Practical Scheduling Research Articles

4-110 色や音に対する「感性」を育てる演習授業 : 運用スケジュールの効率化を目指して((04)工学教育の個性化・活性化-II,口頭発表)

4-110 色や音に対する「感性」を育てる演習授業 : 運用スケジュールの効率化を目指して((04)工学教育の個性化・活性化-II,口頭発表)

Minimizing earliness and tardiness costs in scheduling jobs with time windows

Motivated by the practical scheduling problem of facilities in an interrelated supply chain network, this study addresses the scheduling problem of jobs on unrelated parallel machines with sequence-dependent setup times, as well as machine- and job-dependent processing rates. Each job includes a time window delimited by its ready and due dates. For a manufacturer, the ready date of a job refers to the delivery date for the job’s raw materials promised by its upstream supplier, whereas the due date of the job is the date the manufacturer promised to deliver to its downstream customer. Ready and due dates are the parameters of the scheduling problem of the manufacturer and the processing times in which all orders are expected to lie within their time window. Finding a feasible schedule is sufficient for most manufacturers who disregard the objective functions commonly seen in existing literatures. If such a feasible schedule is not possible, suggestions for re-negotiation with the supplier and/or buyer are calculated using the proposed method which minimizes earliness and tardiness costs.To the best of our knowledge, no prior research has thoroughly addressed this scheduling problem. Three methods based on the mixed integer programming (MIP), namely, the hard-constraint mixed integer programming (HCMIP), the earliness-tardiness mixed integer programming (ETMIP), and the three-phase heuristics-initialized mixed integer programming (HIMIP), are proposed and tested. The experiment results show that HIMIP is the most effective in solving the problem.

Acquisition, retention and transfer of simulated laparoscopic tasks using fNIR and a contextual interference paradigm

BackgroundUsing functional near infrared spectroscopy, a noninvasive, optical brain imaging tool that monitors changes in hemodynamics within the prefrontal cortex (PFC), we assessed performance and cognitive effort during the acquisition, retention and transfer of multiple simulated laparoscopic tasks by novice learners within a contextual interference paradigm. MethodsThird-year medical students (n = 10) were randomized to either a blocked or random practice schedule. Across 3 days, students performed 108 acquisition trials of 3 laparoscopic tasks on the LapSim® simulator followed by delayed retention and transfer tests. Performance metrics (Global score, Total time) and hemodynamic responses (total hemoglobin (μm)) were assessed during skill acquisition, retention and transfer. ResultsAll acquisition tasks resulted in significant practice schedule X trial block interactions for the left medial anterior PFC. During retention and transfer, random performed the skills in less time and had lower total hemoglobin change in the right dorsolateral PFC than blocked. ConclusionsCompared with blocked, random practice resulted in enhanced learning through better performance and less cognitive load for retention and transfer of simulated laparoscopic tasks.

Towards Practical and Near-Optimal Coflow Scheduling for Data Center Networks

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop . As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS , which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.

On the Link Scheduling for Cellular-Aided Device-to-Device Networks

We consider a cellular-aided inband overlay device-to-device (D2D) network, where a base station (BS) and all devices share a frequency band for their communications, but the BS allocates dedicated radio resources to D2D direct communications to avoid the mutual interference between D2D and cellular communications. We first mathematically formulate the optimal sum rate of the D2D network, and provide closed-form approximations of the average sum rate for low and high signal-to-noise ratio (SNR) regimes. Furthermore, we propose two practical D2D link scheduling algorithms: centralized and distributed. The centralized algorithm reduces the computational complexity at the BS, and its performance is shown to be optimal since the SNR of D2D links tends to be either zero or infinity. The distributed algorithm significantly reduces the signaling overhead caused by channel state information feedback from devices to the BS, and the performance loss of the distributed algorithm is marginal compared with that of the centralized algorithm, particularly when the number of D2D pairs is small.

