scholarly journals Comments on “Minimizing Buffer Requirements Under Rate-Optimal Schedule in Regular Dataflow Networks”

2015 ◽  
Vol 81 (1) ◽  
pp. 129-133 ◽  
Author(s):  
José-Inácio Rocha ◽  
Octávio Páscoa Dias ◽  
Luís Gomes
Keyword(s):  
Optimal Schedule ◽  
Dataflow Networks

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

Optimal Operation and Maintenance of Gas Compressor Stations: An Integrated Framework Applied to a Large-Scale Industrial Case

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Dionysios P. Xenos ◽  
Erling Lunde ◽  
Nina F. Thornhill
Keyword(s):  
Optimization Model ◽  
Large Scale ◽  
Optimal Schedule ◽  
Optimal Operation ◽  
Optimal Distribution ◽  
Compressor Station ◽  
Operational Costs ◽  
Operation And Maintenance ◽  
Maintenance Model ◽  
Operational Aspects

This paper presents a framework which integrates maintenance and optimal operation of multiple compressors. The outcome of this framework is a multiperiod plan which provides the schedule of the operation of compressors: the schedule gives the best decisions to be taken, for example, when to carry out maintenance, which compressors to use online and how much to load them. These decisions result in the minimization of the total operational costs of the compressors while at the same time the demand of the plant is met. The suggested framework is applied to an industrial gas compressor station which encompasses large multistage centrifugal compressors operating in parallel. The optimization model of the framework consists of three main parts: the models of compressor maps, the operational aspects of compressors, and a maintenance model. The results illustrate the optimal schedule for 90 days and an example of the optimal distribution of the load of the compressors for 5 days. Finally, the results show the economical benefits from the integration of maintenance and optimization.

scholarly journals Integration of Mixed-Integer Nonlinear Program and Project Management Tool to Support Sustainable Cost-Optimal Construction Scheduling

2021 ◽  
Vol 13 (21) ◽  
pp. 12173
Author(s):  
Borna Dasović ◽  
Uroš Klanšek
Keyword(s):  
Project Management ◽  
Optimal Schedule ◽  
Indirect Costs ◽  
Management Tool ◽  
Mixed Integer ◽  
Task Completion ◽  
Nonlinear Program ◽  
Project Schedule ◽  
Construction Scheduling ◽  
Optimal Construction

This paper presents the integration of mixed-integer nonlinear program (MINLP) and project management tool (PMT) to support sustainable cost-optimal construction scheduling. An integrated structure of a high-level system for exact optimization and PMT was created. To ensure data compatibility between the optimization system and PMT and to automate the process of obtaining a cost-optimal schedule, a data transformation tool (DTT) was developed within a spreadsheet application. The suggested system can determine: (i) an optimal project schedule with associated network diagram and Gantt chart in continuous or discrete time units; (ii) optimal critical and non-critical activities, including their early start, late start, early finish, late finish along with total and free slack times; and (iii) minimum total project cost along with the allocation of direct and indirect costs. The system provides functionalities such as: (i) MINLP can be updated, and schedules can be re-optimized; (ii) the optimal schedule can be saved as a baseline to track changes; (iii) different optimization algorithms can be engaged whereby switching between them does not require model changes; (iv) PMT can be used to track task completion in the optimized schedule; (v) calendar settings can be changed; and (vi) visual reports can be generated to support efficient project management. Results of cost-optimal project scheduling are given in a conventional PMT environment, which raises the possibility that the proposed system will be more widely used in practice. Integration of MINLP and PMT allows each software to be used for what it was initially designed. Their combination leads to additional information and features of optimized construction schedules that would be significantly more difficult to achieve if used separately. Application examples are given in the paper to show the advantages of the proposed approach.

scholarly journals Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

2021 ◽  
Vol 9 (4) ◽  
pp. 940-950
Author(s):  
Hongqian Wei ◽  
Jun Liang ◽  
Chuanyue Li ◽  
Youtong Zhang
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Optimal Schedule ◽  
Nsga Ii ◽  
Vehicle Load

scholarly journals Early Discontinuation and Nonadherence to Adjuvant Hormonal Therapy in a Cohort of 8,769 Early-Stage Breast Cancer Patients

2010 ◽  
Vol 28 (27) ◽  
pp. 4120-4128 ◽  
Author(s):  
Dawn L. Hershman ◽  
Lawrence H. Kushi ◽  
Theresa Shao ◽  
Donna Buono ◽  
Aaron Kershenbaum ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hormonal Therapy ◽  
Early Stage ◽  
Optimal Schedule ◽  
Medication Possession Ratio ◽  
Adjuvant Hormonal Therapy ◽  
Breast Cancer Patients ◽  
Early Discontinuation ◽  
Younger Women ◽  
Hormone Sensitive

Purpose While studies have found that adjuvant hormonal therapy for hormone-sensitive breast cancer (BC) dramatically reduces recurrence and mortality, adherence to medications is suboptimal. We investigated the rates and predictors of early discontinuation and nonadherence to hormonal therapy in patients enrolled in Kaiser Permanente of Northern California health system. Patients and Methods We identified women diagnosed with hormone-sensitive stage I-III BC from 1996 to 2007 and used automated pharmacy records to identify hormonal therapy prescriptions and dates of refill. We used Cox proportional hazards regression models to analyze factors associated with early discontinuation and nonadherence (medication possession ratio < 80%) of hormonal therapy. Results We identified 8,769 patients with BC who met our eligibility criteria and who filled at least one prescription for tamoxifen (43%), aromatase inhibitors (26%), or both (30%) within 1 year of diagnosis. Younger or older age, lumpectomy (v mastectomy), and comorbidities were associated with earlier discontinuation, while Asian race, being married, earlier year at diagnosis, receipt of chemotherapy or radiotherapy, and longer prescription refill interval were associated with completion of 4.5 years of therapy. Of those who continued therapy, similar factors were associated with full adherence. Women age younger than 40 years had the highest risk of discontinuation (hazard ratio, 1.51; 95% CI, 1.23 to 1.85). By 4.5 years, 32% discontinued therapy, and of those who continued, 72% were fully adherent. Conclusion Only 49% of patients with BC took adjuvant hormonal therapy for the full duration at the optimal schedule. Younger women are at high risk of nonadherence. Interventions to improve adherence and continuation of hormonal therapy are needed, especially for younger women.

ONLINE SCHEDULING OF DYNAMIC TREES

1995 ◽  
Vol 05 (04) ◽  
pp. 635-646 ◽  
Author(s):  
MICHAEL A. PALIS ◽  
JING-CHIOU LIOU ◽  
SANGUTHEVAR RAJASEKARAN ◽  
SUNIL SHENDE ◽  
DAVID S.L. WEI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Online Scheduling ◽  
The Other ◽  
Static Case ◽  
Scheduling Problem ◽  
Dynamic Trees ◽  
Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

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