Reactive Scheduling Research Articles

A MATHEMATICAL MODEL FOR DYNAMIC PROJECT SCHEDULING PROBLEM AND REACTIVE SCHEDULING IMPLEMENTATION

In today's real-life implementations, projects are executed under uncertainty in a dynamic environment. In addition to resource constraints, the baseline schedule is affected due to the unpredictability of the dynamic environment. Uncertainty-based dynamic events experienced during project execution may change the baseline schedule partially or substantially and require projects' rescheduling. In this study, a mixed-integer linear programming model is proposed for the dynamic resource-constrained project scheduling problem. Three dynamic situation scenarios are solved with the proposed model, including machine breakdown, worker sickness, and electricity power cut. Finally, generated reactive schedules are completed later than the baseline schedule.

Selection of the Optimal Actions for Crashing Processes Duration to Increase the Robustness of Construction Schedules

Both the construction clients and the contractors want their projects delivered on time. Construction schedules, usually tight from the beginning, tend to expire as the progress of works is disturbed by materializing risks. As consequence, the project’s original milestones are delayed. To protect the due date and, at the same time, avoid changes to the logic of work, the manager needs to the project progress and, if delays occur, speed up processes not yet completed. The authors investigate the problem of selecting the optimal set of actions of responding to schedule delays. They put forward a simulation-based method of selecting schedule compression measures (speeding up processes) and determining the best moment to take such actions. The idea is explained using a simple case. The results confirm that it is possible to find an easily implementable schedule crashing mode to answer schedule disturbances. The proposed method enables minimizing the cost of schedule crashing actions and the cost of delays as well as increasing the robustness of the schedule by reducing differences between the actual and the as-planned process starts. It is intended as a decision support tool to help construction managers prepare better reactive schedules. The lowest costs are achieved if the acceleration measures are implemented with some time lag to the occurrence of delays.

An event-based reactive scheduling approach for the Resource Constrained Project Scheduling Problem with unreliable resources

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

Fentanyl analogs on the Swedish webforum flashback: Interest and impact of scheduling

BackgroundSweden regulates new psychoactive substances, including fentanyl analogs, individually. This reactive scheduling procedure enabled the existence of a recreational market for unscheduled fentanyl analogs sold from surface webshops. We measure the interest in 24 named fentanyl analogs and the impact of scheduling. MethodsWe scraped posts in threads on named fentanyl analogs from the Swedish internet forum Flashback.org, 2012–2019. The sample consists of 24 threads with a total of 8761 posts. We construct five measures of interest based on duration of threads, number of posts, and number of distinct posters, and fit a non-seasonal ARMA model to test if there was a change in mean activity after scheduling. ResultsAcross the five measures, there was most interest in acryl fentanyl, butyr fentanyl, and acetyl fentanyl. The number of daily posts was significantly reduced in nine out of 13 threads after scheduling. ConclusionThe scheduling of fentanyl analogs impacted interest on Flashback.org. The biggest effect sizes were from the narcotics scheduling of 2-Me-MAF, acryl, and acetyl fentanyl, while furanyl fentanyl saw the biggest reduction after health scheduling. The reductions were bigger for narcotics scheduling compared to health scheduling.

Tugboat scheduling under ship arrival and tugging process time uncertainty

This study addresses an interesting tugboat scheduling problem considering uncertainty in both container ship arrival and tugging process times for large container ports. The uncertain ship arrival and tugging process times are formulated as a finite set of discrete scenarios that can be generated from historical port traffic data. We deal with the uncertainty by integrating proactive and reactive scheduling strategies such that this study is distinct from most existing studies in the literature. The proactive scheduling strategy considers the expected degree of variability and uncertainty during the execution of a tugboat fleet schedule while the reactive scheduling strategy properly adjusts the initial schedule to cope with unexpected scenarios with minimum recovery cost. A mixed-integer linear programming model for the proposed tugboat scheduling problem is established. For a large-scale problem, an ad-hoc algorithm is designed to generate tugging chains such that the large-scale problem can be tackled effectively. The extensive numerical experiments are finally carried out to demonstrate the practical significances of the models and algorithms developed by this study.

