一种求解变速机调度问题的混合蚁群优化算法

针对一类变速机总加权拖期调度问题,提出一种混合蚁群优化算法.引人单机拖期调度问题中性能良好的修正预计完成时间的一种修改版本启发式规则,计算信息素初值,有利于算法跳出局部极值,并在局部搜索阶段,采用单亲遗传算法基因移位算子,有效优化当代最优解.通过均匀试验设计和统计分析,确定算法的关键参数组合,将算法应用于随机生成的不同规模的40个算例,并将其结果与同类文献中算法的优化结果进行对比分析.结果表明,在相同迭代次数下,混合算法优于对比算法.     

求解卫星任务调度问题的时间片蚁群算法

为更好地求解卫星任务调度问题,提出一种时间片蚁群算法.在算法中引入任务时间片,使算法可分辨任务在不同时间窗内的执行情况;在任务分配中设计了带偏好的卫星片切割策略,改变了以往的任务分配搜索模式,极大地提高了算法的执行速度.相对于传统的蚁群算法和遗传算法,所提方法在求解卫星任务调度时具有较大优势.     

Dispatching rules for unrelated parallel machine scheduling with release dates

A simplified procedure is proposed to predict the surface integrity of complex-shape parts generated by ball-end finishing milling. Along a complex cutting path, the tool inclination may vary within a large range. A geometrical study is performed to predict the effect of the tool inclination (lead angle) on the micro-geometry of the machined surface and on the effective cutting speed. This geometrical study brings out a range of values of the lead angle for which the machined surface is damaged by cutting pull-outs. This geometrical study also brings out a range of values of the lead angle for which the effective cutting speed is null. This case corresponds to extreme values of the cutting forces and to high compressive residual stresses. These predictions are verified for a selection of tool inclinations and other cutting parameters such as cutting speed, feed per tooth and cusp height. These machining tests are performed on a high-strength bainitic steel. The experimental campaign includes milling tests with cutting forces measurements, 2-D optical micro-geometry measurements and X-ray diffraction measurements.     

Ant colony optimization technique for the sequence-dependent flowshop scheduling problem

In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics.     

Hybrid metaheuristics for unrelated parallel machine scheduling with sequence-dependent setup times

This paper proposes several hybrid metaheuristics for the unrelated parallel-machine scheduling problem with sequence-dependent setup times given the objective of minimizing the weighted number of tardy jobs. The metaheuristics begin with effective initial solution generators to generate initial feasible solutions; then, they improve the initial solutions by an approach, which integrates the principles of the variable neighborhood descent approach and tabu search. Four reduced-size neighborhood structures and two search strategies are proposed in the metaheuristics to enhance their effectiveness and efficiency. Five factors are used to design 32 experimental conditions, and ten test problems are generated for each condition. Computational results show that the proposed hybrid metaheuristics are significantly superior to several basic tabu search heuristics under all the experimental conditions.     

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.     

用于供水系统直接优化调度的蚁群改进算法

在城市供水系统中建立了多目标在线直接优化调度模型,并对影响优化调度的各方面因素进行了系统的分析和挑选。使用化多为一的乘除法,将该多目标决策问题转化为单目标问题求解,提出了使用乘法形式的罚函数将模型中的约束函数转化为目标函数。采用蚁群算法求解调度模型。为了更好地得到全局最优解,对算法进行了改进,加入了更多的决策点,实现蚁群算法的二进制编码方法,并采用单只最优蚂蚁更新路径上的外激素值、外激素值限定在一定范围内等改进方法。使用改进算法实现了某小区供水系统的直接优化调度,并与遗传算法优化调度的过程进行了对比,新算法在优化时间及得到最优解的次数上都优于遗传算法。     

A hybrid particle swarm optimization for parallel machine total tardiness scheduling

