Dynamic Rescheduling Research Articles

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

This paper addresses two main problems with many heuristic task allocation approaches—solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensus-based bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm’s exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners —This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation and testing of a version of PI that incorporates all three of these modules are the final goal of this research.

Dynamic response to demand variability for precast production rescheduling with multiple lines

Production scheduling plays a crucial role in the prefabricated construction productivity and on-time delivery of precast components (PCs). However, previous studies mainly focused on the static scheduling of single production line without considering the demand variability in practice. To achieve dynamic production planning, a Two-level Rescheduling Model for Precast Production with multiple production lines is developed to minimise the rescheduling costs based on genetic algorithm, from the two levels of (1) selection of production line and (2) rescheduling of jobs based on PCs’ priority. Further, two scenarios of different and shared mould types are investigated to represent real-world production environments. Finally, a real case study is conducted to test the validity of proposed rescheduling model. 58.1 and 48.5% cost savings are achieved by comparison to no response to changes and heuristic rescheduling methods, respectively. This research contributes to the precast production theory by expanding the insight into dynamic rescheduling with multiple production lines. The methodology will promote the on-time delivery of PCs and enhance the dynamic precast production management.

On a shared human-robot task scheduling and online re-scheduling

Human-Robot (HR) teams are expected to work more efficiently and interdependently compared to the manual non-added value manufacturing processes. This paper discusses a method for enabling the shared HR task scheduling and dynamic re-scheduling. Humans, robots, and HR teams are involved in the resources model, while the task sequence and the task precedence constraints consist of the workload model. An intelligent multi-criteria decision-making framework is proposed for evaluating alternative solutions for the shared HR task scheduling. Dynamic re-scheduling is possible in the cases of unexpected events during the execution of the task. The dynamic rescheduling is triggered by IEC61499 events generated in running time. The method has been implemented as a software tool in Java and has been demonstrated in the hybrid assembly of a vehicle turbocharger.

Dynamic scheduling of shared human-robot manufacturing operations

The use of robots working with humans offers new possibilities in the execution of assembly tasks. A common model needs to be established describing how a task will be executed in a collaborative manner. The proposed methodology adopts a two-level breakdown of each job. On top, a job is analyzed in simpler tasks. Tasks are translated to specific operations, with the related constraints, to be carried out by a human or a robot. This approach enables a collaborative flexible manufacturing assembly station while supporting dynamic re-scheduling of manufacturing operations towards an adaptive and more efficient execution of the production schedule.

Agent-based Dynamic Rescheduling of Daily Activities

When simulating individuals’ daily plan, lots of unexpected events need to be considered, like traffic jam and weather changes. Therefore, there will be a mismatch between the original plan and the executed one. Faced with this situation, individuals need to adjust the rest of the activities to make a new schedule. This paper analyzes the causes of rescheduling, and establishes a new rescheduling model, combining strengths of existing rescheduling models. The model in this paper considers the rescheduling possibilities and choices as much as possible. It takes time pressure and schedule similarity into consideration when updating a schedule. Furthermore, this paper analyzes joint trip/activity execution by studying the cooperation between agents during the rescheduling process.

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.

Using CPS Enabled Microgrid System for optimal power utilization and supply strategy

Because the power grid experiences dynamic variations in energy generation and demand, inclusion of renewable energy alone will not assure self-sustainability of the grid system. Energy sustainability can be achieved by developing Cyber Physical System enabled smart buildings capable of dynamic energy management. In this research work, we propose the architecture for CPS enabled sustainable buildings integrated with Distributed Energy Generators (DEG). We have developed three algorithms, namely, Equipment Classification Algorithm (ECA), Context Aware Room Energy Utilization (CAREU), and Availability based Management Algorithm (AMA), for dynamic energy management to attain energy sustainability of smart buildings, and optimization models for profit maximization in smart buildings. Prototype of the smart buildings are developed with features such as realtime energy monitoring, renewable energy integration, dynamic rescheduling and reallocation of energy utilization of equipments to attain energy sustainability. The results show that the proposed method provides energy sustainability compared to the current state-of-the-art methods.

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

DRASE: A Dynamic Rescheduling and Self-Adaptive Estimation Technique to Enhance ACS Throughputs in CWMP

The first-come, first-served strategy is generally applied to implement customer premises equipment (CPE) wide area network management protocol, but may work poorly under overloaded conditions. Namely, many sessions established between CPEs and the corresponding auto configuration server (ACS) must be kept waiting. Therefore, a dynamic rescheduling and self-adaptive estimation (DRASE) approach is proposed in this letter to increase the throughput of an ACS with an acceptable drop rate. Specifically, the order of rescheduling is determined by the estimated processing time (EPT), the remaining lifetime, and the waiting time of each session. Once the session is completed, a self-adaptive technique is adopted to update the EPT to increase the effectiveness of rescheduling. Experimental studies show that the throughput of an ACS using DRASE at peak times reaches 1.129 times of the original value, and other evaluated performance metrics are also significantly improved.