Multisite supply planning for drug products under uncertainty

AbstractIn the pharmaceutical industry, the goal of a supply planner is to make efficient capacity allocation decisions that ensure an uninterrupted supply of drug products to patients and to maintain product inventory levels close to the target stock. This task can be challenging due to the limited availability of manufacturing assets, uncertainties in product demand, fluctuations in production yields, and unplanned site downtimes. It is not uncommon to observe uneven distribution of product inventories with some products carrying excess inventories, while other products may be close to a stockout. Maintaining high stock levels can have economic repercussions due to the risk of expiration of unused products (whereas products facing a stockout can adversely affect the treatment regimen of patients). The network complexity of pharmaceutical supply‐chains coupled with regulatory constraints and siloed planning systems force supply planners to rely on manual (error‐prone) decision‐making processes. Such an approach results in suboptimal capacity allocation and inventory management decisions. In this work, we propose a stochastic optimization methodology for the production scheduling of multiple drug products in lyophilization units across multiple sites. The framework leverages information obtained from historical and forecast data to generate scenarios of uncertain parameters (e.g., yield, demand, and downtimes) that can realize in the future. The optimization model determines a product filling schedule that maintains product stock levels close to targets under diverse scenarios. We show that this approach helps in avoiding reactive scheduling and in maintaining a more robust production plan than deterministic procedures (which ignore uncertainty). Specifically, planning under a stochastic optimization approach reduces the number of scenarios under which backlogs are observed and also reduces the magnitude of the backlogs.

Discrete time reactive scheduling of gasoline blending and product delivery in presence of demand and component uncertainties using graphical genetic algorithm

Gasoline blending is an important downstream operation in the refinery. The operation is susceptible to uncertainties such as fluctuation in component quality, fluctuation in demand, and a combination of both. These problems naturally involve multiple objectives and non-linear terms corresponding to the mixing of the components for which the genetic algorithm-based approach is more suitable compared to traditional mathematical programming. In this study, such graphical genetic algorithm-based reactive scheduling approach is developed which can handle dynamic changes in component quality and demand as an additional layer of decision making over the nominal scheduling. Three industrial-scale examples are solved using the developed approach for both single- and two-objective optimizations while handling the uncertainty of 10% increase in demand and 5% decrease in component quality. In single-objective optimization, the production cost is minimized whereas in two-objective optimization additionally the fluctuation in blending processing rate is minimized.

Deterministic predictive dynamic scheduling for crop-transport co-robots acting as harvesting aids

Manual harvesting of fresh-market crops like strawberries is very labor-intensive. Apart from picking fruits, pickers spend significant amounts of time carrying full trays to a collection station at the edge of the field. Small teams of harvest-aid robots that help large picking crews by transporting empty and full trays can increase harvest efficiency by reducing pickers’ non-productive walking times. However, robot sharing among the crew may introduce non-productive waiting delays between the time a tray becomes full and when a robot arrives to collect it. Reactive robot scheduling cannot eliminate mean waiting times because pickers must wait for a robot to travel the distance from the collection station to them. Predictive scheduling is better suited to this task, because if the time and location that a pickers’ tray will fill are known to the scheduler in advance, a robot can start moving toward that location before the tray becomes full; hence, waiting times due to robot travel can be reduced or eliminated.In this paper, dynamic predictive scheduling was modeled for teams of robots carrying trays during manual harvesting. The times and locations of the tray-transport requests were assumed to be known exactly (deterministic predictions). Near-optimal scheduling was implemented to provide efficiency upper-bounds for any predictive scheduling algorithms that incorporate uncertainty in the predictive requests. Robot-aided harvesting was simulated using manual-harvest data collected from a commercial picking crew. Scheduling performance was studied as a function of the number of robots – for a given crew size – with robot speed as a parameter. Additionally, the effect of the earliness of the availability of the predictions on performance was studied.Experimental results showed that both reactive and predictive scheduling did not improve the mean non-productive time significantly relative to manual harvesting, when only four robots were used. Actually, deploying fewer than four robots led to worse non-productive time. However, introducing five to eight robots decreased mean non-productive time drastically, and when ten or more robots were used, non-productive time was reduced by 64.6% (reactive scheduling) and up to 93.7% (predictive scheduling) with respect to all-manual non-productive time. The efficiency increases were 15% and 24%, respectively. It was also verified that reactive dispatching always performed worse than deterministic predictive scheduling. Also, when the robot-to-picker ratio was larger than approximately 1:3, the waiting time and efficiency plateaued, i.e., did not improve further, regardless of how early the prediction was available to the scheduler. The reason is that the mean waiting time is lower bounded by the sum of mean travel time and tray exchange time, which are both constant. Although the above results represent upper-bounds for performance – since predictions were perfect - they indicate that tray-transport robots acting as harvest aids can increase harvesting efficiency significantly when scheduled properly.

