Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

2012 ◽  
pp. 643-662
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.


2016 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Saleem Z. Ramadan

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.


2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.


2009 ◽  
Vol 1 (2) ◽  
pp. 80-88 ◽  
Author(s):  
Dmitrij Šešok ◽  
Rimantas Belevičius

Aim of the article is to suggest technology for optimization of pile positions in a grillage-type foundations seeking for the minimum possible pile quantity. The objective function to be minimized is the largest reactive force that arises in any pile under the action of statical loading. When piles of the grillage have different characteristics, the alternative form of objective function may be employed: the largest difference between vertical reaction and allowable reaction at any pile. Several different allowable reactions with a given number of such piles may be intended for a grillage. The design parameters for the problem are positions of the piles. The feasible space of design parameters is determined by two constraints. First, during the optimization process piles can move only along the connecting beams. Therefore, the two-dimensional grillage is “unfolded” to a one-dimensional construct, and the supports are allowed to range through this space freely. Second, the minimum allowable distance between two adjacent piles is introduced due to the specific capacities of pile driver.The initial data for the problem are the following: the geometrical scheme of the grillage, the cross-section and material data of connecting beams, minimum possible distance between adjacent supports, characteristics of piles, and the loading data given in the form of concentrated loads or trapezoidal distributed loadings. The results of solution are the required number of piles and their positions.The entire optimization problem is solved in two steps. First, the grillage is transformed to a one-dimensional construct, and the optimizer decides about a routine solution (i.e. the positions of piles in this construct). Second, the backward transformation returns the pile positions into the two-dimensional grillage, and the “black-box” finite element program returns the corresponding objective function value. On the basis of this value the optimizer predicts the new positions of piles, etc. The finite element program idealizes the connecting beams as the beam elements and the piles – as the finite element mesh nodes with a given boundary conditions in form of vertical and rotational stiffnesses. The optimizing program is an elitist genetic algorithm or a random local search algorithm. At the beginning of problem solution the genetic algorithm is employed. In the optimization problems under consideration, the genetic algorithms usually demonstrate very fast convergence at the beginning of solution and slow non-monotonic convergence to a certain local solution point after some number of generations. When the further solution with a genetic algorithm refuses to improve the achieved answer, i.e. a certain local solution is obtained; the specific random search algorithm is used. The moment, at which the transition from genetic algorithm to the local search is optimal, is sought in the paper analyzing the experimental data. Thus, the hybrid genetic algorithm that combines the genetic algorithm itself and the local search is suggested for the optimization of grillages.


2018 ◽  
Vol 19 ◽  
pp. 01037
Author(s):  
Łukasz Knypiński ◽  
Krzysztof Kowalski ◽  
Lech Nowak

In the paper the way of adaptation of the penalty function method to the genetic algorithm is presented. In case of application of the external penalty function, the penalty term may exceed the value of the primary objective function. This means, that the value of the modified objective function is negative, while in genetic algorithm the adaptation must be of positive value, especially it in the selection procedure utilizes the roulette method. The sigmoidal transformation is applied to solve this problem. The computer software is developed in the Delphi environment. The proposed approach is applied to optimization of the electromagnetic linear actuator.


2020 ◽  
Vol 8 (5) ◽  
pp. 2598-2604

The Genetic Algorithm (GA) approach is an evolutionary optimization technique, which is developed based on the fundamental theories of natural selection and evolution. The present study focuses on the design of an isolated traffic signal for a two-phase intersection using the conventional method and GA-based optimization technique for unsaturated traffic flow conditions. The methodology of the study includes formulation of an objective function and the constraints, formulation of the constraint violation coefficient, formulation of the modified objective function, formulation of the fitness index (fi), and the GA operations to determine the best green signal timings. The intrinsic nature of Genetic Algorithms in performing elitism, which assures to carry forward the best solution identified in each generation to the next generation. An example problem was solved to demonstrate elitism using binary genetic algorithm with a single variable approach. In the GA operation, parent strings/chromosomes are selected and the crossover is performed along with mutation to form the new offspring’s. Mutation helps in avoiding the convergence of the solution to local optima. In this operation Fitness Index (FI) values of each strings/chromosomes are used as a measure to identify the parent strings to perform GA operations for the next generation. The results of the study indicate that the proposed technique can be used to optimize the signal timings of an isolated traffic signal, this will influence on reducing the delays at the junctions.


2010 ◽  
Vol 450 ◽  
pp. 560-563
Author(s):  
Dong Mei Cheng ◽  
Jian Huang ◽  
Hong Jiang Li ◽  
Jing Sun

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.


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