Dynamic Pallet Routing in a Manufacturing Transport Line With Model Predictive Control

This brief describes the application of model predictive control to a manufacturing multipallet multitarget transport line. The mathematical representation of the plant is based on a mixed logical dynamical model. The performance index to be minimized weights the distance of the pallets from their final target, the pallet residence time on the line, and the control inputs. The resulting mixed integer linear programming problem is recursively solved to compute the control action according to the receding horizon approach. Both simulation and experimental results on the real system are reported and discussed to witness the performances of the control algorithm and its ability to manage even pallet route conflicts and target dynamic rescheduling.

A dynamic multi-agent-based scheduling approach for SMEs

In modern manufacturing systems with computational complexities, decision-making with respect to dynamic rescheduling and reconfiguration in case of internal disturbances is an important issue. This paper introduces a multi-agent-based dynamic scheduling system for manufacturing flow lines (MFLs) using the Prometheus methodology (PM) considering the dynamic customer demands and internal disturbances. The PM is used for designing a decision-making system with the feature of simultaneous dynamic rescheduling. The developed system is implemented on a real MFL of a small- and medium-sized enterprise (unplasticized polyvinyl chloride (uPVC) door and window) where the dynamic customer demands and internal machine break downs are considered. The application has been completely modeled using a Prometheus design tool, which offers full support to the PM, and implemented in JACK agent-based systems. Each agent is autonomous and has an ability to cooperate and negotiate with other agents. The proposed decision-making system supports both static and dynamic scheduling. A simulation platform for testing the proposed multi-agent system (MAS) is developed, and two real scenarios are defined for evaluating the proposed system. The analysis takes into account the comparisons of the overall performances of the system models using the MAS scheduling and conventional scheduling approaches. The result of simulation indicates that the proposed MAS could increase the uptime productivity and the production rate of flexible flow-line manufacturing systems.

A scheduling model for astronomy

Abstract Astronomical scheduling problem has several external conditions that change dynamically at any time during observations, like weather condition (humidity, temperature, wind speed, opacity, etc.), and target visibility conditions (target over the horizon, Sun/Moon blocking the target). Therefore, a dynamic re-scheduling is needed. An astronomical project will be scheduled as one or more Scheduling Blocks (SBs) as an atomic unit of astronomical observations. We propose a mixed integer linear programming (MILP) solution to select the best SBs, favors SBs with high scientific values, and thus maximizing the quantity of completed observation projects. The data content of Atacama Large Millimeter/Submillimeter Array (ALMA) projects of cycle 0 and cycle 1 were analyzed, and a synthetic set of tests of the real instances was created. Two configurations, one of 5000 SBs in a 3 months season and another 10,000 SBs a 6 months season were created. These instances were evaluated with excellent results. Through the testing it is showed that the MILP proposal has optimal solutions.

PowerNap: a power-aware distributed Wi-Fi access point scheduling algorithm

In this paper, we design a power-aware distributed access point scheduling algorithm, PowerNap, to enhance power conservation of co-existing multiple access points (APs) each having multiple clients in the same wireless vicinity. This consequently addresses low channel utilization, degraded throughput, and unfairness problems of Wi-Fi networks in an energy-efficient way. PowerNap schedules transmission periods of APs according to their traffic loads to ensure fair access of the medium from their respective clients’ perspective. It supports dynamic rescheduling of AP transmission periods to aid client mobility and traffic fluctuations. The scheduling also ensures that no two APs, in a shared environment, wake up their clients at exactly the same time, decreasing data packet collisions and thus increasing network throughput and energy-efficiency. PowerNap achieves decentralization by exploiting single-hop neighborhood information (e.g., traffic loads) only, and thus, it is scalable. Performance evaluations, carried out in ns-3, depict that the effectiveness of the proposed PowerNap algorithm surpasses the state-of-the-art approaches in terms of energy consumption, network throughput, and fairness.

