Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.     

基于遗传算法的Job Shop调度问题研究

在多平行工作站环境下,为使限定资源分配下的车间调度问题(Job Shop problem,JSP)具有最小总延迟时间;同时又可设定各订单具有不同的开工日(release date)及到期日,提出以可开工时间与结束时间为基础的分解解法,并在遗传算法的基础上构造混合遗传算法(hybrid genetic algorithm,HGA)来实现目标设定。实验结果表明,HGA在问题求解质量与Lingo解的最佳解差异在15%以内,并具备较基本型遗传算法更佳的稳定性。结果显示该算法可帮助管理人员实现智能资源配置与订单调度。     

An approach based on hybrid genetic algorithm applied to image denoising problem

An approach based on hybrid genetic algorithm (HGA) is proposed for image denoising. In this problem, a digital image corrupted by a noise level must be recovered without losing important features such as edges, corners and texture. The HGA introduces a combination of genetic algorithm (GA) with image denoising methods. During the evolutionary process, this approach applies some state-of-the-art denoising methods and filtering techniques, respectively, as local search and mutation operators. A set of digital images, commonly used by the scientific community as benchmark, is contaminated by different levels of additive Gaussian noise. Another set composed of some Satellite Aperture Radar (SAR) images, corrupted with a multiplicative speckle noise, is also used during the tests. First, the computational tests evaluate several alternative designs from the proposed HGA. Next, our approach is compared against literature methods on the two mentioned sets of images. The HGA performance is competitive for the majority of the reported results, outperforming several state-of-the-art methods for images with high levels of noise.     

An effective evolutionary algorithm for the practical capacitated vehicle routing problems

The evolutionary algorithms are extensively adopted to resolve complex optimization problem. Genetic algorithm (GA), an evolutionary algorithm, has been proved capable of solving vehicle routing problems (VRPs). However, the resolution effectiveness of GA decreases with the increase of nodes within VRPs. Normally, a hybrid GA outperforms pure GA. This study attempts to solve a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) that is practical for use by manufacturers. The proposed HGA involves three stages. First, a diverse and well-structured initial chromosome population was constructed. Second, response surface methodology (RSM) experiments were conducted to optimize the crossover and mutation probabilities in performing GA. Finally, a combined heuristics containing improved insertion algorithm and random insertion mutation operator was established to stir over gene permutations and enhance the exploration capability of GA diversely. Furthermore, an elitism conservation strategy was implemented that replace inferior chromosomes with superior ones. As the proposed HGA is primarily used to solve practical problems, benchmark problems involving fewer than 100 nodes from an Internet website were utilized to confirm the feasibility of the proposed HGA. Two real cases one for locally active distribution and another for arms part transportation at a combined maintenance facility, both involving the Taiwanese armed forces are used to detail the analytical process and demonstrate the practicability of the proposed HGA for optimizing the CVRP.     

基于混合遗传算法求解非线性方程组

将非线性方程组的求解问题转化为函数优化问题,且综合考虑了拟牛顿法和遗传算法各自的优点,提出了一种用于求解非线性方程组的混合遗传算法。该混合算法充分发挥了拟牛顿法的局部搜索、收敛速度快和遗传算法的群体搜索、全局收敛的优点。为了证明该混合遗传算法的有效性,选择了几个典型的非线性方程组,从实验计算结果、收敛可靠性指标对比不同算法进行分析。数值模拟实验表明,该混合遗传算法具有很高的精确性和收敛性,是求解非线性方程组的一种有效算法。     

A hybrid genetic algorithm that optimizes capacitated vehicle routing problems

This study primarily focuses on solving a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) capable of practical use for manufacturers. The proposed HGA has three stages. First, the nearest addition method (NAM) was incorporated into sweep algorithm (SA) that simultaneously accounts for axial and radius relationships among distribution points with the depot to generate a well-structured initial chromosome population, rather than adopting either the NAM OR SA alone. Second, response surface methodology (RSM) was employed to optimize crossover probability and mutation probability via systematic experiments. Finally, an improved sweep algorithm was incorporated into the GA, producing a stir over gene permutations in chromosomes that enhance the exploration diversity of the GA, thereby avoiding convergence in a limited region, and enhancing the search capability of the GA in approaching a close-to-optimal solution. Furthermore, an elitism conservation strategy holding superior chromosomes to replace inferior chromosomes was also performed. As the proposed HGA is primarily used to solve practical problem, benchmark problems with fewer than 100 distribution points from an Internet website were utilized to confirm the effectiveness of the proposed HGA. A real case regarding the mission of local active distribution from armed forces in Taiwan details the analytical process and demonstrates the practicability of the proposed HGA to optimize the CVRP.     

Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence.

After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop.

Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.     