Reactive scheduling approach for solving a realistic flexible job shop scheduling problem

ABSTRACT Reactive Scheduling (RS) and the realistic Flexible Job Shop Scheduling Problem (FJSSP) are of major importance for the implementation of real-world manufacturing systems. The present study proposes a scheduling rules-based surrogate assisted simulation-optimisation approach for solving a combinatorial optimisation problem related to a realistic FJSSP. The proposed approach aims to capture the dynamic nature of the FJSSP and to balance both short-term reactivity facing repetitive perturbations and the overall performance of manufacturing systems. Besides and to enhance the optimisation process, a GA-based computational procedure allows managing the use of a hybrid neuronal surrogate and DES model for the accurate and fast calculation of the fitness function, considering the Makespan minimisation criterion and dealing with rush orders. The approach is applied to a highly automated Flexible robotised Manufacturing System (FMS) integrating different realistic and representative constraints to the classical FJSSP. Computational simulations and comparisons demonstrate that the proposed approach shows competitive performances compared to other resolution models, considering obtained solutions quality and short-term reactivity. The proposed resolution model provides technical tools for future control systems and allows for the practical implementation of customised assembly systems in Industry 4.0, relying on innovative emerging technologies.

A reactive scheduling method for disturbances in aircraft moving assembly line

Aircraft assembly requires a large number of materials from hundreds of suppliers, and the uncertainty in material delivery has a negative impact on the assembly schedule. Existing researches stop short of introducing how to reschedule assembly activities in this context, so this paper addresses a reactive scheduling problem of aircraft moving assembly line with uncertain material delivery, and a bi-objective model is established. To absorb the advantage of machine learning-based method, we present a SVDD-based reactive scheduling method (SVDD-RS). Firstly, the models under different settings of disturbances in material delivery are solved, and the obtained policies are used to train the SVDD classification model in the offline training phase. In the online reactive scheduling phase, the trained SVDD classification model is used to make a preliminary decision for unstarted activities, and exact start-times are further determined by the local forward-looking algorithm. Computational experiments are carried out over practical cases generated from an aircraft assembly line to evaluate the performance of SVDD-RS. The results show that the SVDD classification model can quickly select policies with reasonable accuracy, and SVDD-RS can guarantee a quick response to the disturbance and produce a high-quality solution, compared to other existing reactive scheduling methods.

Multi-Layer Event-Based Vehicle-to-Grid (V2G) Scheduling With Short Term Predictive Capability Within a Modular Aggregator Control Structure

In this work, a novel method of event-based V2G scheduling is devised that is suitable for dynamic real time aggregator control in large scale V2G applications within centrally controlled EV car parks. The method is applicable in deterministic systems where a V2G network provides or receives electricity in reoccurring and predictable patterns (events). The scheduling strategy shown is based on a robust modular high-level aggregator control structure and a proposed communications and data management system. The scheduling consists of three algorithm layers, differentiating between predictive scheduling for in-event periods, smart charging for out-of-event periods and reactive scheduling for ongoing adjustments in real-time to account for uncertainty. The scheduling process is described in terms of its underlying rules for prioritising EVs to be either charged or discharged. It's behaviour is then analysed using a simulated car park of up to one thousand connected EVs for an example application in which a V2G network is used to support nearby electrified rail infrastructure, providing power for train acceleration and accepting power from regenerative braking. The departure or arrival of a train of known type and speed pattern can be regarded as a reoccurring event and its effect on the V2G network is therefore predictable due to train schedules and tracking.

