Reactive Scheduling Research Articles

A baseline-reactive scheduling method for carrier-based aircraft maintenance tasks

Carrier-based aircraft maintenance tasks are conducted in time-critical, resource-constrained, and uncertain environments. Optimizing the scheduling allocation scheme of maintenance personnel and equipment, reasonably responding to uncertainty disturbances, and maintaining a high fleet availability are vital to the combat and training missions of carrier-based aircraft. The maintenance task scheduling problem for carrier-based aircraft is investigated in this study. First, a mathematical model for comprehensive carrier-based aircraft maintenance task scheduling that considers constraints such as maintenance personnel, equipment/shop, space, and parallel capacity is developed. Second, to generate the baseline scheduling scheme, an improved non-dominated sorting genetic algorithm II (I_NSGA-II) with local neighborhood search is proposed for the model optimization solution; I_NSGA-II uses the serial scheduling generation scheme mechanism to generate the time sequence scheduling scheme for maintenance personnel and equipment/workshop of different fleet sizes. Third, to cope with dynamic uncertainty disturbances, two reactive scheduling methods, i.e., complete rescheduling and partial rescheduling, are proposed to perform reactive scheduling corrections to the baseline schedule. Case simulation shows that the established mathematical model is reasonable and practical, and that the proposed I_NSGA-II is superior to the current mainstream algorithms. In addition, the decision maker can select between the two reactive scheduling methods flexibly based on the different forms and scales of disturbance.

Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks

Scheduling under uncertainty is an area of interest in artificial intelligence. We study the problem of Dynamic Controllability (DC) of Disjunctive Temporal Networks with Uncertainty (DTNU), which seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action durations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We present a tree search approach to determine whether or not a DTNU is R-TDC. Moreover, we leverage the learning capability of a Graph Neural Network (GNN) as a heuristic for tree search guidance. Finally, we conduct experiments on a known benchmark on which we show R-TDC to retain significant completeness with regard to DC, while being faster to prove. This results in the tree search processing fifty percent more DTNU problems in R-TDC than the state-of-the-art DC solver does in DC with the same time budget. We also observe that GNN tree search guidance leads to substantial performance gains on benchmarks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.

Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

Fusing Speed Index during Web Page Loading

With conventional web page load metrics (e.g., Page Load Time) being blamed for deviating from actual user experiences, in recent years a more sensible and complex metric called Speed Index (SI) has been widely adopted to measure the user's quality of experience (QoE). In brief, SI indicates how quickly a page is filled up with above-the-fold visible elements (or crucial elements for short). To date, however, SI has been used as a metric for performance evaluation, rather than as an explicit heuristic to improve page loading. To demystify this, we examine the entire loading process of various pages and ascribe such incapability to three-fold fundamental uncertainties in terms of network, browser execution, and viewport size. In this paper, we design SipLoader, an SI-oriented page load scheduler through a novel cumulative reactive scheduling framework. It does not attempt to deal with uncertainties in advance or in one shot, but schedules page loading by "repairing" the anticipated (nearly) SI-optimal scheduling when uncertainties actually occur. This is achieved with a suite of efficient designs that fully exploit the cumulative nature of SI calculation. Evaluations show that SipLoader improves the median SI by 41%, and provides 1.43 times to 1.99 times more benefits than state-of-the-art solutions.

Fusing Speed Index during Web Page Loading

With conventional web page load metrics (e.g., Page Load Time) being blamed for deviating from actual user experiences, in recent years a more sensible and complex metric called Speed Index (SI) has been widely adopted to measure the user's quality of experience (QoE). In brief, SI indicates how quickly a page is filled up with above-the-fold visible elements (or crucial elements for short). To date, however, SI has been used as a metric for performance evaluation, rather than as an explicit heuristic to improve page loading. To demystify this, we examine the entire loading process of various pages and ascribe such incapability to three-fold fundamental uncertainties in terms of network, browser execution, and viewport size. In this paper, we design SipLoader, an SI-oriented page load scheduler through a novel cumulative reactive scheduling framework. It does not attempt to deal with uncertainties in advance or in one shot, but schedules page loading by "repairing" the anticipated (nearly) SI-optimal scheduling when uncertainties actually occur. This is achieved with a suite of efficient designs that fully exploit the cumulative nature of SI calculation. Evaluations show that SipLoader improves the median SI by 41%, and provides 1.43 times to 1.99 times more benefits than state-of-the-art solutions.

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

SummaryThe exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

S-Graph Based Reactive Scheduling with Unexpected Arrivals of New Orders

S-Graph Based Reactive Scheduling with Unexpected Arrivals of New Orders

Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines

Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.

