Good Scheduling Research Articles

Adaptive Genetic Algorithm for High School Time-Table

Time-table scheduling in senior high school is complex combinatorial problem. The scheduling process needs much time and susceptible to human error. This human error can lead to the violation of hard constraints. Beside the hard constraints, the good schedule must minimize the violation of soft constraint to ensure the students’ and teachers’ preference can be satisfied. The aim of this study was to build the automated scheduling process using adaptive genetic algorithm (AGA). The AGA implements in this study to flexible the probability of mutation (pm) based on the fitness. The minimum pm was set to 0.01, while the maximum pm was 0.2. The experiment shows that the AGA give the better fitness compared to the original GA. The best fitness reached by AGA was 0.054 with the average of maximum generation was 402. The original GA with fixed pm 0.1 resulted best fitness 0.045 and the average generation was 387.

The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

SummaryScheduling is a widely used method in parallel computing, which assigns tasks to compute resources of a parallel environments. In this article, we consider independent parallel tasks to be scheduled onto a heterogeneous execution platform consisting of a set of multicores of different architecture. Each parallel task has an internal potential parallelism which allows a parallel execution on any multicore processors. However, the execution time may differ due a different computation speed of different multicores. In this article, we propose a new search‐based scheduling algorithm Heterogeneous Parallel task scheduling based on A* (called HP*) to solve the problem of scheduling independent parallel tasks onto heterogeneous multicore platforms. Specifically, we propose a heuristic cost function needed for an informed search. Also, three pruning techniques are proposed, which are shown to significantly reduce the search space of HP*. Performance measurements on a heterogeneous platform are performed and the results of HP* are compared to scheduling results of other popular scheduling methods. The performance results with benchmark tasks from the SPLASH‐3 benchmark suite demonstrate the good scheduling results and the improvements achieved by HP*.

A feedback-directed method of evolutionary test data generation for parallel programs

Abstract Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation.

Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction

In this paper, we address the dynamic single-machine scheduling problem for minimisation of total weighted tardiness by learning of dispatching rules (DRs) from schedules. We propose a decision-tree-based approach called Generation of Rules Automatically with Feature construction and Tree-based learning (GRAFT) in order to extract dispatching rules from existing or good schedules. GRAFT consists of two phases: learning a DR from schedules, and improving the DR with feature-construction-based genetic programming. With respect to the process of learning DRs from schedules, we present an approach for transforming schedules into training data containing underlying scheduling decisions and generating a decision-tree-based DR. Thereafter, the second phase improves the learned DR by feature-construction-based genetic programming so as to minimise the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach, and the results showed that it outperforms the existing dispatching rules. Moreover, the proposed algorithm is effective in terms of extracting scheduling insights in such understandable formats as IF–THEN rules from existing schedules and improving DRs by grafting a new branch with a discovered attribute into a decision tree.

Penjadwalan Kompetisi Sepak Bola Liga PSN Ngada Menggunakan Algoritma Honey Bee Mating Optimization

Penjadwalan sepak bola menjadi salah satu factor kunci agar sebuah kompetisi yang diselenggarakan dapat berjalan baik atau tidak. Penjadwalan yang kurang baik akan mampu mempengaruhi kondisi tim yang akan bertanding, karena jadwal yang kurang baik akan menghambat tim untuk mempersiapkan timnya secara baik untuk pertandingan selanjutnya. Sehingga dalam penelitian ini akan merancang sistem yang dapat menentukan penajadwalan secara baik berdasarkan batasan masalah yang sebaiknya dihindari dalam menjadwalkan kompetisi sepak bola liga PSN Ngada fase 10 besar yang nmenggunakan sistem setengah kompetisi. Pada penelitian ini akan menggunakan algoritma honey bee mating optimization, algoritma ini dipilih karena mudah untuk diterapkan dan dapat menghasilkan penjadwalan yang cukup baik. Algoritma ini memungkinkan terbentuknya penjadwalan yang menghidari batasan yang telah ditentukan dari kombinasi yang telah dibentuk, yang telah melewati proses flight mating, crossover, mutasi dan pemilihan ratu. Siklus ini akan terus berlangsung hingga mencapai iterasi yang ditentukan dan memilih penjadwalan dengan nilai fitness terbaik.

Design optimal sampling plans for functional regression models

Functional regression models are widely considered in practice. To make a precise statistical inference, a good sampling schedule for collecting informative functional data is needed. However, there has not been much research on the optimal sampling schedule design for functional regression model so far. To address this design issue, an efficient computational approach is proposed for generating the best sampling plan in the function-on-function linear regression setting. The obtained sampling plan allows a precise estimation of the predictor function and a precise prediction of the response function. The proposed approach can also be applied to identify the optimal sampling plan for the problem with scalar-on-function linear regression model. Through case studies, this approach is demonstrated to outperform the methods proposed in the previous studies.