Discrete Firefly Algorithm for Scaffolding Construction Scheduling

Scaffolding activities involve a considerable amount of resource input and effort, particularly in very large scale and complex projects. Based on one of the present research gaps identified from literature review that very limited research emphasis has been placed on the impact of design of time-cost optimization in modular scaffolding construction process, this study aims to formulate a feasible multiobject discrete firefly algorithm (MDFA) for optimizing scaffolding project resource and scheduling schemes. The proposed MDFA has been tested under a scaffolding-specific case and a generic construction project case and manifested that it can produce accurate and effective solutions to assist scaffolding planners in developing practical project schedules and addressing complex time-cost trade-off challenges.

Enhanced Retention Performance Modeling for Intelligent Tutoring System

The aim of Personalized Adaptive Scheduling System is not only to maximize learning, but also to minimize the tendency to forget. Empirical studies from psychology and cognitive theory of memory indicate that repetition with gradually increasing spacing interval is necessary to promote optimal long-term retention. While all individuals differ in their capacity to learn and retain information, the existing personalized systems attempt to model student retention performance based on learning capabilities only. The present study aims to investigate the extent to which individual differences in learning and forgetfulness help in predicting retention performance, which is essential for designing personalized retrieval practice schedules for a student. This study was conducted using data from Personalized Adaptive Scheduling System in ASSISTments, an adaptive mathematical tutor. The results illustrate the importance of student learning and forgetfulness features in predicting retention performance of a new skill, the proposed prediction model showed a significant improvement from an R of 0.2975 with an existing baseline model to an R value of 0.3989. In addition the newly identified features are used to predict the retention interval of a student, since ideal personalized retention schedules should be roughly equal to the retention interval of the student. The predictive accuracy of the linear regression model thus obtained was found to be statistically significant with an R value of 0.413.

A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

Self-adaptive ruin-and-recreate algorithm for minimizing total flow time in no-wait flowshops

This paper studies the no-wait flowshop scheduling problem with total flow time criterion, which is NP-complete in the strong sense. A self-adaptive ruin-and-recreate (SR&R) algorithm is proposed to solve this complex problem. The performance of the proposed SR&R algorithm is compared with that of the best available heuristics and the SR&R algorithm without the self-adaptive mechanism by application to a set of classic benchmark instances that were presented by Taillard. Computational results show that the proposed SR&R algorithm improves upon the best known solutions in more than half benchmark instances, and provides the best known solutions for the remaining unimproved instances in a reasonable computational time. The contribution of this work is to provide an easy-to-use approach to solve effectively and efficiently this practical but complex scheduling problem.

The Effect of Blocked, Random and Mixed Practice Schedules on Speech Motor Learning of Tongue Twisters in Unimpaired Speakers.

There are few investigations comparing practice schedules in speech motor learning, despite certain schedules being recommended for the clinical treatment of speech motor disorders. This study compared effects of random, blocked and mixed practice on tongue twister accuracy in unimpaired speakers. We hypothesized that blocked practice would benefit acquisition of learning, but that random practice and mixed blocked-then-random practice would yield superior retention of learning. We found that the random and blocked-random practice schedules yielded superior accuracy at the end of the acquisition phase of learning and at a 1-week retention test. Exploratory post hoc analyses raised the possibility that the retention effects were most evident when tongue twisters were elicited in a random schedule. Theoretical accounts of these results are discussed.

Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance.