基于GA-PSO的天基预警系统资源调度方法

为解决天基预警系统中的卫星资源调度问题,从预警任务特点出发,在对预警任务进行分解的基础上,建立了资源调度模型．结合传统遗传算法（GA）和粒子群算法（PSO）的优点,采用一种混合遗传粒子群（GA-PSO）算法来求解资源调度问题．该算法在解决粒子编解码问题的前提下,将遗传算法的遗传算子应用于粒子群算法,改善了粒子群算法的寻优能力．实验结果表明,提出的算法能有效解决多目标探测时天基预警系统的资源调度问题,调度结果优于传统粒子群算法和遗传算法．     

A case study of scheduling storage tanks using a hybrid geneticalgorithm

This paper proposes the application of a hybrid genetic algorithm (GA) for scheduling storage tanks. The proposed approach integrates GAs and heuristic rule-based techniques, decomposing the complex mixed-integer optimization problem into integer and real-number subproblems. The GA string considers the integer problem and the heuristic approach solves the real-number problems within the GA framework. The algorithm is demonstrated for three test scenarios of a water treatment facility at a port and has been found to be robust and to give a significantly better schedule than those generated using a random search and a heuristic-based approach     

An effective hybrid genetic algorithm for flow shop scheduling with limited buffers

As a typical manufacturing and scheduling problem with strong industrial background, flow shop scheduling with limited buffers has gained wide attention both in academic and engineering fields. With the objective to minimize the total completion time (or makespan), such an issue is very hard to solve effectively due to the NP-hardness and the constraint on the intermediate buffer. In this paper, an effective hybrid genetic algorithm (HGA) is proposed for permutation flow shop scheduling with limited buffers. In the HGA, not only multiple genetic operators based on evolutionary mechanism are used simultaneously in hybrid sense, but also a neighborhood structure based on graph model is employed to enhance the local search, so that the exploration and exploitation abilities can be well balanced. Moreover, a decision probability is used to control the utilization of genetic mutation operation and local search based on problem-specific information so as to prevent the premature convergence and concentrate computing effort on promising neighbor solutions. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the HGA. Meanwhile, the effects of buffer size and decision probability on optimization performances are discussed.     

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution.     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.     

基于HGA算法的Rough集理论在故障诊断中的应用

知识约简问题是粗集理论的一个核心问题,文章提出了一种基于混合遗传算法的相对约简算法,把模拟退火融入到遗传算法中形成混合遗传算法,提高了遗传算法的优化效率,并在此基础上寻求最小条件属性集及最小属性值约简,论文最后以某导弹测控系统配电分系统故障诊断为例,证明该算法是一种行之有效的约简算法,从而为导弹系统的故障诊断提供了一条新思路.     

基于混合遗传算法的自动组卷问题的研究

针对遗传算法(GA)容易出现未成熟收敛和进化后期计算效率低的问题,提出了一种基于混合遗传算法(HGA)的智能组卷算法.将自适应遗传算法(AGA)与位爬山法相结合,提高组卷性能.在进化前期采用AGA进行全局寻优,增强GA的收敛速度同时避免GA的未成熟收敛.在进化后期启动位爬山法增强AGA的局部搜索能力.试验结果表明,HGA相对于AGA在有效性、稳定性和计算效率三方面都有较大提升,更能有效解决自动组卷问题,具有较好的使用性能和实用性.     

求解敏捷供应链调度优化问题的混合遗传算法

针对敏捷供应链调度决策中,需求的时间、数量约束和供应商生产能力、可用调度时段约束造成系统优化的复杂性,设计结合贪婪算法的混合遗传算法进行求解。算法以供应链系统库存成本和运输成本为适应度函数,以包含企业信息、部件信息和调度时段信息的时段编码作为遗传编码,以线性次序交叉LOX算子和逆序变异INV算子进行交叉和变异操作,在解码过程中结合贪婪算法进行调度决策和适应度计算,保证算法在满足约束条件的基础上快速收敛到系统Pareto最优解,通过算例验证算法的有效性。     

Glass container production scheduling through hybrid multi-population based evolutionary algorithm

Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.     

用混合遗传算法求解虚拟企业生产计划

针对虚拟企业生产计划的特点，以各成员企业承担的生产任务为对象，以快速响应市场为目标，建立了生产任务计划的数学模型，并基于该模型，提出一种基于遗传算法与模拟退火算法混合的求解算法，充分发挥了遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的优点．从而提高了算法的全局寻优能力．数值仿真计算表明了该算法的良好收敛性和有效性．     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.     

基于单纯形算子的混合遗传算法

通过遗传算法（GA）与传统单纯形搜索法相结 合,并基于对遗传算法算子计算结构的调整,提出一种针对非线性规划问题的新算法——基 于单纯形算子的混合遗传算法（HGA）,仿真结果验证了这种新算法的有效性和合理性．