Packet Scheduling in Multipath TCP: Fundamentals, Lessons, and Opportunities

Multipath transmission control protocol (MPTCP) is a transport protocol that allows the simultaneous use of multiple transmission control protocol subflows across the existing IP addresses between peers. Since each subflow undergoes the bottleneck link condition of its path, selecting the best subflow to schedule an outgoing packet plays a key role in the multipath performance. While good scheduling decisions can significantly improve throughput, wrong decisions prevent users from benefiting the aggregating capacities of available subflows. To deal with this concern, in the last years, several scheduling solutions were proposed to achieve different goals and applications. Different from other surveys and tutorials, in this article, we provide a tutorial of packet scheduling in the MPTCP that brings not only its fundamentals but also a detailed analysis of multipath performance from a consistent experimental methodology. In a multipath network setup, we compare single-criterion and multicriteria schedulers under different bottleneck conditions and multipath congestion controls. From a comprehensive experimental analysis that unveils several performance issues, we discuss the lessons learned and research opportunities regarding the multipath throughput improvement as the central motivation. In this way, this article provides a comprehensive analysis of packet scheduling in the MPTCP, not only in theory but in practice as well.

Efficient scheduling and resource allocation in 802.11ax multi-user transmissions

802.11ax introduces OFDMA to WiFi. It thus enables multiplexing users/user groups in the frequency domain. WiFi networks usually operate in a multipath environment which generates a frequency selective channel. Hence, the capacity of a user/user group changes over different subcarriers. A good scheduling and resource allocation scheme can maximize user rates by allocating users and user groups on subcarriers based on their CSI and other system considerations.In this paper, we investigate how to optimally assign users and user groups to subcarriers with the goal of maximizing the throughput in the context of 802.11ax. We introduce a novel divide and conquer based algorithm which we prove to be optimal under the assumption that a user can be assigned to more than one resource unit. We also introduce two practical algorithms, a greedy and a recursive one, which split the bandwidth into resource units and schedule users on them while observing all 802.11ax protocol constraints. Extensive simulations and experiments comparing the performance of the aforementioned algorithms establish that our practical schemes achieve very good performance in all studied scenarios while handling a plethora of real-world constraints.

Production scheduling problem in a factory of automobile component primer painting

In a factory of automobile component primer painting, various automobile parts are attached to overhead hangers in a conveyor line and undergo a series of coating processes. Thereafter, the components are wrapped at a packaging station. The packaging process should be fully balanced by an appropriate sequence of components to prevent the bottleneck effect because each component requires different packaging times and materials. An overhead hanger has a capacity limit and can hold varying numbers of components depending on the component type. Capacity loss can occur if the hanger capacity is not fully utilized. To increase hanger utilization, companies sometimes mix two or more component types on the same hangers, and these hangers are called mixed hangers. However, mixed hangers generally cause heavy workload because different items require additional setup times during hanging and packing processes. Hence, having many mixed hangers is not recommended. A good production schedule requires a small number of mixed hangers and maximizes hanger utilization and packaging workload balance. We show that the scheduling problem is NP-hard and develop a mathematical programming model and efficient solution approaches for the problem. When applying the methods to solve real problems, we also use an initial solution-generating method that minimizes the mixing cost, set a rule for hanging the items on hangers considering eligibility constraint, and decrease the size of tabu list in proportion to the remaining computational time for assuring intensification in the final iterations of the search. Experimental results demonstrate the effectiveness of the proposed approaches.

Risk mitigation on product distribution and delay delivery : A case study in an Indonesian manufacturing company

In a dynamic supply chain environment, problems that arise due to delays in the delivery of raw materials and distribution of products to consumers can pose risks and threats to the company. This study aims to identify and mitigate the risk of late delivery of goods. Risk management starts with identifying sources of risk, defining risks, creating risk maps and mitigating risks. a case study conducted at a manufacturing packaging company in Batam, Indonesia. The results showed that the biggest risks were supplier production capacity, supplier product quality, forecasting from consumers, daily finished good delivery schedule to consumers and loading and unloading processes at consumers. This research proposes mitigation to minimize the impact of short-term and long-term risks.

Impact on mental health by "Living in Isolation and Quarantine" during COVID-19 pandemic.

Impact on mental health by "Living in Isolation and Quarantine" during COVID-19 pandemic.

