scholarly journals Best-compromise nutritional menus for childhood obesity

2019 ◽  
Author(s):  
Paul Bello ◽  
Pedro Gallardo ◽  
Lorena Pradenas ◽  
Jacques A. Ferland ◽  
Victor Parada
Keyword(s):  
Childhood Obesity ◽  
Programming Model ◽  
Numerical Study ◽  
Glycemic Load ◽  
Computational Time ◽  
Menu Planning ◽  
Metaheuristic Methods ◽  
Cholesterol Intake ◽  
Study Test ◽  
Multiobjective Mathematical Programming

AbstractChildhood obesity is an undeniable reality and has shown a rapid growth in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that can be defined with a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid healthy nutritional menu planning to prevent childhood obesity. This model enables a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake). In addition, it considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. To complete this numerical study, test instances associated with children aged 4-18 years old were created. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in less computational time. The numerical results indicate proper guidelines for personalized plans for individual children.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

An Investigation Into Nonlinear Growth Rate of Two-Dimensional and Three-Dimensional Single-Mode Richtmyer–Meshkov Instability Using an Arbitrary-Lagrangian–Eulerian Algorithm

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Mike Probyn ◽  
Ben Thornber ◽  
Dimitris Drikakis ◽  
David Youngs ◽  
Robin Williams
Keyword(s):  
Growth Rate ◽  
Numerical Scheme ◽  
Initial Conditions ◽  
Numerical Study ◽  
Three Dimensional ◽  
Single Mode ◽  
Computational Time ◽  
Initial Growth ◽  
Arbitrary Lagrangian Eulerian ◽  
Layer Width

This paper presents an investigation into the use of a moving mesh algorithm for solving unsteady turbulent mixing problems. The growth of a shock induced mixing zone following reshock, using an initial setup comparable to that of existing experimental work, is used to evaluate the behavior of the numerical scheme for single-mode Richtmyer–Meshkov instability (SM-RMI). Subsequently the code is used to evaluate the growth rate for a range of different initial conditions. The initial growth rate for three-dimensional (3D) SM Richtmyer–Meshkov is also presented for a number of different initial conditions. This numerical study details the development of the mixing layer width both prior to and after reshock. The numerical scheme used includes an arbitrary Lagrangian–Eulerian grid motion which is successfully used to reduce the mesh size and computational time while retaining the accuracy of the simulation results. Varying initial conditions shows that the growth rate after reshock is independent of the initial conditions for a SM provided that the initial growth remains in the linear regime.

scholarly journals Maternal Dietary Glycemic and Insulinemic Indexes Are Not Associated with Birth Outcomes or Childhood Adiposity at 5 Years of Age in an Irish Cohort Study

2019 ◽  
Vol 149 (6) ◽  
pp. 1037-1046 ◽  
Author(s):  
Ling-Wei Chen ◽  
Pilar Navarro ◽  
Celine M Murrin ◽  
John Mehegan ◽  
Cecily C Kelleher ◽  
...  
Keyword(s):  
Cohort Study ◽  
Childhood Obesity ◽  
Waist Circumference ◽  
Birth Outcomes ◽  
Glycemic Load ◽  
Insulinemic Index ◽  
Nonlinear Relation ◽  
Z Scores ◽  
Dietary Glycemic Index

ABSTRACT Background High maternal dietary glycemic index (GI) and glycemic load (GL) may be associated with adverse offspring birth and postnatal adiposity outcomes through metabolic programming, but the evidence thus far, mainly from studies conducted in high-risk pregnant populations, has been inconclusive. No study has examined the influence of maternal insulin demand [measured by food insulinemic index (II) and insulinemic load (IL)] on offspring outcomes. Objectives We investigated associations between maternal GI, GL, II, and IL and offspring birth outcomes and postnatal adiposity in a general pregnant population. Methods The study was based on data from 842 mother-child pairs from the Lifeways prospective cohort study in Ireland. Through the use of standard methodology, maternal GI, GL, II, and IL were derived from dietary information obtained via a validated food-frequency questionnaire in early pregnancy (12–16 wk). Birth outcomes were abstracted from hospital records. At 5-y follow-up, children's body mass index (BMI) and waist circumference were measured. Associations were assessed through the use of multivariable-adjusted regression analysis. Results Mothers had a mean ± SD age of 30.3 ± 5.7 y and a mean BMI (kg/m2) of 23.9 ± 4.2. The mean ± SD for dietary glycemic and insulinemic indexes were: GI = 58.9 ± 4.4; GL = 152 ± 49; II = 57.4 ± 14.5; IL = 673 ± 267. After adjustment for confounders, no consistent associations were observed between maternal GI, GL, II, and IL and birth outcomes including birth weight, macrosomia, gestational age, and postterm births. Similarly, no association was observed with BMI and waist circumference z scores and childhood obesity (general and central) at 5-y follow-up. There was no evidence of a nonlinear relation between the studied indexes and outcomes. Conclusions We observed no clear relation between maternal GI, GL, II, and IL and offspring birth outcomes and childhood obesity in a general pregnant population.

