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.     

Ant colony optimization for unrelated parallel machine scheduling

Meeting due dates is a major issue in most manufacturing systems, and one effective measure for due dates is total weighted tardiness. In this research, we consider an ant colony optimization (ACO) algorithm incorporating a number of new ideas (heuristic initial solution, machine reselection step, and local search procedure) to solve the problem of scheduling unrelated parallel machines to minimize total weighted tardiness. The problem is NP-hard in the strong sense, because the single machine case is already NP-hard in the strong sense. Computational results show that the proposed ACO algorithm outperforms other existing algorithms in terms of total weighted tardiness.     

A meta-heuristic approach to single machine scheduling problems

A new meta-heuristic evolutionary algorithm, named a memetic algorithm, for solving single machine total weighted tardiness scheduling problems is presented in this paper. Scheduling problems are proved to be NP-hard (Non-deterministic polynomial-time hard) types of problems and they are not easily or exactly solved for larger sizes. Therefore, application of the meta-heuristic technique to solve such NP hard problems is pursued by many researchers. The memetic algorithm is a marriage between population-based global searches with local improvement for each individual. The algorithm is tested with benchmark problems available in the OR (operations research) library. The results of the proposed algorithm are compared with the best available results and were found to be nearer to optimal. The memetic algorithm performs better than the heuristics like earliest due date and modified due date.     

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.     

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.     

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.     

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.     

A nondominated ranked genetic algorithm for bi-objective single machine preemptive scheduling in just-in-time environment

Motivated by just-in-time (JIT) manufacturing, we study the bi-objective scheduling problem of minimizing the total weighted earliness and the number of tardy jobs on a single machine, in which machine idle time and preemption are allowed. The problem is known to be NP-hard. In this paper, we propose a new mathematical model, with nonlinear terms and integer variables which cannot be solved efficiently for medium- and large-sized problems. A method combining the new ranked-based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named nondominated ranking genetic algorithm (NRGA), has been presented to find nondominated solutions in a reasonable time. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The solutions obtained via NRGA are compared against solutions obtained via ε-constraint method in small-sized problems. Experimental results show that the proposed NRGA is competitive in terms of the quality and diversity of solutions in medium- and large-sized problems.     

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.     

基于工件聚类的并行机调度算法及其应用

针对纺织生产广泛存在的带工件释放时间、以最小化总拖期工件数和总拖期时间为目标的大规模并行机调度问题,提出一种基于工件聚类的遗传算法。该算法将求解过程分为工件聚类和工件排序两个阶段。在工件聚类阶段,基于影响并行机调度性能的重要调度特征量,采用改进的模糊C-均值聚类方法将所有待上机工件分为多个聚类;在工件排序阶段,采用基于规则编码的遗传算法,优化各聚类内工件的加工顺序。数值计算结果及实际应用效果表明,所提出的算法适用于求解带工件释放时间的大规模并行机调度问题。     

求解具有预防性维护的并行机调度的遗传算法

为了避免设备出现故障对生产造成损失,需对设备进行有效的预防性维护。本文研究了工件带有释放时间的预防性维护的并行机调度问题,以最小化总工期为优化目标。对该问题设计了一个遗传算法进行求解。染色体为工件序列和机器序列,采用最先适配启发式方法确定各工件的最优时间表。数值实验结果表明,本文设计的遗传算法的性能优于简单启发式算法——最短加工时间的最先适配算法,且计算时间是可接受的。     

模具电火花加工车间的非同等并联机混合遗传算法

针对模具企业电火花加工车间的生产调度特点,提出了一种非同等并联机的调度数学模型.与此同时,建立了一种遗传与模拟退火的混合算法,并应用于模具企业电火花加工车间,实际算例说明了该数学模型和求解方法的可靠性和有效性.     

