Rescheduling Policies Research Articles

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.

Evaluation of the Implementation of Reschedule Policy in Portfolio at Risk (PAR) Control at PT. PNM Mekaar Regional Blitar

Troubled financing at PT. PNM Mekaar Regional Blitar can occur due to internal factors and external factors. Internal factors are factors that come from within the company itself, such as weak analysis and supervision of customer financing. External factors are factors that are beyond the control of the company's management, such as the character of untrustworthy customers and natural disasters. The implementation of a rescheduling policy for customers with problems is expected to control the PAR value. Based on the phenomena that occur, researchers conduct research on evaluating the application of rescheduling policies in controlling PAR values. This study uses a descriptive qualitative approach. Sources of data used in this study consisted of primary data and secondary data. Data collection was obtained through observation, interviews and documentation. Data analysis through three paths: data reduction, data presentation, conclusion and verification

A preventive–reactive approach for nurse scheduling considering absenteeism and nurses’ preferences

The nurse scheduling problem (NSP) has become significant in recent years due to its direct impact on patient healthcare. This problem involves assigning nurses’ shifts while fulfilling a set of hard constraints associated with labor regulations and soft constraints related to personal preferences, workload balance, among others. Most studies have focused on providing solutions for deterministic scenarios without considering unexpected disruptions, such as an unscheduled nurse absence. This study integrates two of the most common approaches to address absenteeism: preventive and reactive. First, we propose a multiobjective linear model for staff scheduling that preventively assigns backup nurses for each day. The NSP is known to be an NP-hard problem. Therefore, we used a genetic algorithm to obtain solutions in a reasonable amount of time. To mitigate the effect of unscheduled nurse absences, we propose two reactive rescheduling policies, one that seeks to maintain the baseline schedule and another that prioritizes the exclusive use of backup nurses. We used Montecarlo simulation under different problem settings to compare the proposed policies with a policy that does not use the preventive approach. The probability that a nurse will accept an additional shift to cover an absence was also considered. Simulation results suggest that both of our preventive–reactive policies outperform the non-preventive policy, especially in the presence of a small probability that a nurse will accept an additional shift. Finally, we used the proposed policies to create the monthly nursing schedule in a reference hospital in Bogotá-Colombia.

End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning

With the advent of the socio-technical manufacturing paradigm, the way in which rescheduling decisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly dynamic conditions. To cope with uncertain production environments, a drastic increase in the type and degree of automation used at the shop floor for handling unforeseen events and unplanned disturbances is required. In this work, the on-line rescheduling task is modelled as a closed-loop control problem in which an artificial autonomous agent implements a control policy generated off-line using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control policy is stored in a deep neural network, which is used to select repair actions in order to achieve a small set of repaired goal states. The rescheduling agent is trained using Proximal Policy Optimisation based on a wide variety of simulated transitions between schedule states using colour-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end deep learning of successful rescheduling policies to encode task-specific control knowledge that can be understood by human experts.

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.

A queueing model for customer rescheduling and no-shows in service systems

We study an M/M/1/K queue where customers can reschedule their appointments. Rescheduled customers show up with higher probabilities, incurring lower no-show costs, but rescheduling also frees up slots that may not be filled later, leading to wasted service capacity and lower throughput. The system manager aims to minimize the long-run average cost by controlling rescheduling policies. We derive conditions under which rescheduling should be allowed for different scenarios depending on whether customers can reschedule only once or multiple times.

Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant

Periodic rescheduling is a commonly used method for scheduling short-term operations. Through computational experiments that vary plant parameters, such as the load and the capacity of a facility, we investigate the effects these parameters have on plant performance under periodic rescheduling. The results show that choosing a suitable rescheduling policy depends highly on some key plant parameters. In particular, by modifying various parameters of the facility, the performance ranking of the various rescheduling policies may be reversed compared to the results obtained with nominal parameter values. This highlights the need to consider both facility characteristics and what the crucial objective of the facility is when selecting a rescheduling policy. This study considers a variant of the job shop problem, used to model the operation of an industrial-scale analytical services facility using different periodic rescheduling policies. A rolling horizon routine is used to schedule operations over the scheduling horizon. Performance is measured in terms of job throughput, job makespan, and proportion of jobs on time at the end of the scheduling horizon to obtain a more complete understanding of how performance varies between rescheduling policies.