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

2020 ◽  
Vol 15 (4) ◽  
pp. 1363-1387
Author(s):  
Mohammad Saeid Atabaki ◽  
Seyed Hamid Reza Pasandideh ◽  
Mohammad Mohammadi
Keyword(s):  
Inventory Management ◽  
Programming Model ◽  
Lot Sizing ◽  
Unconstrained Minimization ◽  
Computational Time ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Content Type ◽  
Imperfect Quality ◽  
Lot Sizing Problem

Purpose Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs. Design/methodology/approach The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO. Findings Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible. Originality/value Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

scholarly journals Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Wenming Cheng ◽  
Peng Guo ◽  
Zeqiang Zhang ◽  
Ming Zeng ◽  
Jian Liang
Keyword(s):  
Variable Neighborhood Search ◽  
Parallel Machines ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Deteriorating Jobs ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem

In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems.

scholarly journals Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

2016 ◽  
Vol 28 (5) ◽  
pp. 449-460 ◽  
Author(s):  
Wenliang Zhou ◽  
Xia Yang ◽  
Lianbo Deng ◽  
Jin Qin
Keyword(s):  
Ant Colony Algorithm ◽  
Programming Model ◽  
Crew Scheduling ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Train Services ◽  
Duty Time

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time.

Optimization Methodology for Innovative Automotive Crash Absorbers

2013 ◽  
Author(s):  
Luca D’Agostino ◽  
Luca Bertocchi ◽  
Luca Splendi ◽  
Antonio Strozzi ◽  
Patrizio Moruzzi
Keyword(s):  
Variable Thickness ◽  
Numerical Study ◽  
Computational Cost ◽  
Maximum Energy ◽  
Computational Time ◽  
Geometrical Parameters ◽  
Mass Ratio ◽  
Element Analysis ◽  
Vehicle Simulation ◽  
Full Vehicle

The simulation of vehicle crash impacts requires accurate and computationally expensive Finite Element analysis. An effective procedure consists in considering and establishing which improvement can be made on an equivalent sub-model of the full vehicle. In this way, all the analysis can be performed on smaller models, thus saving computational time. A full vehicle simulation is required only at the end of the design process to validate the results of the sub-model analysis. A software based on a genetic optimization algorithm has been developed in order to optimize the geometrical parameters of a variable-thickness crash absorber. A numerical study on the folding of thin-walled aluminum tubes with variable-thickness has been performed in order to achieve the maximum energy absorption-to-mass ratio. Moreover, the performance in terms of folding length and crush load peaks have been considered. Different optimization strategies have been implemented to find out which solution guarantees the achievement of the optimization target with the lowest computational cost. The results show how the approach proposed by the authors allows an efficient variable-thickness crash absorber to be obtained. In fact it performs better in term of crash behavior and energy dissipation-to-mass ratio, with respect to the original constant_thickness model.

Prediction of Cancer Disease Using Classification Techniques in Map Reduce Programming Model

2018 ◽  
pp. 139-158
Author(s):  
M. A. Saleem Durai ◽  
Anbarasi M. ◽  
Jaiti Handa
Keyword(s):  
Programming Model ◽  
Distributed File System ◽  
Computational Time ◽  
Classification Algorithms ◽  
Cancer Dataset ◽  
Cancer Disease ◽  
Single Output ◽  
Knn Classification ◽  
Mapreduce Model ◽  
Sensitivity Specificity

As the volume of data is increasing with time the primary issue is how to store and process such data and get useful information out of it. Analysis of classification algorithms and MapReduce programming model has led to the conclusion that the distributed file system and parallel computing attributes of MapReduce are good for designing classifier model. The major reason for it is parallel processing of data in which data is divided and processed in parallel and the output from each is reduced further for a single output. In this paper, we are going to study how to use MapReduce model to build classifier model. We are using cancer dataset to predict if a person has cancer or not by using Naive Bayes and KNN classification algorithms. We have compared them on the basis on computational time and the factors like sensitivity, specificity, and accuracy. In the end, we would be able to compare these two algorithms and tell which one works better on MapReduce programming model

A Numerical Study of Developing Buoyancy-Assisted Mixed Convection With Spatially Periodic Wall Heating

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Chris D. Dritselis
Keyword(s):  
Mixed Convection ◽  
Richardson Number ◽  
Numerical Study ◽  
Heat Fluxes ◽  
Computational Time ◽  
Convection Flow ◽  
Parabolic Model ◽  
Flow Modification ◽  
Elliptic Model ◽  
Spatially Periodic

The validity of a parabolic model for simulating the developing buoyancy-assisted mixed convection flow in a vertical channel with spatially periodic wall temperature is verified by a full elliptic model of the momentum and energy equations. A detailed assessment of the effects of the grid resolution, the Richardson number, the Reynolds number, and the preheating zone is presented through extensive comparisons of the velocity and temperature fields and spatial variations of pressure and local heat fluxes at the walls yielded by both models. The parabolic model is capable of reproducing the flow modification into a pattern consisting of a recirculating zone with increasing Richardson number, capturing adequately the main trends of the flow and heat transfer results. For certain combinations of the relevant nondimensional parameters, the solutions of the parabolic model agree reasonably well with those of the elliptic model from a quantitative point of view. In all the cases examined here, the computational time needed by the parabolic model is significantly smaller than that of the elliptic model.

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