scholarly journals A Successful Three-Phase Metaheuristic for the Shift Minimization Personal Task Scheduling Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kimmo Nurmi ◽  
Nico Kyngäs

Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods.

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.


2019 ◽  
Author(s):  
Vivek A. Rudrapatna ◽  
Benjamin S. Glicksberg ◽  
Atul J. Butte

AbstractBackgroundReal-world data are receiving attention from regulators, biopharmaceuticals and payors as a potential source of clinical evidence. However, the suitability of these data to produce evidence commensurate with randomized controlled trials (RCTs) and the best practices in their use remain unclear. We sought to compare the real-world effectiveness of Tofacitinib in the treatment of IBD against efficacy rates published by corresponding RCTs.MethodsElectronic health records at the University of California, San Francisco (UCSF) were queried and reviewed to identify 86 Tofacitinib-treated IBD patients through 4/2019. The primary endpoint was treatment effectiveness. This was measured by time-to-treatment-discontinuation and by the primary endpoints of RCTs in Ulcerative Colitis (UC) and Crohn’s Disease (CD). Endpoints were measured and analyzed following a previously published protocol and analysis plan.Findings86 patients (68 with UC, 18 with CD) initiated Tofacitinib for IBD treatment. Most of the data needed to calculate baseline and follow-up disease activity indices were documented within the EHR(77% for UC, 91% for CD). Baseline characteristics of the UCSF and RCT cohorts were similar, except for a longer disease duration and 100% treatment failure of Tumor Necrosis Factor inhibitors in the former. None of the UCSF cohort would have met the RCT eligibility criteria due to multiple reasons.The rate of achieving the RCT primary endpoints were highly similar to the published rates for both UC(16%, P=0·5) and CD (38%, P=0·8). However, treatment persistence was substantially higher: 69% for UC (week 52) and 75% for CD (week 26).InterpretationAn analysis of routinely collected clinical data can reproduce published Tofacitinib efficacy rates, but also indicates far greater treatment durability than suggested by RCTs including possible benefit in CD. These results underscore the value of real-world studies to complement RCTs.FundingThe National Institutes of Health and UCSF Bakar InstituteResearch in ContextEvidence before this studyTofacitinib is the most recently approved treatment for Ulcerative Colitis. Data related to treatment efficacy for either IBD subtype is generally limited, whether from controlled trials or real-world studies. A search of clinicaltrials.gov was performed in January 2019 for completed phase 2 or 3, interventional, placebo-controlled clinical trials matching the terms “Crohn’s Disease” OR “Ulcerative Colitis” in the conditions field, and matching “Placebo” AND “Tofacitinib” OR “CP-690,550”) in the Interventions field. We identified three Phase 3 trials for UC (OCTAVE trials, all initially reported in a single article in 2016) and three Phase 2 trials of CD (two published in the same article in 2017, one reported in 2014). The Phase 3 UC trials reported 57·6% pooled clinical response rate in the Tofacitinib-assigned groups after 8 weeks (induction), and a 37·5% pooled remission rate among eligible induction trial responders in the Tofacitinib-assigned groups at 52 weeks. The 2017 CD trial reported a 70·8% pooled rate of response or remission in the Tofacitinib-assigned groups after 8 weeks, and a 47·6% pooled rate of response or remission among enrolled induction-trial responders at 26 weeks. A bias assessment of both UC and CD trials indicated a high risk of attrition bias and unclear risk of bias related to conflicts of interest. We also performed a search of pubmed.gov in January 2019 using search terms (“Colitis” OR “Crohn’s”) AND (“Tofacitinib” OR “CP-690,550”) OR “real-world” to identify cohort studies of Tofacitinib efficacy in routine clinical practice. No studies meeting these criteria were identified.Added value of this studyThis is one of the early studies to closely compare the results of clinical trials with the continuously-updated data captured in the electronic health records, and the very the first to assess the efficacy-effectiveness gap for Tofacitinib. We found that none of the patients treated at our center thus far would have qualified for the clinical trial based on published eligibility criteria. We found that the drug appeared to perform similarly to its efficacy when using the endpoints reported in clinical trials, but treatment persistence was significantly greater than would have been expected from the reported trial outcomes: 69% for UC at week 52 and 75% for CD at week 26.Implications of all the available evidenceTofacitinib is an effective treatment for the Ulcerative colitis and may be efficacious for Crohn’s disease. Controlled trials may not be representative of real-world cohorts, may not be optimally designed to identify efficacious drugs, and may not accurately predict patterns of use in clinical practice. Further studies using real-world data as well as methods to enable their proper use are needed to confirm and continuously monitor the efficacy and safety of drugs, both for on- and off-label use.


MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 179-188
Author(s):  
Abdelhamid Khiat ◽  
Abdelkamel Tari

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qing An ◽  
Jun Zhang ◽  
Xin Li ◽  
Xiaobing Mao ◽  
Yulong Feng ◽  
...  

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.


2018 ◽  
Vol 11 (3) ◽  
pp. 390 ◽  
Author(s):  
Basar Ogun ◽  
Çigdem Alabas-Uslu

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem is addressed to provide on-time completion of customer orders in the environment of lean manufacturing. The problem is to optimize partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components.Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid from inventory of final products. The first model is a non-linear integer programming model while the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented.Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times comparing to the other two models. It was also showed that the alternative model can solve moderate sized real-world problems.Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature since it includes new circumstances which may arise in real-world applications. This research, also, contributes the literature of batch scheduling problem by presenting new optimization models.


Networks ◽  
1990 ◽  
Vol 20 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Nagraj Balakrishnan ◽  
Richard T. Wong

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