Repetition is the most commonly used practice strategy by musicians. Although blocks of repetition continue to be suggested in the pedagogical literature, work in the field of cognitive psychology suggests that repeated events receive less processing, thereby reducing the potential for long-term learning. Motor skill learning and sport psychology research offer an alternative. Instead of using a blocked practice schedule, with practice completed on one task before moving on to the next task, an interleaved schedule can be used, in which practice is frequently alternated between tasks. This frequent alternation involves more effortful processing, resulting in increased long-term learning. The finding that practicing in an interleaved schedule leads to better retention than practicing in a blocked schedule has been labeled the “contextual interference effect.” While the effect has been observed across a wide variety of fields, few studies have researched this phenomenon in a music-learning context, despite the broad potential for application to music practice. This study compared the effects of blocked and interleaved practice schedules on advanced clarinet performance in an ecologically valid context. Ten clarinetists were given one concerto exposition and one technical excerpt to practice in a blocked schedule (12 min per piece) and a second concerto exposition and technical excerpt to practice in an interleaved schedule (3 min per piece, alternating until a total of 12 min of practice were completed on each piece). Participants sight-read the four pieces prior to practice and performed them at the end of practice and again one day later. The sight-reading and two performance run-throughs of each piece were recorded and given to three professional clarinetists to rate using a percentage scale. Overall, whenever there was a ratings difference between the conditions, pieces practiced in the interleaved schedule were rated better than those in the blocked schedule, although results varied across raters. Participant questionnaires also revealed that the interleaved practice schedule had positive effects on factors such as goal setting, focus, and mistake identification. Taken together, these results suggest that an interleaved practice schedule may be a more effective practice strategy than continuous repetition in a music-learning context.

Effectiveness of methotrexate with step-down glucocorticoid remission induction (COBRA Slim) versus other intensive treatment strategies for early rheumatoid arthritis in a treat-to-target approach: 1-year results of CareRA, a randomised pragmatic open-label superiority trial

ObjectivesCombining disease-modifying antirheumatic drugs (DMARDs) with glucocorticoids (GCs) is an effective treatment strategy for early rheumatoid arthritis (ERA), yet the ideal schedule and feasibility in daily practice are debated. We...

Graph multi-coloring for a job scheduling application

In this paper, we introduce a graph multi-coloring problem where each vertex must be assigned a given number of different colors, represented as integers, and no two adjacent vertices can share a common color. In the variant considered, the number of available colors is such that not all vertices can be colored. Furthermore, there is a bound on the number of vertices which can be assigned the same color. A gain is associated with each vertex and the first objective is to maximize the total gain over all colored vertices. Secondary objectives consider the sequence of colors assigned to each vertex. More precisely, the range and the number of interruptions must be minimized, where the range corresponds to the difference between the largest and smallest colors assigned to a vertex. This variant of the graph multi-coloring problem is of interest because it can model practical job scheduling applications. An integer linear programming formulation is first proposed to address small-size instances. A construction heuristic, as well as local search methods, are then reported to tackle larger instances. The local search methods are based on several neighborhood structures, each one focusing on a specific property of the problem. Different ways to combine these neighborhood structures are also investigated.

PSBS: Practical Size-Based Scheduling

Size-based schedulers have very desirable performance properties: optimal or near-optimal response time can be coupled with strong fairness. Despite this, however, such systems are rarely implemented in practical settings, because they require knowing a priori the amount of work needed to complete jobs: this assumption is difficult to satisfy in concrete systems. It is definitely more likely to inform the system with an estimate of the job sizes, but existing studies point to somewhat pessimistic results if size-based policies use imprecise job size estimations. We take the goal of designing scheduling policies that explicitly deal with inexact job sizes . First, we prove that, in the absence of errors, it is always possible to improve any scheduling policy by designing a size-based one that dominates it: in the new policy, no jobs will complete later than in the original one. Unfortunately, size-based schedulers can perform badly with inexact job size information when job sizes are heavily skewed; we show that this issue, and the pessimistic results shown in the literature, are due to problematic behavior when large jobs are underestimated. Once the problem is identified, it is possible to amend size-based schedulers to solve the issue. We generalize FSP—a fair and efficient size-based scheduling policy—to solve the problem highlighted above; in addition, our solution deals with different job weights (that can be assigned to a job independently from its size). We provide an efficient implementation of the resulting protocol, which we call Practical Size-Based Scheduler (PSBS). Through simulations evaluated on synthetic and real workloads, we show that PSBS has near-optimal performance in a large variety of cases with inaccurate size information, that it performs fairly and that it handles job weights correctly. We believe that this work shows that PSBS is indeed pratical, and we maintain that it could inspire the design of schedulers in a wide array of real-world use cases.