The parallel machine scheduling problem has received increasing attention in recent years. This research considers the problem of scheduling jobs on parallel machines with a total tardiness objective. In the view of its non-deterministic polynomial-time hard nature, the particle swarm optimization (PSO), which is inspired by the swarming or collaborative behavior of biological populations, is employed to solve the parallel machine total tardiness problem (PMTP). Since it is very hard to directly apply standard PSO to this problem, a new solution representation is designed based on real number encoding, which can conveniently convert the job sequences of PMTP to continuous position values. Moreover, in order to enhance the performance of PSO, we introduce clonal selection algorithm (CSA) into PSO and therefore propose a new CSPSO method. The incorporation of CSA can greatly improve the swarm diversity and avoid premature convergence. We further investigate three parameters of PSO and CSPSO, finding that the parameters have marginal impact on CSPSO, which indicates that CSPSO is a very stable and robust method. The performance of CSPSO is evaluated in comparison with traditional genetic algorithm (GA) and standard PSO on 250 benchmark instances. Experimental results show that CSPSO significantly outperforms GA and PSO, with obtaining the optimal solutions of 237 instances. Additionally, PSO appears more effective than GA.     

并行机调度问题粒子群优化研究

采用粒子群算法优化并行机调度问题,提出了基于机器和粒子位置取整的粒子编码方法和基于工件和粒子位置次序的粒子编码方法,并给出了两种不同粒子编码方法所对应的粒子群算法的步骤.通过对两个并行机算例的计算说明,基于两种不同编码方法的粒子群算法都能有效地对并行机调度问题进行优化,其中,基于工件和粒子位置次序的粒子编码所对应粒子群算法的优化性能要好些.     

A Pareto evolutionary algorithm approach to bi-objective unrelated parallel machine scheduling problems

This paper addresses the unrelated parallel machine scheduling problem with job sequence- and machine-dependent setup times. The preemption of jobs is not permitted, and the optimization criteria are to simultaneously minimize total weighted flow time and total weighted tardiness. The problem has applications in industries such as TFT-LCD, automobile, and textile manufactures. In this study, a Pareto evolutionary approach is proposed to solve the bi-objective scheduling problem. The performance of this approach using different encoding and decoding schemes is evaluated and is compared with that of two multi-objective simulated annealing algorithms via a set of instances generated by a method in the literature. The experimental results indicate that the Pareto evolutionary approach using random key representation and weighted bipartite matching optimization method outperforms the other algorithms in terms of closeness metric, based on similar computation times. Additionally, although the proposed method does not provide the best distribution in terms of diversity metric, it found most of the reference solutions.     

一类并行机调度问题的动态调度算法

针对不确定制造环境中配件数量约束条件发生变化后的并行机动态调度问题,提出了一种基于操作属性模式的并行机动态调度算法.该算法针对总拖期时间性能指标的优化,根据配件负载的裕量和相邻操作的属性模式,对原调度方案的操作次序和操作上机时间进行了调整.在不同操作和设备规模下,以及不同配件数量变化幅度下进行了数值计算.数值计算结果和实际应用结果表明,该算法是有效的,具有计算复杂度低、实时性好、对原调度算法不敏感的特点.     

Ant colony optimization for group technology applications

The problem of grouping of parts into part-families and that of machines into machine-cells has attracted the attention of many researchers particularly for medium variety of parts with medium production volume requirement, which traditionally required their production in batches for achieving economics of production. This kind of grouping, consequently offering benefits of mass production, was aimed to have independent cells processing ideally almost different sets of part-types. For this purpose, a number of approaches are available from various kinds of heuristics to mathematical programming formulations. Evolutionary methods such as neural network, genetic algorithm, and simulated annealing have also been tried and have been found to provide better grouping solutions with much less computational complexity. In the present paper, ant colony optimization approach with number of newer strategies, incorporating more generalised framework of ants’ behaviour, has been applied to the parts and machines grouping problems taken from the literature. The results obtained from their application were found to be encouraging and thus establish the usefulness of the proposed approaches. Average performance of Tabu search with multiple ants was found to be the best and thus the parameter values for this approach were also determined using design of experiments methodology.     

