Minimizing total tardiness on parallel machines based on Genetic Algorithm

This paper discusses the problem of scheduling a set of jobs on parallel machines considering sequence-dependent setup times. The processing sequence of jobs assigned to each machine is determined to minimize the makespan. Since it is a NP-hard problem, even for instances with small sizes, the computational effort may be prohibitively large. Then genetic algorithms with two types of assignment scheme are presented to obtain near optimal solutions. The popular scheme of random assignment is used in the genetic algorithm (GA-1). Unlike GA-1, the scheme of greedy assignment is employed in the genetic algorithm (GA-2). The computational experiments are carried out to compare the performance of GA-1 with that of GA-2. Computational results show that GA-2 has better performance than GA-1.

In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP) model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA), and three heuristic algorithms (Johnson, SPTCH and Palmer) are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.

This paper deals with the configuration of a wireless network with the aim of minimizing the overall cost of both operation and network installation. The trade-off between the operation cost and the installation cost is the key consideration when designing cellular telecommunication networks, and can save costs and improve the performance of the network. In this paper, we propose an integrated framework for selecting Mobile Switching Center (MSC) among the candidate MSCs and assigning Base Stations (BSs) to the selected MSCs with the objective function of minimizing the cost of MSC setup, BS to MSC cabling, as well as the cost of handover. Capacity constraint for the selected MSC is also considered in the problem. The problem is expressed in an integer programming model and the Lagrangian relaxation method is proposed to solve the problem by dualizing some constraints. The Lagrangian relaxed problem is decomposed into subproblems that can be resolved optimally. The Lagrangian heuristic algorithm is suggested to find feasible solutions to the original problem. Computational experiments are performed to test the effectiveness and efficiency of the proposed heuristic algorithm. In the experiments, Lagrangian bounds on the optimal solution are used to show the effectiveness of the algorithm. The results of the proposed algorithm are also compared with those of some conventional meta-heuristics, Tabu search (TS) and Genetic algorithm (GA). The computational experiments show that the performance of the proposed heuristics is satisfactory in both the speed and the quality of the solution generated.

Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Factory layout is NP problem[1]. There are many methods to solve it ,such as engineering diagram, flow chart method, various heuristic algorithms, SA( simulated annealing) and GA(genetic algorithm) [2].ACO (ant colony optimization) is used to solve it in this paper. The logistics costs exist between two workshops that are treated as pheromone that guides ants to search the best solution. Smaller logistics cost is, stronger the two workshops of relation is. In the process of optimization theworkshop with low logistics cost is more likely to be chosen, which minimizes the system logistics cost. Compared with GA, ACO has the advantage in speed. The mean value of the solution, the best solution, the worst solution is better too. More the number of workshop is, more obvious the superiority is.

In this paper the image compression problem is analyzed using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs the clustering efficiently. The possibility of solving clustering problems with genetic algorithms provides optimal solution. The present work makes use of genetic clustering algorithms to obtain an ordered representation of the image and then applies the DWT (Discrete Wavelet Transformation) to compress the image. The image quality is measured using PSNR measure. The genetic algorithm (GA)-based compression image has more PSNR value compared to the image without GA. It shows that the quality of the image is good for GA based compression. Also it reduces the problem of memory utilization efficiently due to compression and at the same time quality of the image is maintained due to GA. Keywords: Genetic algorithms, clustering, image compression, discrete wavelet transformation.

Application of fuzzy goal programming and genetic algorithm is considered in this paper. We extend a multi-objective model for integrated inventory-production-distribution planning in supply chain (SC). We consider a supply chain network which consists of a manufacture, with multiple plants, multiple distribution centers (DCs), multiple retailers and multiple customers. The manufacturer produces several items. Decision maker’s imprecise aspiration levels of goals are incorporated into model using fuzzy goal programming approach. Due to the complexity of problem in large size and in order to get a satisfactory near optimal solution with great speed, a new genetic algorithm is proposed to solve constrained problems. To show the efficiency of the used model and fuzzy goal programming approach and genetic algorithm for the collaborative inventory-production-distribution problem, computational experiments are performed on a hypothetically constructed case problem.