Retention of Mastoidectomy Skills After Virtual Reality Simulation Training.

The ultimate goal of surgical training is consolidated skills with a consistently high performance. However, surgical skills are heterogeneously retained and depend on a variety of factors, including the task, cognitive demands, and organization of practice. Virtual reality (VR) simulation is increasingly being used in surgical skills training, including temporal bone surgery, but there is a gap in knowledge on the retention of mastoidectomy skills after VR simulation training. To determine the retention of mastoidectomy skills after VR simulation training with distributed and massed practice and to investigate participants' cognitive load during retention procedures. A prospective 3-month follow-up study of a VR simulation trial was conducted from February 6 to September 19, 2014, at an academic teaching hospital among 36 medical students: 19 from a cohort trained with distributed practice and 17 from a cohort trained with massed practice. Participants performed 2 virtual mastoidectomies in a VR simulator a mean of 3.2 months (range, 2.4-5.0 months) after completing initial training with 12 repeated procedures. Practice blocks were spaced apart in time (distributed), or all procedures were performed in 1 day (massed). Performance of the virtual mastoidectomy as assessed by 2 masked senior otologists using a modified Welling scale, as well as cognitive load as estimated by reaction time to perform a secondary task. Among 36 participants, mastoidectomy final-product skills were largely retained at 3 months (mean change in score, 0.1 points; P = .89) regardless of practice schedule, but the group trained with massed practice took more time to complete the task. The performance of the massed practice group increased significantly from the first to the second retention procedure (mean change, 1.8 points; P = .001), reflecting that skills were less consolidated. For both groups, increases in reaction times in the secondary task (distributed practice group: mean pretraining relative reaction time, 1.42 [95% CI, 1.37-1.47]; mean end of training relative reaction time, 1.24 [95% CI, 1.16-1.32]; and mean retention relative reaction time, 1.36 [95% CI, 1.30-1.42]; massed practice group: mean pretraining relative reaction time, 1.34 [95% CI, 1.28-1.40]; mean end of training relative reaction time, 1.31 [95% CI, 1.21-1.42]; and mean retention relative reaction time, 1.39 [95% CI, 1.31-1.46]) indicated that cognitive load during the virtual procedures had returned to the pretraining level. Mastoidectomy skills acquired under time-distributed practice conditions were retained better than skills acquired under massed practice conditions. Complex psychomotor skills should be regularly reinforced to consolidate both motor and cognitive aspects. Virtual reality simulation training provides the opportunity for such repeated training and should be integrated into training curricula.

Contextual Interference Can Facilitate Motor Learning in Older Adults and in Individuals With Parkinson's Disease

ABSTRACTIn healthy young adults motor learning is typically improved by practice under conditions of high contextual interference such as that created when a series of skills are practiced in a random sequence. The authors examined whether individuals with Parkinson's disease and age-matched older adults might also benefit from such a learning environment. Participants learned 3 peg placement movements under both random practice and blocked practice schedules. Participants completed free-recall, cued-recall, and transfer tests 1 day and 1 week following practice. During acquisition all learners had shorter movement times (MTs) during blocked than random practice but during retention testing, MTs were shorter if the patterns had been learned with random practice. Fewer errors during free recall following random practice were evident in both groups of learners and MTs to complete a novel transfer task were shorter following random practice. These findings suggest employing a cognitively challenging practice environment might improve motor skill learning in elderly learners and in people with Parkinson's disease.

Retention of the spacing effect with mental practice in hemiparetic stroke.