Ant colony optimization in continuous problem

Based on the analysis of the basic ant colony optimization and optimum problem in a continuous space, an ant colony optimization (ACO) for continuous problem is constructed and discussed. The algorithm is efficient and beneficial to the study of the ant colony optimization in a continuous space. __________ Translated from Mechine Design and Research, 2006, 22(2): 6–8, 12 [译自: 机械设计与研究]     

Ant colony system for job shop scheduling with time windows

Scheduling for a job shop production system is an integral aspect of production management. Scheduling operations must minimize stock, waste, and idle time and ensure on-time delivery of goods in a time window problem. In this study, due date is considered as an interval instead of a time point. This study addresses scheduling with a time window of job shop scheduling problem (JSP) and yields a solution that is close to the time window. The total penalty due to earliness and tardiness is minimized. As the problem is NP-hard, a mathematical model of the JSP with a time window is initially constructed, and data are then simulated. Solutions are obtained by ant colony optimization (ACO) programs written in C-language and are compared with the best solution obtained using LINGO 7.0 to determine the efficiency and robustness. Test results indicate that ACO is extremely efficient. Solution time using ACO is less than that using LINGO. Hence, ACO is both effective and efficient, which are two qualities stressed in business management.     

Ant colony optimization for assembly sequence planning based on parameters optimization

Frontiers of Mechanical Engineering - As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing...     

Ant colony optimization for disassembly sequencing with multiple objectives

Product disassembly takes place in remanufacturing, recycling, and disposal. The disassembly line is the best choice for automated disassembly, so it is essential that it be designed and balanced to work efficiently. The multi-objective disassembly line balancing problem seeks to find a disassembly sequence which provides a feasible disassembly sequence, minimizes the number of workstations, minimizes idle time, balances the line (ensures similar idle times at each workstation), as well as addressing other disassembly-specific concerns. However, finding the optimal balance is computationally intensive due to exponential growth, with exhaustive search quickly becoming prohibitively large. In this paper, an ant colony optimization metaheuristic is presented for obtaining optimal or near-optimal solutions to the disassembly line balancing problem. Examples are considered to illustrate implementation of the methodology. Conclusions drawn include the consistent generation of near-optimal solutions, the ability to preserve precedence, the superior speed of the metaheuristic, and its practicality due to its ease of implementation.     

A tabu search algorithm for unrelated parallel machine scheduling with sequence- and machine-dependent setups: minimizing total tardiness

This study considers the problem of scheduling independent jobs on unrelated parallel machines with machine- and sequence-dependent setup times for the objective of minimizing the total tardiness, i.e., R _m │S _ijk │∑T _j . Since the parallel machines are unrelated, sequence-dependent setup times must depend on machines. To the best of the authors’ knowledge, the simulated annealing and the iterated greedy algorithms are two existing ones for the new class of scheduling problem with an additional constraint of strict due date constraints for some jobs, i.e., deadlines. In this study, we suggest a tabu search algorithm that incorporates various neighborhood generation methods. A computational experiment was done on the instances generated by the method used in the two previous research articles, and the results show that the tabu search algorithm outperforms the simulated annealing algorithm significantly. In particular, it gave optimal solutions for more than 50 % of small-sized test instances. Also, an additional test was done to compare the performances of the tabu search and the existing iterated greedy algorithms, and the result shows that the tabu search algorithm gives quicker solutions than the iterated greedy algorithm although it gives less quality solutions.     

Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem

In recent years, decision makers give more importance to the maintenance function, viewing its substantial contribution to business productivity. However, most literature on scheduling studies does not take into account maintenance planning when implementing production schedules. The achievement of production plan without taking into account maintenance activities increases the probability of machine breakdowns, and inversely, considering maintenance actions in production planning elongates the achievement dates of orders and affects deadlines. In this paper, we propose a bi-objective model to deal with production scheduling and maintenance planning problems simultaneously. The performance criteria considered for production and maintenance are, respectively, the total tardiness and the unavailability of the production system. The start times of preventive maintenance actions and their number are not fixed in advance but considered, with the execution dates of production tasks, as decisions variables of the problem. The solution of the integrated model is based on multi-objective ant colony optimization approach. The proposed algorithm (Pareto ant colony optimization) is compared, on the basis of several metrics, with well-known multi-objective genetic algorithms, namely NSGA-II and SPEA 2, and a hybrid particle swarm optimization algorithm. Interesting results are obtained via empirical study.