Baseline Schedule Research Articles

An intelligent scheduling for 5G user plane function placement and chaining reconfiguration

Services and use cases in 5G and beyond networks are characterized by strict requirements such as ultra-low latency, increased capacity, and high user mobility. Moreover, these networks must be capable of satisfying these ambitious demands as well as anticipating and adapting to dynamically changing conditions in a quick and feasible manner. This study deals with the problem of determining the best time to readjust the user plane function (UPF) placement and session mapping configuration to avoid quality of service (QoS) degradation in the system due to user mobility. To this aim, we rely on machine learning (ML) techniques to anticipate poor QoS events and decide whether a reconfiguration procedure is required based on a pre-established QoS tolerance threshold. Specifically, an ML-based framework, called intelligent scheduling of the reconfiguration (ISR), is proposed to automate the reconfiguration process. This framework applies supervised ML methods, either regressors or classifiers, to predict the QoS values/status at a given time horizon. The simulation experiments revealed the proposed mechanism’s superiority compared to the established scheduling baseline. The ISR solution could not only keep the system QoS under desired values most of the time but also reduce the number of readjustment events by at least 50% compared to the baselines.

Preparing Fluid Samples Under Retention Time Constraints Using Flow-Based Microfluidic Biochips

Sample preparation is an essential step in almost all bioprotocols, which can be efficiently achieved via sequence of mixing steps called mixing graph. In literature, several techniques have been reported to determine a mixing graph with the minimal number of mixing steps, or the minimal usage of reagent fluids, or the minimal wastage, or sometime a combination of them. The retention time of an flow-based microfluidic biochip (FMB) is defined as the maximum duration for which a fluid can be stored within a microchannel without any fluid leakage. However, the retention time has not yet been considered as a scheduling constraint during automation of the sample preparation using an FMB in order to obtain the scheduled mixing graphs. In this paper, we propose a retention time aware scheduling method called time aware list scheduling ( TALS), which can be used with the state-of-the-art methods, and a new satisfiability based mixing algorithm called time aware sample preparation ( TASP) to obtain the scheduled mixing graph for a target ratio satisfying the retention time constraint and the number of available on-chip mixers in an FMB. Simulation results suggest that on an average TALS always outperforms a baseline scheduling method, while scheduling any mixing graph. Whereas, TASP can determine the optimal and scheduled mixing graphs compared to the existing mixing methods combined with TALS.

Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach

Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project’s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders’ risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans.

Proactive project scheduling using GPU-accelerated simulations under uncertainty environment

ABSTRACT Numerous scheduling generation and revision methods have been developed for project scheduling under uncertain environments in previous research. However, most of these methods have yet to address a practical project with more than a hundred activities involving multiple decisions in scheduling generation and execution phases. This study proposes a local search-based scheduling method that comprehensively evaluates a baseline schedule considering decision-making in both the planning and execution phases. The proposed method performs simulations to evaluate schedule robustness accurately using GPU to find a locally optimal solution for large problem instances in a reasonable time. A series of numerical experiments demonstrate that the proposed method can generate a robust schedule for large-scale project instances. These results conclude that the proposed method utilizing the simulation-based evaluation and the GPU acceleration is effective and provide insight into developing a scheduling method for practical projects.

Context‐aware application scheduling in fog computing environment

AbstractFog computing emerges as the new computing environment that stays in the proximity of end‐users and harnesses resources at the edge of the network to extend cloud‐facilities. It provides attractive solutions to the diverse range of Internet of Things (IoT) applications by executing them in the vicinity of end‐users. It is challenging to schedule these latency‐sensitive, computation‐intensive, and resource‐hungry applications on distributed, heterogeneous, and resource‐constrained Fog computing environment while ensuring time‐bound service delivery and satisfying Quality of Service (QoS) requirements of end‐users. In this paper, a context‐aware application scheduling technique is proposed for Fog computing environments that employs various parameters of device‐ and application‐level context to minimize service delivery time and satisfy QoS requirements of various IoT applications such as surveillance and game‐based applications. The performance of the proposed technique is evaluated in a simulated Fog environment and compared with baseline application scheduling techniques. The simulation results demonstrate that the proposed context‐aware scheduling techniques result in significant improvement in service delivery time and QoS compared to baseline scheduling techniques.

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.

