Research on Automatic Flow-shop Planning Problem Based on Data Driven Modelling Simulation and Optimization

Abstract Stereotactic neurosurgery requires a careful planning of cannulae paths to spare eloquent areas of the brain that, if damaged, will result in loss of essential neurological function such as sensory processing, linguistic ability, vision, or motor function. We present an approach based on modelling, simulation, and optimization to set up a computational assistant tool. Thereby, we focus on the modeling of the brain topology, where we construct ellipsoidal approximations of voxel clouds based on processed MRI data. The outcome is integrated in a path-planning problem either via constraints or by penalization terms in the objective function. The surgical planning problem with obstacle avoidance is solved for different types of stereotactic cannulae using numerical simulations. We illustrate our method with a case study using real MRI data.

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Data-Driven Simulation and Optimization Approaches To Incorporate Production Variability in Sales and Operations Planning

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.5b01273 ◽

2015 ◽

Vol 54 (29) ◽

pp. 7261-7272 ◽

Cited By ~ 6

Author(s):

Bruno A. Calfa ◽

Anshul Agarwal ◽

Scott J. Bury ◽

John M. Wassick ◽

Ignacio E. Grossmann

Keyword(s):

Data Driven ◽

Operations Planning ◽

Simulation And Optimization ◽

Production Variability

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Modelling, Simulation and Optimization of High Density Capacitance Solutions for HPC applications

10.1109/emc/si/pi/emceurope52599.2021.9559368 ◽

2021 ◽

Author(s):

Sumant Srikant ◽

Seungki Nam ◽

Sungwook Moon

Keyword(s):

High Density ◽

Simulation And Optimization ◽

Modelling Simulation

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INTEGRATING SIMULATION AND OPTIMIZATION TO SCHEDULE BATCHES IN PARALLEL HYBRID FLOW SHOP

International Journal on Design and Manufacturing Technologies ◽

10.18000/ijodam.70075 ◽

2010 ◽

Vol 4 (1) ◽

pp. 21-29

Author(s):

Saravanan R ◽

Raju R ◽

Ramasamy M

Keyword(s):

Flow Shop ◽

Hybrid Flow Shop ◽

Simulation And Optimization ◽

Parallel Hybrid

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A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Mathematical Problems in Engineering ◽

10.1155/2017/6315613 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Xixing Li ◽

Shunsheng Guo ◽

Yi Liu ◽

Baigang Du ◽

Lei Wang

Keyword(s):

Mathematical Model ◽

Production Planning ◽

Flow Shop ◽

Fitness Function ◽

Capacity Constraint ◽

Planning Problem ◽

Improved Genetic Algorithm ◽

Genetic Operators ◽

Make To Order ◽

Delay Cost

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

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