microgrids (MGs) provide a promising solution to ensure the constant power supply under extreme conditions due to their islanding ability. However, unintentional islanding caused by emergencies may trigger significant a power imbalance in MGs, leading to unsuccessful MG formation in the event that static or transient operational constraints are violated. To address this challenge, we propose a proactive distribution system scheduling method to continue serving critical loads in emergencies by forming reliable MGs. The power flow on the lines is restricted prior to interruption to mitigate the power imbalance in MGs when equipment failures occur. Both static and transient constraints are incorporated into the proposed model to ensure the adequacy of MGs in supplying demands and transient security during the islanding transition. Note that the inclusion of nonlinear transient constraints makes it difficult to solve the model directly. Thus, a two-level simulation-assisted solution approach is presented, where simulation level sends the maximum allowable power imbalance in the MGs to optimization level to determine optimal scheduling schemes. By interfacing with a transient simulation of MGs, the frequency dynamics are exactly incorporated into the constraints. Numerical results validate the effectiveness of the proposed method in secure islanding transition and MG survivability improvement.

Dynamic Resilience Region-Based Proactive Scheduling for Enhancing the Power System Resilience

Dynamic Resilience Region-Based Proactive Scheduling for Enhancing the Power System Resilience

Proactive berth scheduling with data‐driven buffer time in container terminals

AbstractGlobal shipment volumes have been increasing due to changes in the business environment of e‐commerce and manufacturing. Consequently, container vessels carry more cargo for international trade, increasing uncertainties in terminal management. Terminal operators manage terminals by establishing a proactive schedule that responds to disruptions such as vessel delays, and the introduction of buffer time is a representative proactive strategy. In this study, by analyzing historical delay data with machine learning, we propose data‐driven buffer times to consider the heterogeneous arrival uncertainty of vessels. Thus, we proactive scheduling with data‐driven buffer times according to the desired robustness levels. This is a novel study on berth scheduling that applies data mining approaches to improve operations research techniques. Numerical experiments were conducted on the berth scheduling with time‐invariant quay crane assignment using real‐life data to validate the effectiveness of the proposed method. These experimental results revealed that applying the data‐driven buffer time could effectively reduce the cost incurred at the terminal by balancing baseline and recovery costs. In addition, our proposed methodology ensured the quality of the solution compared with a stochastic method and reduced the computational burden of a stochastic problem by using the data‐driven buffer times obtained prior to the solution construction. Therefore, the proposed method can be introduced into terminal operations to overcome the deficiencies of traditional approaches in terms of academic perspective.

Research on the scheduling method of ground resource under uncertain arrival time

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

Proactive Scheduling for Zero-Energy Device Networks With Fast Uplink Grant

To address the inefficiency in terms of resource utilization of grant-free (GF) protocols, the fast uplink (FU) grant medium-access protocol has been proposed. In this work, we design a proactive wireless power transfer (WPT) framework for FU grant zero-energy massive machine-type communication. Specifically, zero-energy devices (ZEDs) first harvest energy during the WPT phase and then transmit data based on the FU grant protocol. Moreover, a multi-arm bandit traffic prediction scheme is adopted. Then, considering that due to the FU grant protocol ZEDs may harvest energy for several WPT rounds prior to data transmission, we extract the ZEDs’ outage probability during uplink. Based on the derived expressions, a chance-constrained optimization problem is formulated, which minimizes the energy consumption for WPT, while guaranteeing an acceptable outage probability for the ZEDs during data transmission. Simulations validate the theoretical analysis and illustrate that the proposed scheme outperforms GF access.

Multi-Stage Proactive Scheduling of Strategic DISCOs in Mutual Interaction With Cloud Energy Storage and Deferrable Loads

Unexpected events have underscored the need for proactive preparedness in local energy systems, especially strategic distribution companies (DISCOs). The state-of-the-art methods to take proactive approaches in DISCOs are to use battery energy storage systems (BESS). However, there is a lack of established approaches that include the inter-dependencies of techno-socio economic features. To overcome this limitation, this paper constitutes a multi-stage resilience-promoting proactive strategy with linear decisions to enhance the strategic DISCOs preparedness level to deal with unscheduled islanding operation under uncertainties. In particular, this strategy provides operational solutions to survive DISCOs under stressful conditions by incorporating the economic operation of cloud energy storage (CES), which manages many BESS, and correlated demand response (DR) programs in resiliency issues. To this end, the lexicography algorithm is employed to prioritize different objectives based on the pre-avoidance, avoidance, survival, and post-disaster stages, which captures the BESS' energy level as well as socio-economic features of DISCO and CES. The effectiveness of the proposed strategy is demonstrated both analytically and empirically. At first, the features of the proposed strategy are examined in responding to islanding state using a set of numerical studies conducted on the IEEE 69-bus test system in GAMS software. After that, insightful analyses are performed via DIgSILENT PowerFactory to verify the feasibility of the proposed strategy.

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

In modern complicated and changing manufacturing environments, unforeseen dynamic events such as machine breakdown or unexpected job arrival make required production resources unpredictable. The scheduling scheme is desired to maintain high stability in dynamic manufacturing environments. To cope with the classic disturbance of machine breakdown, a robust pro-active scheduling scheme is proposed by inserting the repair time into a disjunctive graph for reinforcement learning (IRDRL) in this paper. Firstly, a new mathematical model is developed to predict the machine fault which is assumed to be determined by service time and bearing load. Secondly, a disjunctive graph with breakdown information is designed to express the dynamic scheduling status. Then, an online scheduling framework is built based on the well-trained model through the proximal policy optimization (PPO) algorithm. Finally, compared with the classical methods such as the right-shift strategy and static model of reinforcement learning (RL), the proposed robust pro-active scheduling scheme is verified with high robustness, stability, and short running time.

Data-Driven Model Predictive Control-Based Proactive Scheduling for Commercial Microgrid Considering Anomaly Detection

One of the challenging tasks for commercial microgrids with distributed generation is to ensure critical loads resilience during high-impact, low-frequency extreme events. This article proposes a data-driven model predictive control-based proactive scheduling strategy for microgrids subject to extreme weather events. The goal is to maximize the utility value of the building by realizing the difference between the comfort level of critical loads and the total operating cost of the commercial microgrid during the planning horizon. A two-layer framework is proposed to solve the considered problem. First, the aggregate demand load measured by smart meters is processed using a combined long short-term memory and fuzzy neural network algorithm to eliminate anomaly data. Then, the obtained real data are applied through the deep-Q network method to seek appropriate proactive scheduling solutions. However, the rewards obtained from the deep-Q network method are random because of the uncertainties in extreme events. Therefore, robust optimization namely, robust deep reinforcement learning, is incorporated to enhance the robustness of the solutions. The proposed framework ensures the optimality and feasibility of the commercial microgrid's proactive scheduling strategy within the uncertainties in extreme events and the different intensity levels of anomalous data. Extensive simulation results prove the effectiveness of the proposed optimization model and algorithm for enhancing commercial microgrid resilience in extreme events. Furthermore, it also shows good ability in detecting smart meters anomalous data, with a stable convergence value of the performance index, to secure commercial microgrid smart meter data.

Research on Proactive Scheduling Theory and Method Enabled by 5G Technology for Intelligent Discrete Manufacturing Shop

Research on Proactive Scheduling Theory and Method Enabled by 5G Technology for Intelligent Discrete Manufacturing Shop