Data-driven agile software cost estimation models for DHS and DoD

Problem:Since the Manifesto for Agile Software Development, the cost analysis community has struggled to find the most appropriate size measures for developing accurate agile software development cost estimates at an early phase to establish baseline budgets and schedules, and for the selection of competitive bidders. At this time, common agile sizing measures such as Story Points are not practical since these are reported months after contract award in the Department of Homeland Security (DHS) and Department of Defense (DoD). The problem is compounded with the lack of data to build estimation models for informed decisions. Aims:The primary objective is to investigate how well two new size measures (Functional Story and Issues) accurately relate to total effort, and how these compare and rank against four popular software size measures (Story, Story Point, Unadjusted Function Points, and Simple Function Points). The second objective is to rank the six sizing measures based on how well each estimate software development effort at an early phase or after contract award. Method:The experimental framework relied on an analysis of variance and goodness-of-fit tests to examine and compare the accuracy of effort estimation models using six competing size measures. The analysis is based on data from 17 agile projects implemented between 2014 to 2021. Results:Our two new size measures (Functional Story, Issues) proved to be good predicters of total software development effort. Functional Story is better at predicting total effort at early phase than Function Points and Story. Functional Story and Function Points are better at predicting total effort after contract award than Story Point and Issues. Conclusion:The DHS and DoD cost community can choose one or more of these estimation models to evaluate agile software development cost proposals or track agile developer’s progress after contract award.

A bi-layer model for berth allocation problem based on proactive-reactive strategy

To reduce the impact of equipment failure, vessel arrival time delay and other unexpected factors, container terminal managers need to develop a more robust berth scheduling plan while considering the recovery costs. To deal with the berth allocation problem (BAP) with uncertain arrival time, a proactive-reactive hybrid strategy to deal with the impact of uncertainty is proposed. According to the characteristics of BAP and proactive-reactive strategy, a bi-layer programming model is constructed. A robust baseline plan with buffer time is generated in the first layer, and a recovered scheduling plan is generated in the second layer according to the actual arrival time. The decisions between the two layers of models affect each other. In addition, an Adaptive cross-entropy algorithm (ACEA) is designed to solve the first layer model. Different from the traditional cross-entropy algorithm (CEA), an adaptive search strategy is added to further search and optimize the neighborhood of the elite solutions. Furthermore, heuristic adjustment strategies (HAS) are designed to deal with the second layer. The computational results demonstrate that the bi-layer model proposed in this paper can better balance the baseline schedule cost and the recovered schedule cost. Numerical experiments are carried out to assess the effectiveness of the proposed model and the efficiency of the proposed algorithms.

Identifying and Analysing the Root Causes of Quality Non-Conformance in Construction Project Baseline Schedules

Identifying and Analysing the Root Causes of Quality Non-Conformance in Construction Project Baseline Schedules

Tabu search for proactive project scheduling problem with flexible resources

In this paper, we address a proactive resource-constrained project scheduling problem where resources have several skills and can switch the selected skill at discrete time instants. The objective of this paper is to simultaneously determine the starting times of activities and select the skills of resources to maximize the schedule robustness, while satisfying constraints of precedence, renewable resources and a project deadline. After defining the problem, an integer linear programming model is constructed and the studied problem is proven to be NP-hard. Considering the NP-hardness of the problem, we develop a tabu search embedded with two special algorithms to solve the problem. Based on the characteristics of the problem, a resource allocation algorithm for determining the skill for resources is proposed. The two special algorithms are regarded as two improvement measures to combine with the basic tabu search, leading to three versions of the tabu search, i.e., TS, TS-IM1, and TS-IM12. A commercial mathematical programming solver, which can optimally solve the model, is used as a benchmark for comparing the developed algorithm. Through a computational experiment performed on a randomly generated dataset, the effectiveness of the designed algorithms and the two improvement measures is verified, and the results for single-skilled resources as well as the skill switching of flexible resources are discussed. In addition, the effects of the key parameters on the objective function are also analysed. From the computational results, we can draw several conclusions. First, among the three developed algorithms, TS-IM12, which is the tabu search embedded with two improvement measures, performs best, and compared with the commercial software, TS-IM12 shows a higher efficiency in solving the studied problem. Second, the increase in the resource flexibility should be moderate. Third, the robustness of the baseline schedule increases with an increase of the resource flexibility, the resource availability and the project deadline. Finally, the improvement of the schedule robustness brought by the increase of the resource flexibility or the resource availability will be further magnified as the project deadline extends, while the improvement of the schedule robustness caused by the increase of the resource flexibility will be reduced as the resource availability augments.