A review of job shop scheduling problems in multi-factories

The distributed job shop scheduling problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. It is one of the well-known NP-hard combinatorial optimisation problem to solve optimally. In the last two decades, the problem has captured the interest of a number of researchers and therefore various methods have been employed to study this problem. In this paper, we first present an overview of pioneer studies conducted on solving distributed job shop scheduling problems and a classification of the employed techniques is given. Then, depth analysis of the outcome of existing literature is presented.

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.

Implementation of Hydraulic Fracturing Operation for a Reservoir in KRG

This study focuses on procedures to enhance permeability and flow rate for a low permeability formation by creating a conductive path using the hydraulic fracturing model. Well data are collected from the Qamchuqa KRG oil field formation. A Fracpro simulator is used for modelling the hydraulic fracturing process in an effective way. The study focuses on an effective hydraulic fracturing design procedure and the parameters affecting the fracture design. Optimum design of fracturing is achieved by selecting the proper fracturing fluid with a suitable proppant carried in a slurry, determining the formation fracturing pressure, selection of a fracture propagation fluid, and also a good proppant injection schedule, using a high pump rate and good viscosity. Permeability and conductivity are calculated before and after applying the hydraulic fracturing. Fracture height, length, and width are calculated from the Fracpro software, among other parameters, and the production rate changes. From the results, it is observed that by using hydraulic fracturing technology, production will increase and permeability will be much higher. The original formation permeability is 2.55 md, and after treatment, the average fracture conductivity has significantly increased to 1742.3 md-ft. The results showed that average fracture width is 0.187 inch. The proppant used in this treatment has a permeability of 122581 md. The suitable fluid choice is hyper with an apparent viscosity of 227.95 cp, and the proper proppant type is Brady sand with a conductivity of 2173.41 md-ft. Fracture orientation from the Khurmala oil field in Kurdistan is vertical fractures produced at a depth of 1868 m. Fracture half-length, total fracture height, and average fracture width are 220 ft, 42 ft, and 0.47 inch, respectively. After fracturing, the maximum and average area of fracture are 33.748 and 17.248 ft2, respectively. The recommended pump hydraulic horse power is 3200 HHP, and the total required fluid is 1076.3 bbl. In this study, hydraulic fracture is designed, and then, it has been analyzed after that production is optimized.

Hemoadsorption by extracorporeal cytokine adsorption therapy (CytoSorb®) in the management of septic shock: A retrospective observational study.

Sepsis results in immunologic disturbances with the release of various inflammatory mediators such as cytokines. Cytokines can damage the cells, and the continuous release of inflammatory mediators leads to severely impaired immunity. Therefore, the reduction in cytokine levels by hemoadsorption represents a new concept for blood purification. CytoSorb® as a hemoadsorption device is a detoxification system, which aims to decrease the cytokines levels. This study was conducted to understand any beneficial effects of CytoSorb® therapy in septic patients. This was a retrospective and observational study, approved by the scientific and ethics committee of Max Super Specialty Hospital, Patparganj, Delhi, India and conducted in compliance with current International Council for Harmonization, Good Clinical Practice, Schedule Y, and Indian Council of Medical Research guidelines. Subjects of either gender (age > 18 year) were included in the study. The data were presented as mean ± standard deviation and categorical as frequency and percentage (%). A p value less than 0.05 (p < 0.05) was considered to be statistically significant. A total number of 36 patients were included in the study. Majority of the patients were male with mean age (56.36 ± 14.83). After therapy, procalcitonin and total leucocyte count levels decreased within 24 h. Post therapy, sepsis-related organ failure assessment (SOFA) score of Day (D)1, D2, and D3 reduced to 10.4 ± 3.63, 8.7 ± 4.02, and 7.8 ± 3.67, respectively. The Acute Physiology and Chronic Health Evaluation (APACHE) II score and predicted mortality were lower in the survivor group as compared to the non-survivor group. Hemoadsorption using the extracorporeal adsorption device (CytoSorb®) might be an effective rescue therapy in stabilizing septic shock patients.

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing.

Age-group differences in associations of working hour characteristics and short sickness absences

