scholarly journals Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Bahareh Vaisi ◽  
Hiwa Farughi ◽  
Sadigh Raissi
Keyword(s):  
Cycle Time ◽  
Optimal Solution ◽  
Optimization Method ◽  
Total Production ◽  
Robotic Cell ◽  
Optimal Sequence ◽  
Multiobjective Particle Swarm Optimization ◽  
Current Article ◽  
Mathematical Modelling And Simulation ◽  
Different Parts

The purpose of this paper is to model two problems comprising schedule-allocate (in case of producing identical parts) and sequencing of parts (in case of producing different parts). The first model is used for minimizing the cycle time and operational cost, and the second one for minimizing both the mean and standard deviation of the total production cost as well the cycle time, in an unreliable three-machine robotic cell which confronted with many uncertainty factors. In the current article, mathematical modelling and simulation-based optimization method have been presented to schedule-allocate similar parts and trace the optimal sequence of different parts. Several solution procedures, including epsilon-constraint method and multiobjective particle swarm optimization algorithm, for identical parts case and response surface methodology for different parts case are applied. The results derived from solving numerical examples revealed some advantages in terms of time to attain the optimal solution.

scholarly journals Optimizing Tool Path Sequence Of Plasma Cutting Machine Using TSP Approach

2020 ◽  
Vol 184 ◽  
pp. 01037
Author(s):  
Shreeyash Sonawane ◽  
Pallavi Patil ◽  
Ramkrishna Bharsakade ◽  
Pankaj Gaigole
Keyword(s):  
Cycle Time ◽  
Tool Path ◽  
Energy Savings ◽  
Cutting Tool ◽  
Optimal Solution ◽  
Optimal Choice ◽  
Plasma Cutting ◽  
Optimal Sequence ◽  
Cutting Head ◽  
The Greedy Algorithm

The paper consists of optimizing the total distance traveled by the Plasma Cutting Tool Head. It is cross-functional implementation traveling salesman problem to the core manufacturing process of plasma cutting. This study contributes to reduce the total rapid traverse length (Total Through-Air Travelled Distance) by the cutting tool (Plasma Beam), which is totally a non-productive as well as non-value adding process contributing to the machine running costs and cycle time of the product directly and indirectly. For deriving this optimal sequence for the plasma cutting head, a heuristic algorithm is used. This algorithm calculates an optimal solution for a problem creating the best possible sequence. The greedy algorithm follows the problem-solving heuristics of making a locally optimal choice at every stage, finally integrating a globally optimal solution. Hence after deducing this optimal sequence and when accompanied by the Plasma Cutting tool, it resulted in the least distance traveled, contributing to minimizing cost and energy savings of the machine and simultaneously reducing the total traveled distance and similarly the cycle time.

scholarly journals Optimal solution of the fractional order breast cancer competition model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Hassani ◽  
J. A. Tenreiro Machado ◽  
Z. Avazzadeh ◽  
E. Safari ◽  
S. Mehrabi
Keyword(s):  
Breast Cancer ◽  
Fractional Order ◽  
Legendre Polynomials ◽  
Numerical Experiments ◽  
Optimal Solution ◽  
Optimization Method ◽  
Competition Model ◽  
Basis Functions ◽  
Cell Populations ◽  
Operational Matrices

AbstractIn this article, a fractional order breast cancer competition model (F-BCCM) under the Caputo fractional derivative is analyzed. A new set of basis functions, namely the generalized shifted Legendre polynomials, is proposed to deal with the solutions of F-BCCM. The F-BCCM describes the dynamics involving a variety of cancer factors, such as the stem, tumor and healthy cells, as well as the effects of excess estrogen and the body’s natural immune response on the cell populations. After combining the operational matrices with the Lagrange multipliers technique we obtain an optimization method for solving the F-BCCM whose convergence is investigated. Several examples show that a few number of basis functions lead to the satisfactory results. In fact, numerical experiments not only confirm the accuracy but also the practicability and computational efficiency of the devised technique.

scholarly journals Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Market Competition ◽  
Optimal Solution ◽  
Cost Savings ◽  
Cost Effective ◽  
Optimization Method ◽  
Optimal Decision ◽  
Environmental Damage ◽  
Fuel Cost

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

Thermomechanical Response of Commercially Pure Titanium under Large Plastic Deformation at High Strain Rates

2012 ◽  
Vol 548 ◽  
pp. 174-178 ◽  
Author(s):  
Chong Yang Gao ◽  
Wei Ran Lu
Keyword(s):  
Pure Titanium ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strain Rates ◽  
Commercially Pure Titanium ◽  
Thermomechanical Response ◽  
Commercially Pure ◽  
Different Temperatures ◽  
Cp Ti ◽  
Constitutive Parameters

By using a dislocation-based plastic constitutive model for hcp metals developed by us recently, the dynamic thermomechanical response of an important industrial material, commercially pure titanium (CP-Ti), was described at different temperatures and strain rates. The constitutive parameters of the material are determined by an efficient optimization method for a globally optimal solution. The model can well predict the dynamic response of CP-Ti by the comparison with experimental data and the Nemat-Nasser-Guo model.

scholarly journals GA-Based Optimization Method for Mobile Crane Repositioning Route Planning

2021 ◽  
Vol 11 (13) ◽  
pp. 6010
Author(s):  
Han-Seong Gwak ◽  
Hong-Chul Lee ◽  
Byoung-Yoon Choi ◽  
Yirong Mi
Keyword(s):  
Operating Time ◽  
Route Planning ◽  
Optimal Solution ◽  
Optimization Method ◽  
Computational Method ◽  
Test Cases ◽  
Construction Sites ◽  
Productivity Improvement ◽  
Planning Optimization ◽  
Mobile Crane

Mobile cranes have been used extensively as essential equipment at construction sites. The productivity improvement of the mobile crane affects the overall productivity of the construction project. Hence, various studies have been conducted regarding mobile crane operation planning. However, studies on solving RCP (the repositioning mobile crane problem) are insufficient. This article presents a mobile crane reposition route planning optimization method (RPOS) that minimizes the total operating time of mobile crane. It converts the construction site into a mathematical model, determines feasible locations of the mobile crane, and identifies near-global optimal solution (s) (i.e., the placement point sequences of mobile crane) by implementing genetic algorithm and dijkstra’s algorithm. The study is of value to practitioners because RPOS provides an easy-to-use computerized tool that reduces the lengthy computations relative to data processing and Genetic Algorithms (GAs). Test cases verify the validity of the computational method.

A Systematic Optimization Design Method for Thermal Management of Passenger Vehicles

2021 ◽  
pp. 1-27
Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Thermal Management ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Optimal Solution ◽  
Optimization Method ◽  
Drive Shaft ◽  
Management Problem ◽  
Systematic Optimization

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

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