In this paper, a mathematical model is established for the three-dimensional packing problem. This model is a multi-objective optimization problem which considers two factors: volume utilization ratio and load utilization ratio. This paper uses heuristic algorithm to design on the basis of the actual experience of packing. At the same time, combining genetic algorithm and heuristic algorithm, a three-dimensional hybrid genetic heuristic algorithm (3D-HGH) is proposed. This algorithm improves the performance of heuristic algorithm and improves the utilization ratio. The results of three-dimensional visualization of loading are obtained by simulation experiments.

A relax-and-fix Pareto-based algorithm for a bi-objective vaccine distribution network considering a mix-and-match strategy in pandemics

RAM growth of the PATRIOT missile system : Mack W. Wyatt. Proc. a. Reliab. Maintainab. Symp., 381 (1987)

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Optimality models used in behavioral ecology, and especially on insect parasitoids, have taken a variety of approaches, from classical analytical tools to individualbased simulations. The increasing awareness that much of the observable behavior in parasitoids depends on the state of the insect (be it the physiological or informational state) has led to the increasing use of stochastic dynamic programming models. However, optimal behaviors of one individual often depend upon the behavior of conspecifics, further complicating the issue. While classical game theory may be applied when behavior is not state dependent, genetic algorithms (GA) provide a powerful way in finding optimal behaviors for situations where such optimal behavior depends upon an animal’s state and on the frequency of alternative behaviors of conspecifics. More generally, GAs can be used when there is a need to find the optimal strategy among a number of different alternative behaviors whose number is far too great to be exhaustively checked. GAs are search algorithms that proceed in a fashion analogous to natural selection. They are individual-based simulations that identify optimal solutions by searching the enormous space of potential solutions mimicking the process of evolution in biological systems. In this chapter, we discuss research questions in parasitoid behavioral ecology that might benefit from applying GA as a research tool for optimality models. Examples will be developed and explained. Among others, using the foraging problem of estimating habitat quality, we show how fast GAs find optimal solutions for parameter spaces that are impossible to solve numerically otherwise.

The scheduling problem is a typical time table problem in educational administration. For such a NP complete problems, when the genetic algorithm solves this problem, it has precociousness phenomenon and quickly converges not to the global optimal solution but to the local optimal solution. Therefore, we use the advantage of local search method and partial matching crossover operation to solve TTP problem with the genetic algorithm combining search algorithm. We do a lot of experiments and evaluate the performance of the improved genetic algorithm. The experiment results show that genetic search algorithm is a more superior algorithm to apply to the TTP problem.

The multiple destination routing (MDR) problem can be formulated as finding a minimal cost tree which contains designated source and multiple destination nodes so that certain constraints in a given communication network are satisfied. This is a typical NP-hard problem, and therefore only heuristic algorithms are of practical value. As a first step, a new genetic algorithm is developed to solve the MDR problems without constraints. It is based on the transformation of the underlying network of an MDR problem into its distance complete form, a natural chromosome representation of a minimal spanning tree (an individual), and a completely new computation of the fitness of individual. Compared with the known genetic algorithms and heuristic algorithms for the same problem, the proposed algorithm has several advantages. First, it guarantees convergence to an optimal solution with probability one. Second, not only are the resultant solutions all feasible, the solution quality is also much higher than that obtained by the other methods (indeed, in almost every case in our simulations, the algorithm can find the optimal solution of the problem). Third, the algorithm is of low computational complexity, and this can be decreased dramatically as the number of destination nodes in the problem increases. The simulation studies for the sparse and dense networks all demonstrate that the proposed algorithm is highly robust and very efficient in the sense of yielding high-quality solutions.

To solve the NP-difficult problem of Network-on-Chip (NoC) mapping, researchers currently use heuristic search algorithms represented by Genetic Algorithm (GA) for optimization. However, as the scale of the problem increases, the performance of these algorithms will deteriorate rapidly, and the global optimal solution cannot be obtained. Therefore, this paper proposes an improved mapping optimization algorithm based on Generative Adversarial Networks (GANs), which combines GA with GANs to overcome the former's shortcomings. The simulation results show that compared to GA without GANs post-processing, in MMSE-IRC and SVD examples of the wireless communication system, the optimal solution obtained by trained GANs is closer to the true optimal solution, and their power consumption is reduced by 24.49% and 17.54% respectively, while the delay is reduced by 10.89% and 8.77% respectively, and it performs better in terms of distribution and convergence.