Study of event-driven and periodic rescheduling on a single machine with unexpected disruptions

This paper studies the rescheduling problem of a single machine facing unexpected disruptions in order to determine which parameters can help reducing the negative impacts of these disruptions on schedule performance. A Genetic Algorithm (GA) is used to generate the initial schedule and the updated ones according to a reactive strategy. The performance of event-driven rescheduling and periodic rescheduling policies are compared in terms of total tardiness and total cost of rescheduling. Other factors that may affect rescheduling such as disruption time, disruption duration and number of disruptions are investigated. The sensitivity of results to both due date tightness and cost factor variation is tested. The results showed that the timing of the occurrence of disruption as related to scheduling horizon has a major effect on determining the best rescheduling policy. Event-driven policy is superior to other policies for short infrequent disruptions. It was found that the periodic policy is more appropriate for long and frequent disruptions.

Maximizing operating room efficiency by effectively managing elective case cancellation

Background: Surgery cancellation and rescheduling affect efficiency and satisfaction, both critical aspects of the modern orthopaedic practice. The purpose of this study was to critically and comprehensively evaluate cancellations to define actionable ways to improve efficiency, with the goal to develop evidence-based, transparent rescheduling policies. Methods: A retrospective review was completed of all patients who were scheduled for elective surgery over 1 yr. Outcome measures included rate of cancellation, interval from time of cancellation to scheduled surgery date, risk factors for cancellation, re-schedule rate, re-cancellation rate, and risk factors for re-cancellation. Results: There were 7,215 elective surgeries, 13% of which were cancelled prior to surgery. Forty-four percent of surgeries were cancelled within 3 days of the surgery. Of those that were cancelled, 53% were rescheduled, 21% of which had cancelled again, which was significantly higher than the comparison group of first-time cancellations. Those that were cancelled without a correctable reason had significantly more re-cancellations and a lower procedure completion rate. Conclusions: When scheduling surgeries, it is more efficient to preference surgeries that have not been previously cancelled, and among previously cancelled surgeries to preference those with a correctable reason for cancellation. While not novel concepts, this study provides an evidence base for rescheduling policies, which can be made transparent for patients. Ultimately, this may improve efficiency as well as patient trust and satisfaction.

Closed-loop Rescheduling using Deep Reinforcement Learning

Fil: Palombarini, Jorge Andres. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Centro de Investigaciones y Transferencia de Villa Maria. Universidad Nacional de Villa Maria. Centro de Investigaciones y Transferencia de Villa Maria; Argentina

A Heuristic for Inserting Randomly Arriving Jobs Into an Existing Hoist Schedule

Hoist scheduling in automated electroplating lines has been extensively studied in a static environment. However, practical electroplating lines are subject to diversified unforeseen disruptions that require frequent rescheduling to maintain or optimize system performance. This paper addresses a hoist scheduling problem, where randomly arriving jobs need to be inserted into an existing schedule without changing the sequence of hoist moves already scheduled. The objective is to minimize the total completion time of all the jobs in the existing schedule and a newly inserted job. We develop a polynomial-time heuristic that adjusts the starting times of the existing hoist moves to a limited extent but does not bring about a severe disturbance of the existing hoist moves. We compare our algorithm with two existing approaches with different rescheduling policies (i.e., partial and zero adjustment of the existing schedule). We empirically analyze the productivity and the stability of the schedules generated by the three approaches. Computational results demonstrate that our algorithm can generate more productive and stable schedules than the two existing approaches. Note to Practitioners —Electroplating and chemical surface treatment lines with automated material handling hoists are commonplace in electronics, semiconductor, and many other manufacturing industries. In an uncertain environment, hoist rescheduling plays an important role in improving the productivity and reducing the impact of disruptions. This paper presents a hoist scheduling algorithm to deal with dynamic job arrivals by considering the impact of the disturbance incurred by rescheduling. Our algorithm can generate a better schedule with smaller total completion time and slighter disturbance than the existing algorithms. The proposed algorithm runs in polynomial time and can be used to control hoist operations in practical electroplating lines. A comparative analysis provides useful insights on the implementation of rescheduling approaches and policies to industry practitioners.

