Reactive Scheduling Approach Research Articles

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

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method.

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 reactive scheduling approach for the resource-constrained project scheduling problem with dynamic resource disruption

PurposeThis paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.Design/methodology/approachThis paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.FindingsThe results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.Originality/valueThe paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

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.

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).

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.

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 Approach Based on Fuzzy Inference for Hybrid Flowshop Systems

A Reactive Scheduling Approach Based on Fuzzy Inference for Hybrid Flowshop Systems

Dealing with disruptions in low-volume manufacturing: a constraint programming approach

Unexpected events can compromise the execution of the production schedule in low-volume assembly lines. When a disruption occurs because of a delayed part supply, a quality problem or an operator absence, a reactive scheduling approach should be used in a short time in order to prevent significant deviations in final performances. In this study, we propose a new approach based on constraint programming to deal with disruptions. It is tested on a large dataset of problem instances and the obtained results are discussed.

Task Assignment in Camera Networks: A Reactive Approach for Manufacturing Environments

Camera networks have been increasingly adopted in manufacturing to enhance workplace safety and maintain production quality levels. Effective management of cameras is crucial for the network to achieve its designated monitoring goal and fulfil task-specific requirements. In this paper, we investigate the online multicamera task assignment problem and propose a reactive scheduling approach that is suitable for manufacturing environments. In our problem, tasks occur stochastically, based on detecting or receiving specific information of activities occurring in the workcell. Task-specific information such as location, priority, required resolution of detection, and number of cameras needed to monitor the task are revealed at its release time, while the task lifetime remains unknown until its actual completion time. A utility function is derived to quantify each camera’s ability to monitor a task occurrence. Estimation is based on the expected task quality, measured in terms of the resolution of detection, and each camera’s cost, measured in terms of its pose change and workload. We develop a framework for experimentally designing and testing camera networks in a visually and behaviorally realistic virtual simulation environment. In particular, we demonstrate our solution on a manufacturing workcell. Results show the efficiency of the presented framework, which can be applied for establishing practical camera networks.

A New Reactive Scheduling Approach for Short-Term Crude Oil Operations under Tank Malfunction

A new reactive scheduling model in the form of FRS (full rescheduling strategy) for front-end crude oil operations has been developed. The reactive rescheduling model is triggered off across all the units at the time that tank malfunction is detected. As compared to the previous work (Ind. Eng. Chem. Res.. 2014, 53, 12502–12518) more practical features have been addressed on tank failure handling solutions: (i) failure tank leftover inventory will be first evacuated to a backup tank, and later on directed to downstream units, and (ii) failure tank leftover inventory will be immediately directed to its downstream units. Furthermore, comprehensive strategy analyses have been conducted on partial rescheduling strategy (PRS) and FRS to validate their respective pros and cons. It is found out from case studies that although FRS results in more operational costs than PRS due to an increased number of time events, FRS is more flexible and less susceptible to current scheduling results, which ensures more robust ...

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information.

Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields

This paper presents a reactive scheduling approach for flexible manufacturing systems, which integrates the overall energy consumption of the production. This work is justified by the growing needs of manufacturers for energy-aware control, due to new important environmental criteria, which holds true in the context of high reactivity. It makes production hard to predict. The proposed reactive scheduling model is based on potential fields. In this model, resources that sense the intentions from products are able to switch to standby mode to avoid useless energy consumption and emit fields to attract products. Simulations are provided, featuring three indicators: makespan, overall energy consumption and the number of resource switches. Real experiments were carried out to illustrate the feasibility of the approach on a real system and validate the simulation results.

Reactive scheduling in a job shop where jobs arrive over time

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job shop scheduling problem (CJSSP), it is assumed that all jobs to be processed are available at the beginning of the scheduling process. Reactive scheduling approach is one of the effective approaches for DJSSP. In the paper, a heuristic is proposed to implement the reactive scheduling of the jobs in the dynamic production environment. The proposed heuristic decomposes the original scheduling problem into a number of sub problems. Each sub problem, in fact, is a dynamic single machine scheduling problem with job release dates. The scheduling technique applied in theproposed heuristic is priority scheduling, which determines the next state of the system based on priority values of certain system elements. The system elements are prioritized with the help of scheduling rules (SRs). An approach based on gene expression programming (GEP) is also proposed in the paper to construct efficient SRs for DJSSP. The rules constructed by GEP are evaluated in the comparison of the rules constructed by GP and several prominent human made rules selected from literatures on extensive problem sets with respect to various measures of performance.