Abstract Background Working-hour characteristics are linked to disturbed sleep, but little is known about their association with short sickness absences (SA). We investigated the association between working hour characteristics in shift work and the incidence of short (1-3 days) SA in three age groups. Methods The data was from the hospitals’ electronic working time records. The final sample was restricted to the first incidence of short SA (1-3 days) from 2008 to 2015 and to shift working employees (n = 12156, 89% women). Age groups were &amp;lt; 35 (n = 5652), 35-49 (n = 3592) and ≤ 50 years (n = 2545). Work shifts (morning, day, evening, and night), quick returns (&amp;lt; 11 hours between two work shifts), length of work shift and weekly working time were calculated for case-crossover study design to compare the working hour characteristics of the 28 days preceding SA (exposure window) and those of the 28 days before the exposure window (control window). Conditional logistic regression models were run for odds ratios (OR) with 95% Confidence Intervals (95%CI). Results Working hour characteristics, especially length of working hours, played the clearest role among those &amp;lt; 35 years of age. In this age group, exposure to &amp;gt; 25% of &amp;gt; 40 weekly working hours associated with an increased likelihood of short SA (OR 1.30, 95%CI 1.17-1.44), whereas weekly working hours had OR 1.02 (95%CI 1.02-1.03). Having only a few (≤ 4) quick returns decreased the likelihood of SA (ORs 0.31-0.54), whereas having ≥25 quick returns increased it (OR 2.75, 95%CI 2.44-3.11). The results showed mostly similar trends among those of 35-49 years of age whereas among older workers (≤ 50 years) only an association for quick returns was observed. Conclusions Long working hours increase the risk of short SA among those ≤ 49 years of age and several quick returns among all age groups. These working-hour characteristics should be avoided to minimize SA and increased costs for the employer, but also to maintain public health. Key messages Unhealthy working-hour characteristics should be paid special attention in shift scheduling to avoid sickness absence. Good shift scheduling could assist in maintaining workability and therefore public health.

Hybrid Invasive Weed Optimization with Greedy Algorithm for an Energy and Deadline Aware Scheduling in Cloud Computing

Cloud computing is being envisioned to be the computing paradigm of the next generation primarily for its advantages of on-demand services, risk transference, resource pooling that is independent of location and ubiquitous network access. Service quality is allocated using various resources in the scheduling process. The deadline refers to the time period from task submission until task completion. An algorithm that has good scheduling attempts at keeping the task executed inside the constraint of the deadline. The Genetic Algorithm (GA) is a common metaheuristic that is used often in literature for procuring solutions that are either optimal or near-optimal. The Invasive Weed Optimization (IWO) is an evolutionary algorithm that is population-based with certain interesting specifications like creations of offspring that are based on the levels of fitness of the parents which increases the size of the population and generates new population by making use of the best among parents and the best among off-springs. The Greedy Algorithms will construct an object that is globally best by means of continuously choosing the option that is locally the best. In this work, a hybrid GA with the Greedy Algorithm and a Hybrid IWO with the Greedy Algorithm that has been proposed for the energy and the deadline-aware scheduling in cloud computing.

Ensemble of constraint-handling techniques for solving reservoir scheduling problems

Abstract Reservoir scheduling based on evolutionary algorithms needs to handle potentially stringent physical and operational constraints. Both generic and reservoir scheduling problem-specific constraint-handling techniques (CHTs) have their own merits and limitations. No CHT currently available can yield better solutions than the others consistently. To ensure good reservoir operation schedules, we develop an ensemble of CHTs (ECHT) that can utilize the advantages of different individual CHTs. In the ensemble, each CHT has its own population. In every generation, the different offspring populations are mixed together and evaluated. Each CHT then assigns fitness to all individuals and selects some of them to form its new parent population. The ECHT has been tested against long-term hydropower scheduling of two large-scale reservoir systems in China. Results show that the ECHT outperforms the state-of-the-art CHTs, and its probability of returning feasible solutions is much higher. The reservoir levels optimized with the ECHT are well suited for hydropower generation, which also reduce the chance of reservoir spilling.

Synchronized scheduling of production and outbound shipping using bilevel-based simulated annealing algorithm

In general, different divisions even in a same company may make scheduling decisions irrespective of the overall benefit of the company. It is practically impossible to carry out the integrated optimization, and the sequential optimization results in isolated solutions within the divisions often leads to inferior overall solutions. In this study, a novel bilevel approach is proposed for joint synchronized production and shipping scheduling, considering production division as the leader and shipping division as the follower. A bilevel-based simulating annealing (SA) algorithm is proposed as the solution algorithm. Specifically, first, the production division prepares a tentative production schedule obtained by the SA. Based on the tentative schedule, shipping scheduling is addressed by an effective heuristic named Earliest Completion Machine. An inner SA is also presented for shipping scheduling to generate good quality shipping schedules. The tentative schedule is iteratively updated until the stopping criterion is satisfied. The effectiveness and efficiency of the proposed approach were verified via comparison with the traditional sequential and integrated approaches. Furthermore, the sensitivity analysis results indicate that shipping capacity has a considerable effect on the overall makespan. The allocation of the fixed shipping capacity onto blocks was also investigated, indicating that the large more strategy outperforms the average allocation and the large less strategies.