A Multiport Electric Energy Routing Scheme Applied to Battery Energy Storage System

In this paper, the research status of topology and control strategy of energy storage grid-connected system is analyzed, and aiming at the working characteristics of the repurposed battery, a cascade power electronic transformer (CPET) with independent DC output is proposed. The working principle of current fed isolated bidirectional DC-DC converter (CF-IBDC) and cascaded H-bridge (CHB) is analyzed, and the decoupling control strategy is designed. In this paper, a hierarchical control strategy is designed for the repurposed battery energy storage (RBES) grid-connected system based on CPET, which consists of three layers: energy layer, power layer, and state of charge (SOC) layer. The energy layer responds to active and reactive power scheduling instructions, the power layer controls the grid-connected current and tracks the grid voltage, and the SOC layer equates the charged state of repurposed batteries. A 3 MVA/12 kV three-phase grid-connected simulation system was established, and a 1 kW single-phase system experiment platform was designed. The simulation and experimental results can verify the correctness of the theoretical analysis and the feasibility of the control strategy.

DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems

Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies.

Scheduling elective and emergency surgeries at shared operating rooms with emergency uncertainty and waiting time limit

While timely emergency OR access is strictly required in many specialties, this paper provides a highly responsive preventive-reactive scheduling procedure to address emergency arrivals with waiting time limits before and on surgery day with no assumption on the emergency arrival patterns. To improve responsiveness in situations where existing break-in-moments-based scheduling does not perform well, a MIP model is first developed to generate initial schedules by making ex-ante preparations for future possible arrivals with the objective of minimizing the sacrifice in the throughput of elective surgeries. For applicability, two additional heuristics, which are derived from the MIP model but include other scheduling ideas, are also designed for preventive scheduling. Furthermore, as a supplement to preventive scheduling, a corresponding reactive scheduling method is designed to be applied on surgery day. Facing different emergency realizations, it provides effective and satisfactory adjustments on surgery day by scheduling arrived emergencies timely, properly updating the remaining schedule to prepare for subsequent arrivals with consideration of minimizing the adjusting impact on scheduled elective cases. The effectiveness and performances of the proposed procedures and heuristics are evaluated in comparison with traditional preventive and reactive scheduling strategies through simulations and statistical tests. The numerical results show that the methods developed in this study can significantly improve the responsiveness with an acceptable sacrifice in OR utilization and can provide satisfactory schedules for both types of patients. Some scheduling guidelines based on simulation results and the applicability of the proposed scheduless are also discussed at the end of this study.

Reactive Procedure for Robust Project Scheduling under the Activity Disruptions

The baseline schedule may become infeasible when disruptions happen during the execution of project. This will lead to project delays and financial losses. In this paper, we developed a robust reactive optimization model. Our objective is to maximize the robustness of reactive schedule. Moreover, a robust reactive procedure is proposed for baseline schedule under a series of disruptions caused by activity duration. To test the proposed approach, sets of J60 and J90 instances from the Project Scheduling Library (PSPLIB) were used. Meanwhile, we tested the reactive project scheduling with the aim of minimizing makespan and compared the maximal robustness reactive schedule and the minimal makespan reactive schedule. We developed a reactive procedure involving genetic algorithm for reactive scheduling due to its non-deterministic polynomial hard (NP-hard). The results indicated that robust reactive scheduling has the advantage in maintaining the schedule’s ability of tolerating variabilities and reducing the impact of activity disruptions on the makespan. The research results can help project managers develop a robust schedule which can tolerate variabilities in a random environment.

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents.Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical.The proposed architecture has been tested to illustrate its feasibility and practicality.

Modelling and an improved NSGA-II algorithm for sustainable manufacturing systems with energy conservation under environmental uncertainties: a case study

ABSTRACT This study addresses a flexible job shop scheduling problem considering the energy tax regulations. We introduce two strategies as reactive scheduling to deal with two different kinds of uncertainty. The first uncertainty is about the start and processing times of the jobs with a known probability distribution. The second kind is related to machine breakdowns, modification or cancellation of the orders, and receive new orders without any known probability distribution. Two conflict objective functions are considered as minimising tax cost on surplus energy consumption and minimising total cost of jobs lateness based on soft time-windows. A bi-objective mathematical model is developed to formulate the problem. Since the problem is well-known strongly NP-hard, a new solution approach is introduced based on the NSGA-II algorithm to solve the problem in a reasonable computational time. Some test problems based on a real case study are used to evaluate the performance of the proposed solution approach. The result analysis confirms the effectiveness of the proposed algorithm and its superiority comparing with another algorithm based on the classic NSGA-II. Moreover, sensitivity analysis is done on the main parameters to provide proper managerial suggestions based on the obtained Pareto front solutions.