Fault-Tolerant Dynamic Rescheduling for Heterogeneous Computing Systems

As the scale and complexity of heterogeneous computing systems grow, failures occur frequently and have an adverse effect on solving large-scale applications. Hence, fault-tolerant scheduling is an imperative step for large-scale computing systems. The existing fault-tolerant scheduling algorithms belong to static scheduling, and they allocate multiple copies of each task to several processors no matter whether processor failures affect the execution of tasks. Such active replication strategies not only waste resource but also sacrifice the makespan. What is more, they cannot guarantee the successful execution of applications. In this paper, we propose a fault-tolerant dynamic rescheduling algorithm named FTDR, which can overcome above drawbacks. FTDR keeps listening to the processor failure, and reschedules the suspended tasks once failures occur. Because FTDR reschedules the tasks that are suspended because of failures, it can tolerate an arbitrary number of failures. Randomly generated DAGs are tested in our experiments. Experimental results show that the proposed algorithm achieves good performance in terms of makespan and resource consumption compared with its direct competitors.

Performance Evaluation of Bidding-Based Multi-Agent Scheduling Algorithms for Manufacturing Systems

Artificial Intelligence techniques have being applied to many problems in manufacturing systems in recent years. In the specific field of manufacturing scheduling many studies have been published trying to cope with the complexity of the manufacturing environment. One of the most utilized approaches is (multi) agent-based scheduling. Nevertheless, despite the large list of studies reported in this field, there is no resource or scientific study on the performance measure of this type of approach under very common and critical execution situations. This paper focuses on multi-agent systems (MAS) based algorithms for task allocation, particularly in manufacturing applications. The goal is to provide a mechanism to measure the performance of agent-based scheduling approaches for manufacturing systems under key critical situations such as: dynamic environment, rescheduling, and priority change. With this mechanism it will be possible to simulate critical situations and to stress the system in order to measure the performance of a given agent-based scheduling method. The proposed mechanism is a pioneering approach for performance evaluation of bidding-based MAS approaches for manufacturing scheduling. The proposed method and evaluation methodology can be used to run tests in different manufacturing floors since it is independent of the workshop configuration. Moreover, the evaluation results presented in this paper show the key factors and scenarios that most affect the market-like MAS approaches for manufacturing scheduling.

Preventive maintenance scheduling of multi-component systems with interval costs

We introduce the preventive maintenance scheduling problem with interval costs (PMSPIC), which is to schedule preventive maintenance (PM) of the components of a system over a finite and discretized time horizon, given a common set-up cost and component costs dependent on the lengths of the maintenance intervals. We present a 0-1 integer linear programming (0-1 ILP) model for the PMSPIC; the model is identical to that presented by Joneja (1990) for the joint replenishment problem within inventory management. We study this model from a polyhedral and exact solutions’ point of view, as opposed to previously studied heuristics (e.g. Boctor, Laporte, & Renaud, 2004; Federgruen & Tzur, 1994; Levi, Roundy, & Shmoys, 2006; Joneja, 1990). We show that most of the integrality constraints can be relaxed and that the linear inequality constraints define facets of the convex hull of the feasible set. We further relate the PMSPIC to the opportunistic replacement problem, for which detailed polyhedral studies were performed by Almgren et al. (2012a). The PMSPIC can be used as a building block to model several types of maintenance planning problems possessing deterioration costs. By a careful modeling of these costs, a polyhedrally sound 0-1 ILP model is used to find optimal solutions to realistic-sized multi-component maintenance planning problems. The PMSPIC is thus easily extended by side-constraints or to multiple tiers, which is demonstrated through three applications; these are chosen to span several levels of unmodeled randomness requiring fundamentally different maintenance policies, which are all handled by variations of our basic model.Our first application considers rail grinding. Rail cracks increase with increasing intervals between grinding occasions, implying that more grinding passes must be performed—thus generating higher costs. We optimize the grinding schedule for a set of track sections presuming a deterministic model for crack growth; hence, no corrective maintenance (CM) will occur between the grinding occasions scheduled. The second application concerns two approaches for scheduling component replacements in aircraft engines. The first approach is bi-objective, simultaneously minimizing the cost for the scheduled PM and the probability of unexpected stops. In the second approach the sum of costs for PM and expected CM—without rescheduling—is minimized. When rescheduling is allowed, the 0-1 ILP model is used as a policy by re-optimizing the schedule at a component failure, which then constitutes an opportunity for PM. The policy manages the trade-off between costs for PM and unplanned CM and is evaluated in a simulation of the engine. The third application considers components’ replacement in wind mills in a wind farm, extending the PMSPIC to comprise multiple tiers with joint set-up costs. Due to the large number of components unexpected stops occur frequently, thus calling for a dynamic rescheduling, which is evaluated through a simulation of the system. In each of the three applications, the use of the 0-1 ILP model is compared with age or constant-interval policies; the maintenance costs are reduced by up to 16% as compared with the respective best simple policy. The results are strongest for the first two applications, possessing low levels of unmodeled randomness.