An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines

In this paper, we address the hybrid flow shop scheduling problems with unrelated parallel machines, sequence-dependent setup times and processor blocking to minimize the makespan and maximum tardiness criteria. Since the problem is strongly NP-hard, we propose an effective algorithm consisting of independent parallel genetic algorithms by dividing the whole population into multiple subpopulations. Each subpopulation will be assigned with different weights to search for optimal solutions in different directions. To further cover the Pareto solutions, each algorithm is combined with a novel local search step and a new helpful procedure called Redirect. The proposed Redirect procedure tries to help the algorithm to overcome the local optimums and to further search the solution space. When a population stalls over a local optimum, at first, the algorithm tries to achieve a better solution by implementing the local search step based on elite chromosomes. As implementing the local search step is time-consuming, we propose a method to speed up the searching procedure and to further increase its efficiency. If the local search step failed to work, then the Redirect procedure changes the direction and refreshes the population. Computational experiments indicate that our proposed improving procedures are thriving in achieving better solutions. We have chosen two measures to evaluate the performance of our proposed algorithms. The obtained results clearly reveal the prosperity of our proposed algorithm considering both measures we have chosen.     

A block approach to earliness-tardiness scheduling problems

In this paper, we consider a single-machine job scheduling problem where the objective is to minimize the weighted sum of earliness and tardiness (E/T) penalties of jobs. This problem is consistent with the just-in-time (JIT) production. We propose partitioning of permutation into subsequences (blocks) and replacing sets of moves with its representatives, significantly decreasing the size of the searched neighborhood. Some new properties of the problem and compound moves make eliminating “bad” elements and speeding up calculations possible. These properties allow us to propose a new fast local search algorithm based on a tabu search method. Computational experiments are presented and the results show that the algorithm proposed allows us to obtain the best-known results in a short time.     

Solving flow shop scheduling problems by quantum differential evolutionary algorithm

This paper proposed a novel quantum differential evolutionary algorithm (QDEA) based on the basic quantum-inspired evolutionary algorithm (QEA) for permutation flow shop scheduling problem (PFSP). In this QDEA, the quantum chromosomes are encoded and decoded by using the quantum rotating angle and a simple strategy named largest rotating angle value rule to determine job sequence based on job’s quantum information is proposed for the representation of PFSP, firstly. Then, we merge the advantages of differential evolution strategy, variable neighborhood search and QEA by adopting the differential evolution to perform the updating of quantum gate and variable neighborhood search to raise the performance of the local search. We adopted QDEA to minimize the makespan, total flowtime and the maximum lateness of jobs and make the simulations. The results and comparisons with other algorithms based on famous benchmarks demonstrated the effectiveness of the proposed QDEA. Another contribution of this paper is to report new absolute values of total flowtime and maximum lateness for various benchmark problem sets.     

An intensive search evolutionary algorithm for single-machine total-weighted-tardiness scheduling problems

In this paper, an intensive search evolutionary algorithm is proposed to solve single machine total weighted tardiness scheduling problems. A specialised locally improved random swap mutation operator and an ordered crossover operator are used for evolution. The proposed algorithm starts with a pair of sequences: one generated by a greedy heuristic, namely, a backward phase heuristic acts as one parent, and a randomly generated sequence acts as the other. A computational experiment is conducted by applying the mutation operator on the backward phase sequence and the proposed algorithm with the same number of generations as the termination criteria. A total of 125 benchmark instances for sizes 40, 50 and 100 available in the OR library are solved and the results are compared with the available best-known results. It is observed that the proposed evolutionary algorithm provides better results than others .     

A new parallel genetic algorithm for solving multiobjective scheduling problems subjected to special process constraint

This study presents a multiobjective scheduling model on parallel machines (MOSP). Compared with other scheduling problems on parallel machines, the MOSP is distinct for the following characteristics: (1) parallel machines are nonidentical, (2) the type of jobs processed on each machine can be restricted, and (3) the multiobjective scheduling problem includes minimizing the maximum completion time among all the machines (makespan) and minimizing the total earliness/tardiness penalty of all the jobs. To solve the MOSP, a new parallel genetic algorithm (PIGA) based on the vector group encoding method and the immune method is proposed. For PIGA, its three distinct characteristics are as follows: Firstly, individuals are represented by a vector group, which can effectively reflect the virtual scheduling policy. Secondly, an immune operator is adopted and studied in order to guarantee diversity of the population. Finally, a local search algorithm is applied to improve the quality of the population. Numerical results show that it is efficient, can better overcome drawbacks of the general genetic algorithm, and has better parallelism.