Dynamic hybrid scheduling of crude oil using structure adapted genetic algorithm for uncertainty of tank unavailability

The marine access refineries which supply nearly 15% of the global energy requirement constantly encounter uncertainty such as unit unavailability. This uncertainty often arises due to various factors such as pump failure or leakage in tanks. Crude oil scheduling under such uncertain conditions not only compromises the profitability but also compromises the operability of the plant. The study comprehensively analyses the advantages and disadvantages of using preventive and reactive scheduling approaches to handle a commonly encountered tank unavailability uncertainty. Based on these, a new hybrid approach which combines both the features from the preventive and reactive approach is developed. This study is important for solving scheduling optimization problem when uncertainty, non-linearity and multiple objectives are involved. For this purpose, three large-scale marine access refinery scheduling problems having a time horizon of 7, 7 and 14 days were studied.

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

The main technological barrier in relying solely on renewable energy resources is that the sources such as wind and solar are highly intermittent in availability and result in uncertainty in demand satisfaction. This paper focuses on the integration of these uncertain renewable energy sources along with relatively deterministic energy sources such as reformer based fuel cell and battery. The power mix scenario between these multiple renewable energy sources along with the reformer based fuel cell system, coupled with an energy storage option is envisaged in this paper to ensure undisrupted power supply, to combat the possible intermittent nature of these renewable sources. An appropriate scheduling layer which provides a detailed plan of the optimum contribution of the various available power sources is considered over one week (7 days) duration. A model predictive control (MPC) scheme is deployed at the lower level control layer that receives a measurement of the possible fluctuations or uncertainties in the renewable power sources and maintains a smooth operation of the power generation system through appropriate decisions on generation via the reformer based fuel cell or by exploiting the battery storage, to ensure a delay-free delivery of power to the external load. During real-time operation of the plant, due to the uncertainties in the contribution from solar and wind sources, the power demanded from the fuel cell and the battery is varied accordingly by the MPC layer to meet the overall power demand. The performance of the designed MPC to maintain a smooth delivery of power in both the absence and presence of uncertainties in the renewable energy sources, with and without a reactive feedback between the scheduling and control layers, is illustrated using case studies.

Resource Constrained Project Scheduling With Dynamic Disruption Recovery

Resource-constrained project scheduling is one of the most widely studied research problems. Although a large number of algorithms have been developed for solving these problems, many of them ignored several practical issues such as resource unavailability and disruptions, and a recovery plan for after a disruption. This paper investigates different cost-effective measures for project scheduling problems under resource disruptions, in which the disrupted resources are dynamically recovered as the project progresses. Firstly, a new proactive scheduling technique is proposed to determine the make-span with an emphasis on maximizing the floating resources that can be used as a buffer to handle any future disruption effectively. Secondly, we introduce a bi-objective approach for reactive scheduling if a disruption occurs, in which both the revised make-span and recovery cost are minimized. Both proactive and reactive models are solved using a specially designed multi-method based evolutionary optimization algorithm, with the results obtained showing the benefits of the proposed method in comparison to state-of-the-art algorithms.

Robust predictive–reactive scheduling and its effect on machine disturbance mitigation

Matrix-structured production systems are becoming increasingly appealing for realizing an individualized mass production. An appropriate method for scheduling in such systems under consideration of machine disturbances is necessary due to the increased complexity and degrees of freedom in order to achieve a high and stable performance. A robust predictive–reactive method for scheduling is proposed and the relationship between increased robustness in predictive scheduling and the ability to reschedule is analyzed in this paper. The result will enhance understanding of the utility of robust scheduling methods in order to maintain high logistic performance even when facing machine disturbances.