Scheduling Energy Cogeneration Units under Energy Demand Uncertainty

This work presents a methodology for the scheduling of networks that consist of various energy cogeneration units (i.e., generate both electrical energy and heat) under high energy demand uncertainties of bounded form. First, a mathematical framework for the problem in question is developed, and then we present a reactive scheduling approach that relays on multiparametric programming and a receding horizon. In brief, the original mathematical scheduling model is reformulated into a multiparametric programming problem, wherein we consider as uncertain parameters: the energy demands as well as the initial and the final state of the overall system. The resulting parametric optimization problem is solved once and off-line, and afterwards a receding horizon-like approach is applied for the reactive scheduling –that acts on the fly in the energy demand realizations– by performing just function evaluations and avoiding the on-line optimization of such systems. A couple of problem instances are solved to shed light on the potential benefits of the suggested method. The proposed concept has highly practical interest and is very promising since could find application in several other optimization problems that can involve: large-scale optimization problems and process scheduling problems under uncertainty.

The development of a robust resource constrained project scheduling framework

Resource constrained scheduling attempts to schedule projects with deterministic activity durations subject to various objective functions and additional assumptions. During project execution, however, a project may be subject to considerable uncertainty. Such uncertainty may lead to the realised activity duration to deviate from the durations that were estimated in the baseline schedule and cause a serious project overdue. This paper provides a whole robust project scheduling strategy, which is tolerant to various uncertainties. A framework combines the buffer sizing technique, proactive project scheduling approach, and reactive project scheduling approach. The various buffer sizing techniques, proactive project scheduling approaches and reactive project scheduling approaches are investigated. The proposed scheduling framework constructed based on these approaches is tested using a large experimental design in order to detect the effectiveness of each procedure. Knowing the characteristics of each approach enables project managers to identify the appropriate scheduling methodology for different project scheduling environments.

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.

A generic deadlock-free reactive scheduling approach

In this paper, a generic deadlock-free reactive scheduling (GDRS) approach is proposed. The approach can react to the occurrence of a variety of disruptions in flexible job shops, in real time. Internal and external disruptions such as machine breakdown, process time variation, urgent job, arrival of a new job order, and order cancellation are considered. Performance comparisons of the proposed approach with that of Total Rescheduling (TR), and with the modified Affected Operations Rescheduling (mAOR) heuristic proposed earlier in the literature are presented by considering scheduling efficiency and stability. The results show that, for most of the disruptions considered, GDRS retains the rescheduling efficiency of TR while providing superior system stability over the other two approaches.

Analysis of reactive deadlock-free scheduling in flexible job shops

Reactive scheduling is a procedure followed in production systems to react to unforeseen events that disturb the normal operation of the system. In this paper, a novel operations insertion heuristic is proposed to solve the deadlock-free reactive scheduling problem in flexible job shops, upon the arrival of new jobs. The heuristic utilizes rank matrices (Latin rectangles) to insert new jobs in schedules, while preventing the occurrence of deadlocks or resolving them using the available buffer space (if any). Jobs with alternative processing routes through the system are also considered. The heuristic can be employed to execute two reactive scheduling approaches in a timely efficient manner; to insert the new jobs in the already existing schedule (job insertion) or to reschedule all the jobs in the system (total rescheduling). Using experimental design and analysis of variance (ANOVA), the relative performance of the two approaches is studied and analyzed to provide some measures and guidelines for selecting the appropriate reactive scheduling approach for different problem settings. Three measures of performance are considered in the analysis; efficiency of the revised schedules in terms of the mean flow time, resulting system nervousness, and the required solution time. The results show that, on average, job insertion obtains revised schedules featuring significantly lower system nervousness and slightly higher mean flow time than total rescheduling. However, depending on the system size, number and processing times of the new jobs, and the available flexibility in the system, a trade-off between the two approaches should sometimes be considered.

A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling

This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly as dispatching rules while providing more stability. It is ready to be implemented in a distributed environment where agents have minimum global information thereby improving system fault tolerance. Computational experiments on dynamic job arrivals provide the experimental justification of the framework. First, a comparison of computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules is presented to show the effectiveness and robustness of the developed framework. Then, a comparison of the computational results among four cases of dynamic job arrivals is presented to demonstrate the effects of making full use of available uncertain information about disruptions using this framework for the enhancement of scheduling robustness.