Integrated proactive-reactive approach and a hybrid adaptive large neighborhood search algorithm for berth and quay crane scheduling under uncertain combination

<p style='text-indent:20px;'>The integrated berth and quay crane scheduling problem is frequently encountered in port management issue, and the dynamic random data obtained by the container terminals under uncertain combination usually makes it unworkable to execute the scheduling. To promote sustainability and adaptivity of container terminals, this paper focuses on developing an integrated proactive-reactive approach for berth and quay crane scheduling under the fluctuation of vessels' arrival time and breakdown of quay crane. The problem is modelled as a two-stage mixed integer program where the baseline schedule with robustness is established in the first stage, and the recovery schedule is established in the second stage. This study aims to minimize the total management cost deviation and total time deviation of the terminal scheduling, so as to implement minimal changes compared with the baseline schedule. Therefore, the scheduling performance such as robustness, recoverability and adaptability is considered. To solve this model, a hybrid adaptive large neighborhood search (HALNS) algorithm framework combined with variable neighborhood search is developed, and the greedy algorithm is developed to construct the initial feasible solution. The performance evaluation tests of the proposed algorithm are performed on the three scales of benchmark. Computational results indicate the strength of solution methods and effectiveness of the proposed model.</p>

Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency.

Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning

AbstractThis proof-of-concept study provides a novel method for robust-stable scheduling in dynamic flow shops based on deep reinforcement learning (DRL) implemented with OpenAI frameworks. In realistic manufacturing environments, dynamic events endanger baseline schedules, which can require a cost intensive re-scheduling. Extensive research has been done on methods for generating proactive baseline schedules to absorb uncertainties in advance and in balancing the competing metrics of robustness and stability. Recent studies presented exact methods and heuristics based on Monte Carlo experiments (MCE), both of which are very computationally intensive. Furthermore, approaches based on surrogate measures were proposed, which do not explicitly consider uncertainties and robustness metrics. Surprisingly, DRL has not yet been scientifically investigated for generating robust-stable schedules in the proactive stage of production planning. The contribution of this article is a proposal on how DRL can be applied to manipulate operation slack times by stretching or compressing plan durations. The method is demonstrated using different flow shop instances with uncertain processing times, stochastic machine failures and uncertain repair times. Through a computational study, we found that DRL agents achieve about 98% result quality but only take about 2% of the time compared to traditional metaheuristics. This is a promising advantage for the use in real-time environments and supports the idea of improving proactive scheduling methods with machine learning based techniques.

A Systematic Review of Earned Value Management Methods for Monitoring and Control of Project Schedule Performance: An AHP Approach

Successful project management depends on ensuring the project’s objectives. Within these objectives, technical success is associated with achieving the expectations of the project baseline. The baseline of the project is made up of the definition of the scope (WBS), time (schedule) and costs (S curve) of the project. Directly, the project is expected to be technically successful if it manages to deliver its full scope on schedule and without associated cost overruns. Baseline performance management is how project managers track and control the progress of deliverables, timelines and associated costs. In a traditional approach, for waterfall-type projects that use the critical path paradigm, the baseline performance management tool par excellence is earned value management (EVM). Earned value management, in practice, works well when project costs are monitored and controlled; however, applying this approach to measure the status of the schedule presents serious inconsistencies. Over the last several decades, different variations of the original earned value have been developed to overcome some of these inconsistencies when used to measure project schedule status. Within these variations, we have the critical path earned value; the work in progress earned value; the critical path earned value and the work in progress; the earned schedule; and the critical path earned schedule. Each of these proposals tries to address some weakness of the original earned value management applied to time monitoring and control, for example, considering critical tasks as a focus on monitoring the progress of the schedule, solving the problem of task recognition late finishers, reporting schedule variances in time units and measuring adherence to the project’s schedule (P factor). Due to the exposed situation, it is necessary to determine which alternative of the versions of the original earned value is the most appropriate for the management of the project schedule, considering that there are several evaluation criteria that must be considered. In the present research, a systematic review and comparison of EVM and its variations for measuring project baseline schedule performance are performed to determine the most suitable methods for monitoring and controlling the project baseline schedule. For this purpose, the analytic hierarchy process (AHP) is used, and five comparison criteria are considered: schedule variation focused on critical tasks, recognition and measurement of the delay of tasks that finish late, schedule variation in time units, measurement of schedule adherence (P factor) and software support and development. The result of the AHP performed for comparing the methods shows that the best method for monitoring and controlling the project baseline schedule is the critical path earned schedule because it behaves adequately in comparison with the other methods for the evaluated comparison categories.

Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model

BackgroundA robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.MethodsAs a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.ResultsAnalyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.ConclusionsThough it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.

A decomposition-based two-stage online scheduling approach and its integrated system in the hybrid flow shop of steel industry

Steelmaking-continuous casting (SCC) is one of the most critical building blocks in the modern steel industry. Many random events occur in the real-world SCC production system. In this paper, we propose a two-stage online scheduling policy that protects the baseline schedule by the slacks provided by intra-flow times and casting speeds. The main task of the two-stage online scheduling model is: (1) to make “here-and-now” decisions for minimizing economic costs and penalties caused by constraint violations; (2) make “wait-and-see” decisions for online scheduling. Afterward, we propose a decomposition-based optimization algorithm that divides the online scheduling problem into a master problem (MP) to seek partial solutions at the last processing stage and a slave problem (SP) to check optimal solutions for upstream processing stages. Then, we employ the IBM ILOG CPLEX to solve MP and use the constraint programming (CP) optimizer to solve SP. Sensitivity analyses and algorithm comparisons are conducted on a set of well-synthetic and realistic instances to validate the proposed model and algorithm. The results show that the proposed online scheduling model and algorithm can solve realistic industrial case studies. Finally, we also develop a scheduling system integrating the proposed model and algorithm.

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.

A genetic algorithm for proactive project scheduling with resource transfer times

In this paper, we investigate the proactive resource-constrained project scheduling problem with resource transfer times under uncertain environment, aiming to generate robust baseline schedules that will be as stable as possible during project execution. The contribution of this paper is threefold. Firstly, the proactive project scheduling problem with resource transfer times is proposed and modeled as an integrated (one-phase) robust project scheduling problem where decisions regarding activity scheduling and resource transfers are simultaneously considered. Secondly, the computational complexity of the proposed problem is proved to be NP-hard in the strong sense, and a genetic algorithm (GA) is developed to solve this problem, in which two measures are proposed to respectively improve the efficiency and the effectiveness of the GA. Thirdly, through analyzing the results from the designed computational experiment, one interesting phenomenon is found that the consideration of breakable flows as well as a local search of resource transfer priority rules in the decoding procedure does not improve schedule robustness so much but costs much more computing time. Another finding is that the resource transfer priority rule we propose, resource transfer efficiency (RTE), is more likely to obtain the optimal solutions for the tested PSPLIB instances.

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

Real-life project scheduling environments are often dynamic and subject to disruption. Early or late completion of interrupted projects can create costs for the business. At the same time, there is an alternative to producing multiple projects at multiple different costs. In this study, a new mixed integer linear programming model that minimizes the sum of weighted earliness and tardiness penalties and mode selection costs is proposed to solve the real-life problem faced by a boutique furniture company. A proposed dynamic model also considers the cost of deviation from the baseline schedule in case disruption scenarios corrupt the resulting baseline schedule. The problems are solved with the CPLEX solver using the GAMS program. The results show that the interruption scenarios partially change the baseline schedule and increase the total cost. In case of more than one interruption in the same schedule, the number of late completed activities and their delay times increased.

Disruption Management Approaches for Berth Scheduling in Bulk Terminals

Maritime terminals are complex transportation systems sensitive to several sources of uncertainty. An alteration in the baseline planning, as a consequence of one or more disruptive events, can lead terminals to a lower quality of the service provided. Therefore, in a context where a terminal strives to maintain or increase competitiveness, it is necessary to consider the uncertainty in the planning and define actions capable of efficiently and effectively mitigating disruptive events. This paper addresses berthing operations at bulk terminals, considering the arrival and handling times as stochastic variables. Hybrid approaches (i.e., proactive-reactive) are proposed in order to provide the port terminal with robust planning capable of reducing the impact of disruptive events by defining uncertainty-tolerant schedules and reactive actions capable of restoring the performance of the terminal when disruptive events arise. Finally, the solution approaches are evaluated together with and without the incorporation of buffer-time management. The computational results corroborate the effectiveness of integrating proactive and reactive approaches in order to maximize the performance of the terminal and reduce the penalty costs derived from alterations in the baseline schedule, with the consequent increase in the terminal competitiveness.