Process planning and scheduling integration with optimal rescheduling strategies

It is of more practical significance to carry out research on the integrated process planning and scheduling/rescheduling (IPPSR) to achieve a global improvement for the performance of a manufacturing system with sustained pursuit and various uncertainties. In this paper, a framework and a unified dynamic rescheduling model for IPPSR with three typical types of situations normally encountered in a production system, i.e. arrival of new jobs, machine breakdown and order cancellation have been constructed. Meanwhile, an improved evolutionary algorithm (IEA) for the integrated process planning and scheduling (IPPS) to generate an optimal initial scheduling plan has been developed. In order to improve the performance of the algorithm for IPPS and facilitate the extension of it to rescheduling situation, new genetic representation for the scheduling plan combined with process plans and corresponding genetic operators are developed. Based on the dynamic rescheduling model and initial optimised result, the integrated rescheduling strategies for the above three types of situations have been developed. In order to verify the feasibility and performance of the proposed strategies, experimental studies were conducted and comparisons were made for different performance measures; the results show that the proposed approaches not only provide more effective result for IPPS but also for integrated rescheduling with the above three types of uncertainties.

Optimization of Dynamic Job-Shop Scheduling Based on Game Theory

To deal with uncertain dynamic interferences occurred in the workshop, and meet the competition requirements of jobs submitted by different customers, a dynamic job-shop scheduling model based on game theory is presented. In order to find the Nash equilibrium point of the model effectively, dynamic rescheduling judgment based on event-driven policy is adopted, and a hybrid genetic algorithm is designed. The case study is carried out to demonstrate the validity of above dynamic job-shop scheduling methods.

Optimized acceleration of repetitive construction projects

Abstract Contractors and/or owners frequently need to accelerate the delivery of construction projects. Contractors may have to accelerate in order to benefit from contractual bonus, avoid penalties, recover from delays and/or avoid undesirable weather and site conditions. Owners, on the other hand, may order acceleration to meat business and operational opportunities. This paper presents an algorithm for schedule updating, dynamic rescheduling and optimized acceleration of repetitive construction projects. Schedule updating captures the exact progress on site. Dynamic rescheduling aims at capitalizing on the repetitive nature of the project to fine-tune the remaining portion of the project. Optimized acceleration presents an optimized time–cost trade-off that is tailored for repetitive projects. Through a set of iterative steps, the optimized acceleration procedure divides each activity into segments and identifies the segments that would shorten project duration if accelerated. For those identified segments, the ones with the least cost slope are selected and queued for acceleration. Through the proposed segmentation of activities this algorithm provides optimum allocation of additional acceleration resources, thus is rendered capable of identifying least cost acceleration plans. The algorithm allows users to select among different acceleration strategies such as working overtime, working double shifts, working weekends, and employing more productive crews. The presented algorithm maintains work continuity and accounts for typical and non-typical activities. The algorithm is implemented in a spreadsheet application, which automates calculations, yet allows users to fine tune the algorithm to fit the project at hand. The developed algorithm is applied to a case study drawn from literature in order to illustrate its basic features and demonstrate its accuracy.

A multi-agent system to support heuristic-based dynamic manufacturing rescheduling

In manufacturing systems, process planning and scheduling are the two important pre-production planning functions which are usually performed sequentially. In a dynamic manufacturing environment, however, the shop floor has to encounter disruptions caused by disturbances and uncertainties. The original process plan and schedule may then become inefficient or even infeasible. Ideally, the process plan and the schedule have to be dynamically modified in accordance with the resource availability and conflicts on the shop floor. The merit of integrated process planning and scheduling IPPS is to increase the production feasibility and optimality by combining both the process planning and scheduling problems. An increasing number of intelligent approaches, such as search-based algorithms and negotiation-based multi-agent systems, have been proposed for IPPS, Research on the negotiation-based IPPS systems has been focused on the establishment of negotiation protocols to cater for the integration of process planning and scheduling. However, it is intricate to determine the appropriate utility functions and negotiation strategies for individual agentsin the negotiation-based IPPS system. In this paper, a multi-agent system MAS architecture is proposed to solve the dynamic IPPS problem with embedded heuristic algorithms. The MAS system is able to support a variety of heuristic methods to effect dynamic process planning, scheduling and re-scheduling. As a result, the proposed MAS system for dynamic IPPS using heuristics possesses high flexibility, extensibility, and accessibility for manufacturing applications.