Development of Reactive Scheduling System Considering Set-up Operations of Human Operators

Development of Reactive Scheduling System Considering Set-up Operations of Human Operators

Proactive and reactive scheduling of the steelmaking and continuous casting process through adaptive robust optimization

The schedule of a steelmaking and continuous casting (SCC) process defines the unit assignments of casts and ladles, and the processing sequence and timing in each unit. These decisions have to meet strict production requirements such as limited storage duration and continuous operation under various disruptions. Uncertain processing time is a common factor that may invalidate the initial schedule. Traditional deterministic reactive scheduling implements corrective measures for every disruption event by either repairing the deterministic schedule or rescheduling from scratch. In this work, we present a novel adaptive robust optimization method for SCC scheduling under uncertainty. Compared to the deterministic scheduling method, adaptive robust proactive scheduling generates an initial schedule immune to most major disruptions eliminating the need for most changes. Adaptive robust hybrid scheduling presents an attractive trade-off between solution conservatism and excessive scheduling modifications.

Maximizing the number of jobs scheduled at their baseline starting times in case of machine failures

We investigate the problem of keeping the maximum number of starting times of a baseline schedule if some machines happen to be out of order when the baseline schedule is to be implemented. If the machines are identical, we show that the problem is polynomially solved when no deadline is imposed on the reactive schedule and is strongly NP-hard otherwise. If the number of unrelated machines is fixed and if no deadline is imposed on the reactive schedule, a polynomial algorithm, based on a state graph, has been developed. We conclude with an open complexity question and some further research directions for this class of problems.

Planowanie przydziału kompetencji odpornego na nieprzewidziane absencje pracowników

In order to deal with unexpected events such as employee absenteeism and/or a demand for personnel that is higher or lower than expected, organizations need to adopt proactive and reactive scheduling strategies to protect the personnel roster and to respond to this operational variability, respectively. In this paper, we discuss a proactive approach that exploits the concept of employee substitutability to improve the flexibility of a personnel shift roster to respond to schedule disruptions. With a view to developing a DSS-driven method dedicated to competence allocation planning robust to unexpected staff absenteeism, we present the concept of the so-called robust employee competence structure. A declarative model of the concept allows to find an employee competence structure robust to a given set of disruptions while guaranteeing an admissible personnel allocation to the assumed set of tasks. Since the problem of designing such a robust structure is NP-hard, another goal of the present study is to propose a sufficient condition the fulfilment of which will guarantee the validity of calculations. Potential applications of the proposed solution are discussed using examples

Integrated Proactive and Reactive Scheduling for Refinery Front-End Crude Movement with Consideration of Unit Maintenance

A typical front-end crude supply process of refineries encompasses many units, such as vessels, port-side storage tanks, long-distance pipeline, refinery-side charging tanks, and crude distillation units. In reality, the performance of these units will inevitably decay; thus, unit maintenance plans should be well integrated with normal crude movement schedules. In this paper, a novel methodology has been proposed to dynamically and simultaneously determine the optimal schedule of crude movement and unit maintenance operations. Specifically, both proactive scheduling for multiple preventive maintenance tasks and reactive scheduling for emergent corrective maintenance tasks are systematically integrated. Accordingly, the developed mixed-integer nonlinear programming (MINLP) model will optimize the operation schedule for the front-end crude supply process via minimizing the total operating cost during the scheduling time horizon. Meanwhile, it can also determine the optimal unit maintenance schedule, which will identify the best opportunities of not only isolating units from process system to perform maintenance but also resuming their services after maintenance. The model can simultaneously handle multiple unit maintenance tasks while satisfying all strict constraints for crude transferring, storing, blending, and processing operations. The efficacy of the developed methodology and the MINLP model has been demonstrated through various case studies.