A New Two-Stage Approach to Coordinate Electrical Vehicles for Satisfaction of Grid and Customer Requirements

Recently due to air pollution concerns, a large number of electric vehicles have been integrated into the electric distribution system. However, the uncoordinated charging of this technology can cause different voltage issues. This paper proposes a two-stage optimization approach with active and reactive power control to coordinate electric vehicles with both grid-to-vehicle and vehicle-to-grid capabilities to satisfy both grid requirements and electric vehicle prosumer requirements. The system requirements considered are voltage deviation and unbalance and the electric vehicle prosumer requirements considered are minimization of charging and battery degradation costs. The coordination problem is formulated as an optimization problem, where the first stage objectives are: minimization of voltage unbalance, customer charging and battery degradation costs. The first stage optimization problem is solved using the meta-heuristic optimization algorithm known as particle swarm optimization to obtain an optimized real power schedule for the electric vehicles. The second stage is then solved of which the objective is to minimize the bus voltage deviation and provides the reactive power schedule for electric vehicles. All the analyses were carried out on the IEEE 34 bus distribution system and the study results show that the proposed method allows prosumers to charge at a minimum cost without any grid voltage unbalance factors and under/over voltage problems under different scenarios. Thus, this work can be beneficial for system operators or electric vehicle aggregators to create a day-ahead schedule.

An effective MCTS-based algorithm for minimizing makespan in dynamic flexible job shop scheduling problem

In the past several decades, most of the research methods are designed to solve the static flexible job shop scheduling problem. However, in real production environments, some inevitable dynamic events such as new jobs arrival and machine breakdown may occur frequently. In this paper, we study a dynamic flexible job shop scheduling problem (DFJSP) considering four dynamic events, which are new jobs arrival, machine breakdown, jobs cancellation and change in the processing time of operations. A rescheduling method based on Monte Carlo Tree Search algorithm (MCTS) is designed to solve the proposed DFJSP with the objective of minimizing the makespan. Several optimization techniques such as Rapid Action Value Estimates heuristic and prior knowledge are adopted to enhance the performance of the MCTS-based rescheduling method. The response time to dynamic events is critical in DFJSP but has not been solved very well. To greatly reduce the response time to dynamic events, when dynamic events occur, multiple continuous specified time windows are designed for the proposed method, according to which the corresponding subsequent partial schedule for the remaining unprocessed operations is progressively generated. Some experiments have been conducted to compare the proposed method with the commonly used completely reactive scheduling methods and the GA-based rescheduling method. The experiment results indicate that the proposed method is an efficient and promising method for dynamic scheduling both on solution quality and computation efficiency.

Reactive scheduling of multiple EOSs under cloud uncertainties: Model and algorithms

Most earth observation satellites (EOSs) are low-orbit satellites equipped with optical sensors that cannot see through clouds. Hence, cloud coverage, high dynamics, and cloud uncertainties are important issues in the scheduling of EOSs. The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed. Numerous studies have been conducted on methods for the proactive scheduling of EOSs, including expectation, chance-constrained, and robust optimization models and the relevant solution algorithms. This study focuses on the reactive scheduling of EOSs under cloud uncertainties. First, using an example, we describe the reactive scheduling problem in detail, clarifying its significance and key issues. Considering the two key objectives of observation profits and scheduling stability, we construct a multi-objective optimization mathematical model. Then, we obtain the possible disruptions of EOS scheduling during execution under cloud uncertainties, adopting an event-driven policy for the reactive scheduling. For the different disruptions, different reactive scheduling algorithms are designed. Finally, numerous simulation experiments are conducted to verify the feasibility and effectiveness of the proposed reactive scheduling algorithms. The experimental results show that the reactive scheduling algorithms can both improve observation profits and reduce system perturbations.

Managing Uncertainty and Disruptions in Resource Constrained Project Scheduling Problems: A Real-Time Reactive Approach

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedence-related activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition, resource demands along with resource availability at any stage of a project can also vary due to disruptions, which compel practitioners to re-think existing scheduling methodologies. Consequently, to mitigate the conjoint effect of those uncertainties and/or disruptions, this paper proposes a real-time reactive scheduling approach. To deal with uncertain activity durations, a chance constrained based approach is followed, which is later solved by an advanced meta-heuristic based approaches called IGFBIS and IGFBID. After solving an exhaustive list of stochastic RCPSP instances, this paper proves the efficacy of the proposed approaches, which is proven to be more useful to mitigate uncertainty or disruption while executing real-life projects.

Adaptive Operations of Handling Manipulator toward Automated Reactive Scheduling

Adaptive Operations of Handling Manipulator toward Automated Reactive Scheduling

Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems

Most production environments face random, unexpected events such as machine failure, uncertain processing times, the arrival of new jobs, and cancellation of jobs. For the reduction of the undesirable side effects of an unexpected disruption, the initial schedule needs to be reformed partially or entirely. In this paper, a mathematical model is presented to address the integrated cell formation and cellular rescheduling problems in a cellular manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system. Based on the principle of resistance to change, the reactive model seeks a new solution with the minimum difference from the initial solution. This is realized through a simultaneous minimization of the total completion time and the number of displaced machines. For the investigation of the performance of the proposed model, some numerical examples are solved using the GAMS software. The results demonstrate the ability of the reactive model to obtain solutions resistant to unexpected changes