Mental practice (MP) is a promising adjuvant to physical practice that involves many of the same mechanisms and takes on many of the same properties as physical practice. This study compared efficacy of a "massed" MP regimen versus a "distributed" MP regimen on upper extremity (UE) motor impairment and functional limitation. Twenty-seven chronic stroke survivors were administered the UE section of the Fugl-Meyer (FM) and Action Research Arm Test (ARAT), followed by standardized physical practice and MP regimens. One group was administered "massed" MP (60min of MP during a single daily session) and a second group administered distributed MP (20min of MP occurring three times/day). After intervention, changes in FM and ARAT scores of subjects in the distributed condition were significantly higher than those of subjects in the massed condition (FM 3.65, 95% CI 0.82-6.49, p value=0.01; ARAT 3.95, 95% CI 1.24-6.67, p value=0.006). Likewise, at POST 3, subjects in the distributed group showed significantly higher change in ARAT scores (ARAT 4.90, 95% CI 0.57-9.22, p value=0.03); the change in FM scores at POST 3 was 3.18 points higher among subjects in the distributed condition, but only approached significance (95% CI -1.27 to 7.63, p value=0.15). Results suggest that a distributed MP schedule is more efficacious in bringing about paretic UE changes than a massed practice schedule, especially in terms of reducing UE functional limitation.

Embouchure Dysfunction in Air Force Band Brass Musicians.

Occupational injuries and medical problems in musicians are well described, but relatively less attention has been paid to orofacial and embouchure-related problems in professional brass players. This study addressed embouchure-related problems in Air Force Band members, a population of musicians with an intense practice and performance schedule. A survey was developed and distributed via the Air Force Survey Office to 599 active-duty Air Force Band members and 201 Air National Guard members. The survey assessed practice patterns, practice and performance venues, and presence of symptoms suggesting embouchure dysfunction. Responses were obtained from 167 Air Force Band brass players. Of the 157 responding to the question about embouchure dysfunction, 42% reported having experienced an embouchure problem at some point in the past, and 53% of those respondents reported that they were currently experiencing an embouchure problem. Forty-one percent of those with embouchure problems cited practice venues that were not conducive to effective and efficient practice at the time their embouchure problems began, and 48% of those with embouchure problems reported having to overblow in rehearsal at the time their problems began. Embouchure disorders were reported in a large proportion of Air Force Band brass survey respondents, and specific concerns related to practice venues and the need to overblow in practice settings suggest factors suitable to remediation and preventive strategies.

Minimising total weighted earliness and tardiness penalties on identical parallel machines using a fast ruin-and-recreate algorithm

This paper studies the scheduling problem of minimising total weighted earliness and tardiness penalties on identical parallel machines against a restrictive common due date. This problem is NP-hard in the strong sense and arises in many just-in-time production environments. A fast ruin-and-recreate (FR&R) algorithm is proposed to obtain high-quality solutions to this complex problem. The proposed FR&R algorithm is tested on a well-known set of benchmark test problems that are taken from the literature. Computational results provide evidence of the efficiency of FR&R, which consistently outperform existing algorithms when applied to benchmark instances. This work provides a viable alternative approach for efficiently solving this practical but complex scheduling problem.

Flexible scheduling, degradation of job quality and barriers to collective voice

This article examines the operation of flexible scheduling in practice through a case study of a large retail firm in the United Kingdom. It includes analysis of 39 semi-structured interviews, participant observation of shop floor work and non-participant observation of union organizing as well as analysis of key documents. The findings highlight the high level of generalized temporal flexibility across employment statuses. This temporal flexibility enables firm flexibility without necessitating a reliance upon contingent workers. Temporal flexibility is found to entail manager-control of flexible scheduling and is shown to be damaging to perceptions of job quality as it acts as a barrier to work-life balance. Union presence and collective bargaining at the firm are found to be ineffective at influencing flexible scheduling so as to improve job quality. This ineffectiveness can be explained by the union operating in an employer-dominated industrial relations environment in which its associational power is unable to compensate for a lack of institutional and structural economic power.