Proactive Reactive Scheduling Research Articles

Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies.

A distributed permutation flow-shop considering sustainability criteria and real-time scheduling

Recent advancements in production scheduling have arisen in response to the need for adaptation in dynamic environments. This paper addresses the challenge of real-time scheduling within the context of sustainable production. We redefine the sustainable distributed permutation flow-shop scheduling problem using an online mixed-integer programming model. The proposed model prioritizes minimizing makespan while simultaneously constraining energy consumption, reducing the number of lost working days and increasing job opportunities within permissible limits. Our approach considers machines operating in different modes, ranging from manual to automatic, and employs two real-time scheduling strategies: predictive-reactive and proactive-reactive scheduling. We evaluate two rescheduling policies: continuous and event-driven. To demonstrate the model's applicability, we present a case study in auto workpiece production. We manage model complexity through various reformulations and heuristics, such as Lagrangian relaxation and Benders decomposition for initial optimization as well as four problem-specific heuristics for real-time considerations. For solving large-scale instances, we employ simulated annealing and tabu search metaheuristic algorithms. Our findings underscore the benefits of the predictive-reactive scheduling strategy and the efficiency of the event-driven rescheduling policy. By addressing dynamic scheduling challenges and integrating sustainability criteria, this study contributes valuable insights into real-time scheduling and sustainable production.

A Novel Proactive–Reactive Scheduling Approach for the Quay Crane Scheduling Problem: A VUCA Perspective

As the port operations are becoming volatile and uncertain, container handling in the port terminals is becoming more complex due to the rising customer demands. Further, scheduling interruptions create ambiguity in real-time operations. Therefore, the present study focused on the container terminal’s ability to schedule and mitigate real-time probabilistic fluctuations. This article proposes a novel proactive–reactive scheduling approach to solve the quay crane (QC) scheduling problem in the volatility, uncertainty, complexity, and ambiguity world, using equipment reliability functions. The proposed approach is based on a novel reliability assessment formula based on practical facts, including the reliability probabilistic distribution of QC and the container workload variability. Moreover, a proactive iterated local search algorithm is proposed and incorporated with the reliability formula to solve the QC scheduling under fluctuations. The efficiency of the algorithm is illustrated through comparison to an exact method. Real-time reactive simulations that compare the schedules before and after the Go-Live phase show the noteworthy improvement derived from generating plans using the proposed proactive approach compared to an alternative nonproactive one.

A user-induced proactive-reactive scheduling tool for dynamic flow-shop environments

This paper proposes a flexible scheduling tool for dynamic flow-shop production systems that is based on a non-preemptive version of the overlapping load adjustment approach. The developed tool adopts a novel two-phase proactive-reactive dynamic scheduling mechanism. The first phase characterises a set of possible scheduling alternatives without actually building any detailed schedule. This phase follows the earliest loading approach via exploiting the jobs’ slack times across all machines. During the second phase, the dynamic build-up of schedules takes place in a progressive manner through a user-induced module that utilises graphical displays capturing in real-time the impact of unforeseen events on the ongoing schedule. Extensive experiments involving450 randomly generated test instances of three different sizes are carried out to assess the performance of the scheduling tool. The flexibility of the tool is assessed by the number of possible scheduling alternatives offered to the end-user. The results unveiled a high number of scheduling possibilities ranging from 155 schedules for the small size instances to 96,202 schedules for the large size instances. The relatively low number of tardy jobs (0.79 jobs on average) clearly exemplifies the robustness and quality of the scheduling alternatives despite the high number of potentially available schedules.

Critical chain based Proactive-Reactive scheduling for Resource-Constrained project scheduling under uncertainty

Project scheduling problems under both resource constraints and uncertainty have been widely studied due to their real world relevance. In this paper, we design and implement a new integrated proactive–reactive solution approach based on the critical chain method (CCM) to proactively generate a robust and reliable baseline schedule for the class of resource-constrained project scheduling problem (RCPSP) under uncertainty. A discrete-time Markov decision process model is applied for the reactive scheduling phase, which embeds the look-up table method in reinforcement learning to dynamically schedule and adjust schedule reactively using the baseline schedule during project execution. The cost values in the look-up table are calculated based on the occupation of a project buffer and feeding buffers in the baseline schedule generated by the CCM. We conduct computation experiments on the benchmark instances to test our algorithm. The results show that our approach is able to obtain quality solutions efficiently, and competitive with the benchmark algorithms for small- and medium-sized instances.

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.

Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling

Proactive-reactive scheduling is important in the situations where the project collaborators need to coordinate their efforts. The coordination is mostly achieved through the combination of the shared baseline schedule and the deviation penalties. In this paper, we present an extension of predictive Gantt chart to the proactive-reactive scheduling needs. It can be used to track the evolution of the relationship between dynamic and static elements through the time. The dynamic elements are evolving probability distributions due to the uncertainty and revealed information. The static elements are time-agreements in the baseline schedule. We demonstrate that in the state-of-the-art proactive-reactive scheduling, the baseline schedule is agnostic to the information received during the project execution. The sources of such inflexibility in the problem model and the scheduling methods are analyzed. The visualization is highlighted as a precursor to developing new methods that proactively change the baseline schedule in accordance with the gained information.

Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems

Parties that collaborate on projects need to synchronize their efforts. For this reason they seek a decreased rescheduling variability of the time arrangements. Proactive–reactive scheduling is important in such situations. It predominantly achieves synchronization through a shared baseline schedule and deviation penalties. As the latter currently introduce an unrealistically high level of inflexibility, the solution methods never proactively update the baseline schedule. We propose threshold-based cost functions for the deviation penalties to enable a more realistic modeling of aspects of project collaboration. These functions introduce a greater degree of flexibility through the notion of planning horizons for the activities. This results in the possibility of profitable proactive changes to the baseline schedule. We present two metaheuristic approaches for the case of stochastic durations: rollout-based and iterative policy search. Both these approaches use such opportunities to achieve substantial cost–performance improvements in comparison to the best existing method. This enhancement comes at the price of an increased computational burden and the greater complexity of the solution space.

GA-Based Proactive-Reactive Scheduling Mechanism for a Flexible Job Shop Problem under Multi-Uncertainty

This article investigates the flexible job-shop scheduling problem (FJSP) with multi-uncertain. A proactive-reactive scheduling mechanism is put forward to against the fluctuating process time and equipment breakdowns. This mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the redundancy-based technique is used to generate robust baseline schedules; in the reactive scheduling stage, a reactive scheduling is adopted to rectify the predictive scheduling to adapt to the occurrence of machine failures. Based on this, an integrating algorithm is presented with the goal of the make span. Numerical experiments show that the proposed algorithm has a better performance than Genetic Algorithm (GA) on flexible job-shop scheduling problem under processing time uncertainty and the reactive scheduling algorithm can handle the equipment breakdown with little robustness lost.

Evaluation of a new human-machine decision support system for group scheduling

Abstract The group sequencing method is a proactive reactive scheduling method which aims at bringing flexibility as well as quality. This method is based on a human-machine system allowing to choose in real-time the next operation to perform in a group of permutable operations. In this paper, we propose a new decision support system to improve the cooperation between the human operator and the machine. This new system intends to favor the activity of the operator. To evaluate the pertinence of this new method, an experiment has been conducted. The results show the interest of this new decision support system.

Multi-agent-based proactive–reactive scheduling for a job shop

Multi-agent-based proactive–reactive scheduling for job shops is presented, aiming to hedge against the uncertainties of dynamic manufacturing environments. This scheduling mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the objective is to generate a robust predictive schedule against known uncertainties; in the reactive scheduling stage, the objective is to dynamically rectify the predictive schedule to adapt to unknown uncertainties, viz. the reactive scheduling stage is actually complementary to the proactive scheduling stage. A stochastic model is presented, which concerns uncertain processing times in proactive scheduling stage on the basis of analyzing the deficiencies of a classical scheduling model for a production schedule in practice. For the stochastic scheduling problem, a multi-agent-based architecture is proposed and a distributed scheduling algorithm is used to solve this stochastic problem. Finally, the repair strategies are introduced to maintain the original proactive schedule when unexpected events occur. Case study examples show that this scheduling mechanism generates more robust schedules than the classical scheduling mechanism.

The Research on Proactive-Reactive Scheduling Framework Based on Real-Time Manufacturing Information

Because of the dynamic and uncertain conditions of a real manufacturing system, many unforeseen events (e.g., machine breakdown, job revision, urgent jobs arrival, etc.) may lead to numerous schedule disruptions during schedule execution. In this paper, we present a new mixed technique that combines a proactive approach with a reactive approach to deal with scheduling problem under uncertainty. In the proactive phase, we build a robust baseline schedule that minimizes the schedules distance defined as the sum of the absolute deviations between the baseline and expected schedules. The robust baseline schedule contains some built-in flexibility in order to minimize the need of complex search procedures for the reactive scheduling approach. Based on the real-time manufacturing information getting online from the manufacturing execution system (MES), we then develop a reactive scheduling procedure to quickly revise the disrupted schedule when unexpected events occurring. The proposed framework integrated proactive and reactive approach is applied to molds production management system. The experimental results show that this mixed technique is more efficient than another technique when the duration of operation is uncertain.