Extending the boundaries between scheduling and dispatching: hedging and rescheduling techniques

While deterministic scheduling models have been well studied, the use of these models is not well documented in manufacturing environments. Previous research has indicated that deterministic scheduling approaches quickly lose their advantage compared to dispatching rules when processing time uncertainty is present. This research presents the case of a Printed Wiring Board Manufacturer’s drilling operation, which is a group of unrelated parallel machines. The manufacturer wishes to minimise makespan, number of late jobs, total overtime, average machine finishing time and machine utilisation when stochastic uncertainty is present. While deterministic scheduling has been shown to be a good solution approach when processing time variability is low, this research attempts to extend the boundaries in which scheduling is useful by investigating job and machine hedges as well as periodic and event driven rescheduling policies. The success of the approach is evaluated using a simulation model to evaluate the performance over a number of sequential schedules under various distributional assumptions.

Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach

In real-world manufacturing, disruptions are often encountered during the execution of a predetermined schedule, leading to the degradation of its optimality and feasibility. This study presents a hybrid approach for flexible job-shop scheduling/rescheduling problems under dynamic environment. The approach, coined as ‘HMA’ is a combination of multi-agent system (MAS) negotiation and ant colony optimisation (ACO). A fully distributed MAS structure has been constructed to support the solution-finding process by negotiation among the agents. The features of ACO are introduced into the negotiation mechanism in order to improve the performance of the schedule. Experimental studies have been carried out to evaluate the performance of the approach for scheduling and rescheduling under different types of disruptions. Different rescheduling policies are compared and discussed. The results have shown that the proposed approach is a competitive method for flexible job-shop scheduling/rescheduling for both schedule optimality and computation efficiency.

Factors Associated With Resident Continuity in Ambulatory Training Practices.

Continuity of care is a critical element of residents' educational experience in primary care programs. We examined how continuity in resident practices compares to nonteaching practices, identified factors associated with increased continuity, and explored the association between continuity and quality measures. We analyzed 117 235 patient visits to 4 resident practices (26 resident teams in internal medicine, pediatrics, family medicine, and medicine-pediatrics) and 270 242 visits to nonteaching community practices between July 2013 and May 2014. We defined continuity from both clinician and patient perspectives, and used logistic regression models to examine the influence of factors on continuity while controlling for postgraduate year, patient age, gender, race, and insurance. Continuity was greater at nonteaching sites compared to resident practices (87.3% versus 56.2%, P < .001). Resident continuity ranged from 33.1% to 83.7% among resident sites. Factors associated with improved resident continuity included absence of advanced practice providers (71.5% versus 52.3%); consistent use of scheduling protocols (77.5% versus 33.1%); rescheduling policies (71.5% versus 41.3%); increased faculty clinical time (71.5% versus 46.3%); and dismissal policies for excessive missed appointments (71.5% versus 62.5%, P < .001 for all). Increased continuity was associated with improved rates of diabetic control (62.8% versus 54.6%); hypertension control (82.8% versus 57.5%); screening colonoscopy (69.2% versus 31.9%); and mammography (74.8% versus 38.2%, P < .001 for all). Increased clinical faculty time, scheduling protocols, and absence of advanced practice providers were most strongly associated with increasing continuity. Increased continuity was associated with improved quality measures.

Multihybrid job scheduling for fault-tolerant distributed computing in policy-constrained resource networks

Unpredictable fluctuations in resource availability often lead to rescheduling decisions that sacrifice a success rate of job completion in batch job scheduling. To overcome this limitation, we consider the problem of assigning a set of sequential batch jobs with demands to a set of resources with constraints such as heterogeneous rescheduling policies and capabilities. The ultimate goal is to find an optimal allocation such that performance benefits in terms of makespan and utilization are maximized according to the principle of Pareto optimality, while maintaining the job failure rate close to an acceptably low bound. To this end, we formulate a multihybrid policy decision problem (MPDP) on the primary-backup fault tolerance model and theoretically show its NP-completeness. The main contribution is to prove that our multihybrid job scheduling (MJS) scheme confidently guarantees the fault-tolerant performance by adaptively combining jobs and resources with different rescheduling policies in MPDP. Furthermore, we demonstrate that the proposed MJS scheme outperforms the five rescheduling heuristics in solution quality, searching adaptability and time efficiency by conducting a set of extensive simulations under various scheduling conditions.

Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources

We address a dynamic repair shop scheduling problem in the context of military aircraft fleet management where the goal is to maintain a full complement of aircraft over the long-term. A number of flights, each with a requirement for a specific number and type of aircraft, are already scheduled over a long horizon. We need to assign aircraft to flights and schedule repair activities while considering the flights requirements, repair capacity, and aircraft failures. The number of aircraft awaiting repair dynamically changes over time due to failures and it is therefore necessary to rebuild the repair schedule online. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter time periods. We propose a complete approach based on the logic-based Benders decomposition to solve the static sub-problems, and design different rescheduling policies to schedule the dynamic repair shop. Computational experiments demonstrate that the Benders model is able to find and prove optimal solutions on average four times faster than a mixed integer programming model. The rescheduling approach having both aspects of scheduling over a longer horizon and quickly adjusting the schedule increases aircraft available in the long term by 10% compared to the approaches having either one of the aspects alone.

Investigating the robustness of re-scheduling policies with multi-agent system simulation

Increased complexity of current manufacturing systems together with dynamic conditions and permanent demands for flexible and robust functionality makes their management and control very difficult and challenging. Workflow simulation is an effective approach to investigate dynamic workflow scheduling policies and evaluate the overall manufacturing system performance. The results attained in simulation model can give directions on how to maximize system output when selecting an appropriate scheduling practice for a real system. In this paper, we investigate the abilities of multi-agent systems in combination with dynamic dispatching rules and failure handling mechanisms to manage dynamic environment conditions (such as machine failures) for systems in the production automation domain. We measure system robustness by systematically assessing the total system performance (e.g., number of finished products) in a number of representative test cases. We use an agent-based simulation environment, MAST, which has been validated with real-world hardware to strengthen the external validity of the simulation results. We investigated the performance of a re-scheduling component which uses four different policies that define how to adjust the system schedule in case of machine disturbances/failures. In the context of the empirical study the Complete Rerouting re-scheduling policy outperformed all other policies.

A new when-to-schedule policy in online scheduling based on cumulative task delays

This paper proposes a new when-to-schedule policy in online scheduling, which considers timing of rescheduling based on the concept of a control limit policy and rolling schedules. Under the proposed policy, rescheduling is carried out based on a cumulative delay which can be a measure to determine suitable timing of rescheduling. Some computational experiments demonstrate the effectiveness of the proposed policy by applying it to single-machine dynamic scheduling with sequence-dependent setup times and urgent jobs, where total setups are minimized as well as rescheduling frequency. It is also demonstrated that the proposed policy can outperform typical rescheduling policies.

Integer programming approach to reactive scheduling in make-to-order manufacturing

New algorithms based on mixed integer programming formulations are proposed for reactive scheduling in a dynamic, make-to-order manufacturing environment. The problem objective is to update a long-term production schedule subject to service level and inventory constraints, whenever the customer orders are modified or new orders arrive. Different rescheduling policies are proposed, from a total reschedule of all remaining and unmodified customer orders to a non-reschedule of all such orders. In addition, a medium restrictive policy is considered for rescheduling only a subset of remaining customer orders awaiting material supplies. Numerical examples modeled after a real-world scheduling/rescheduling of customer orders in the electronics industry are presented and some results of computational experiments are reported.

Analysis of periodic and event-driven rescheduling policies in dynamic shops

Abstract We address the problem of rescheduling production systems in the face of dynamic job arrivals. Using simple single-and parallel-machine models to gain insight, we provide worst-case and computational analyses of periodic and event-driven rescheduling policies. Our results indicate that event-driven policies can obtain high-quality schedules with less rescheduling than continuous rescheduling policies. We also show that if structure in the job arrival process is exploited very effective periodic rescheduling policies can be designed.