scholarly journals Joint Optimization of Multiprocess Routes and Layout for Low Entropy Flexible Facility

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Hongtao Tang ◽  
Senli Ren ◽  
Weiguang Jiang ◽  
Jiajiong Liang ◽  
Qingfeng Chen
Keyword(s):  
Manufacturing Industry ◽  
Expert Knowledge ◽  
Facility Layout ◽  
Route Planning ◽  
Optimal Solution ◽  
Optimization Method ◽  
Analytic Network Process ◽  
Joint Optimization ◽  
Process Route ◽  
Low Entropy

Facility layout is not only the premise of production, but also a breakthrough for manufacturing industry to realize energy saving, environmental protection, and low entropy development. On the one hand, considering the interaction between product process routes and facility layout, a joint optimization model is proposed. The model aims to minimize the total logistics cost and consider the global optimization of facility layout and process route planning. On the other hand, considering the application of low entropy concept in facility layout, the analytic network process (ANP) is used to evaluate the low entropy layout. In the choice of the final facility layout, the algorithm results and expert knowledge are considered comprehensively to make up for the shortcomings of the model in the design of qualitative indicators. The algorithm innovation of this paper is to use genetic algorithm (GA) and particle swarm optimization (PSO) to search the solution of product process routes and facility layout simultaneously, to ensure the overall optimal solution of the two decision variables. Finally, an example is given to compare the joint optimization results with the independent optimization results, and the effectiveness of the joint optimization method is verified.

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.

Research on the Facilities Layout Planning in MTO Manufacturing Industry Based on Genetic Algorithm

2013 ◽  
Vol 842 ◽  
pp. 695-702
Author(s):  
Ying Wang ◽  
You Rong Li ◽  
Yu Qiong Zhou
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Industry ◽  
Design Method ◽  
Facility Layout ◽  
Optimization Method ◽  
Market Demand ◽  
Layout Planning ◽  
Manufacturing Enterprises ◽  
Make To Order ◽  
Systematic Layout Planning

To enlarge production to meet the market demand, its nessasery to improve the present facility layout for MTO (Make-To-Order) manufacturing enterprises. This paper tries to design a optimization method based on genetic algorithm for the facility layout of MTO enterprises. Firstly, SLP (systematic layout planning) was applied to analyze the material and non-material flow interrelation of the workshop. Secondly, a relatively optimum layout was determined after using fuzzy hierarchy estimation to evaluate the schemes. Then the scheme was optimized with genetic algorithm. The result shows that the optimized logistics transport load is obviously less than before. This design method based on genetic algorithm (GA) is proved feasible and effective in the optimization of facility layout.

Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

2014 ◽  
Vol 1065-1069 ◽  
pp. 3442-3445 ◽  
Author(s):  
Yan Hua Guo ◽  
Fei Fei Liu ◽  
Ning Zhang ◽  
Tao Wang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Algorithm ◽  
Parametric Design ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Optimization Method ◽  
Joint Optimization ◽  
Global Optimal ◽  
First Order Method ◽  
Quasi Newton

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. The parametric model of the planar truss is established by the ANSYS Parametric Design Language. Solutions are obtained using the first-order method native. Genetic algorithms don’t always display better properties than others. Finally, a joint optimization method is proposed, which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method. The method is identified through the numerical example of the two-bar truss. The results indicate the joint optimization method can always converge to the global optimal solution.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

scholarly journals A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

2021 ◽  
Vol 11 (15) ◽  
pp. 6676
Author(s):  
Lorenzo Salas-Morera ◽  
Laura García-Hernández ◽  
Carlos Carmona-Muñoz
Keyword(s):  
Coral Reef ◽  
Collaborative Design ◽  
Expert Knowledge ◽  
Fitness Function ◽  
Facility Layout ◽  
Layout Problem ◽  
New Strategy ◽  
Problem Instances ◽  
Expert Designer ◽  
First Time

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

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.

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