An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.     

Discrete artificial bee colony algorithm for lot-streaming flowshop with total flowtime minimization

Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony (DABC) algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.     

An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.     

An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed left-shift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.     

Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.     

基于离散人工蜂群算法的多目标分布式异构零等待流水车间调度方法

分布式制造(Distributed manufacturing,DM)已成为当前主流制造模式之一，广泛存在于航空、电子等行业实际生产中。在DM中，各个工厂存在诸如机器数量、机器工艺、原料运输条件的差别，即异构性。然而，目前的分布式调度研究中均未考虑工厂的异构性。因此，结合实际需求，研究一种考虑序列相关准备时间的多目标分布式异构零等待流水车间调度问题(Multi-objective distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup time,MDHNWFSP-SDST)。首先，建立了以最大完工时间和总滞后为目标的多目标优化模型。基于问题特征及多目标特性，提出了一种多目标离散人工蜂群算法(Multi-objective discrete artificial bee colony,MODABC)。其次，改进了PWQ算法(Improved PWQ,IPWQ)初始化种群，解决了PWQ存在的数量级和重复解等问题；在雇佣蜂阶段，结合分布式调度问题特点，设计了四种邻域...     

A hybrid genetic algorithm for solving no-wait flowshop scheduling problems

A hybrid genetic algorithm is proposed in this paper to minimize the makespan and the total flowtime in the no-wait flowshop scheduling problem, which is known to be NP-hard for more than two machines. The Variable Neighborhood Search is used as an improvement procedure in the last step of the genetic algorithm. First, comparisons are provided with respect to several techniques that are representative of the previous works in the area. Then, we compare the results given by three proposed algorithms. For the makespan criterion as well as for the total flowtime, the computational results show that our algorithms are able to provide competitive results and new best upper bounds.     

求解置换调度问题的改进混合遗传算法

构造了求解极小化总完工时间的置换调度问题的改进混合遗传算法：先采用构造型启发式算法和随机方法共同产生初始种群，然后在选择、交叉和变异等遗传操作之前借助禁忌搜索算法寻找每个个体的局部最优解组成当前种群，再应用种群整体替换策略保存种群中的优秀个体构成新一代种群。改进混合遗传算法有机地结合了禁忌搜索算法的局部搜索性能和遗传算法的全局搜索性能。仿真实验表明，改进混合遗传算法具有比构造型启发式算法和禁忌搜索算法更好的鲁棒性和寻优性能。     

Discrete harmony search algorithm for the no-wait flow shop scheduling problem with total flow time criterion

In this paper, a discrete harmony search algorithm (DHS) is developed for solving the no-wait flow shop scheduling problem with the objective to minimize total flowtime. Firstly, a harmony is represented as a discrete job permutation, and a new heuristic based on the well-known NEH method is proposed to initialize the harmony memory. Secondly, a novel pitch adjustment rule is employed in the improvisation to produce a new harmony. Thirdly, a local search is presented and embedded to enhance the algorithm's local exploitation ability. Extensive computational experiments are carried out based on a set of well-known benchmark instances. Computational results and comparison show the effectiveness of the presented DHS algorithm in solving the no-wait flow shop scheduling with total flowtime criterion.     

[工艺规划与装配线平衡]两阶段混合算法求解集成工艺规划与调度问题

提出了求解集成式工艺规划与车间调度问题的两阶段混合算法。在工艺规划阶段,使用遗传算法为每个工件生成可选的近优工艺路线集,动态地为车间调度阶段输入已确定的工艺路线;在车间调度阶段,使用蜜蜂交配优化算法快速寻优,设计了蜂王婚飞的流程以保证算法的全局搜索能力,构建了基于不同邻域结构的工蜂培育幼蜂局部搜索策略。使用基准测试集对提出的方法进行验证,并与现有算法进行对比,计算结果证明了提出方法的有效性。     

基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

针对多目标绿色柔性作业车间调度问题（MGFJSP）的特点,提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度,建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型,并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括：设计了一种三维向量的编码和对应解码方案,在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力,在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比,以验证所建模型和所提算法的有效性。     

A GRASP heuristic for the multi-objective permutation flowshop scheduling problem

This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization.     

Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function

This paper proposes a colonial competitive algorithm which is improved by variable neighborhood search algorithm for the simultaneous effects of learning and deterioration on hybrid flowshop scheduling with sequence-dependent setup times. By the effects of learning and deterioration, the processing time of a job is determined by position in the sequence and its execution start time. In addition, it is assumed that the processing time of any job depends on the number of workers assigned to the job on a particular stage, and the more workers assigned to a stage, the shorter the job processing time. These additional traits that are added to the scheduling problem coexist in many realistic scheduling situations. This problem consists of two basic questions of job scheduling and worker assignment. Minimization of the earliness, tardiness, makespan, and total worker employing costs is considered as the objective function. To evaluate the performance of the hybrid colonial competitive algorithm, the random key genetic algorithm, immune algorithm, variable neighborhood search, and hybrid simulated annealing metaheuristic presented previously are investigated for comparison purposes, and computational experiments are performed on standard test problems. Results show that our proposed algorithm performs better than the other algorithms for various test problems.     

Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer

This paper presents a dynamic multi-swarm particle swarm optimizer (DMS-PSO) for solving the blocking flow shop scheduling problem with the objective to minimize makespan. To maintain good global search ability, small swarms and a regrouping schedule were used in the presented DMS-PSO. Each small swarm performed searching according to its own historical information, whereas the regrouping schedule was employed to exchange information among them. A specially designed local search phase was added into the algorithm to improve its local search ability. The experiments based on the well-known benchmarks were conducted. The computational results and comparisons indicated that the proposed DMS-PSO had a better performance on the blocking flow shop scheduling problems than some other compared algorithms in the literature.     

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.     

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based a     

Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem

Lot streaming is the technique of splitting a given job into sublots to allow the overlapping of successive operations in multi-stage manufacturing systems thereby reducing production makespan. Several research articles appeared in literature to solve this problem and most of these studies are limited to pure flowshop environments where there is only a single machine in each stage. On the other hand, because of the applicability of hybrid flowshops in different manufacturing settings, the scheduling of these types of shops is also extensively studied by several authors. However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this paper, we aim to contribute in bridging the gap between the research efforts in flowshop lot streaming and hybrid flowshop scheduling. We propose a mathematical model and a genetic algorithm for the lot streaming problem of several jobs in multi-stage flowshops where at each stage there are unrelated parallel machines. The jobs may skip some of the stages, and therefore, the considered system is a complex generalized flowshop. The proposed genetic algorithm is executed on both sequential and parallel computing platforms. Numerical examples showed that the parallel implementation greatly improved the computational performance of the developed heuristic.     

Heuristic search algorithms for lot streaming in a two-machine flowshop

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling.     

A fitness-based weighting mechanism for multicriteria flowshop scheduling using genetic algorithms

The most effective approximate methods to tackle the problem of scheduling jobs in a permutation flowshop with the bicriteria of makespan and maximum tardiness are based on genetic algorithms (GA). In these methods, the performance of the GA is improved by applying local search to all offsprings belonging to the current population. To do so, the two objectives must be aggregated into a single objective, which is usually accomplished by weighting them into a scalar function. Since the weighting scheme is considered to be critical for the performance of the algorithm, several weighting mechanisms have been suggested in the literature. In this paper, we propose two new weighting schemes and conduct an extensive computational experience under a variety of parameter settings in order to show their effectiveness as compared to existing ones.     

改进蜂群算法及其在圆度误差评定中的应用

针对基本人工蜂群算法(Artificial bee colony algorithm,ABC)的缺点,提出一种改进人工蜂群算法(Improved artificial bee colony algorithm,IABC),并应用于圆度误差最小区域评定中。该改进算法利用信息熵初始化种群,增强种群的多样性,并在引领蜂和跟随蜂搜索阶段,提出一种新的搜索策略,平衡算法的探索与开发能力。详细阐述IABC算法的基本原理与实现步骤,给出圆度误差满足最小包容区域条件的优化目标函数和收益度函数。通过基准测试函数验证IABC算法的有效性和准确性;通过对由三坐标机测得的多组测量数据进行圆度误差评定试验,结果表明IABC算法的评定精度优于最小二乘法、遗传算法以及粒子群算法等其他优化算法,且在求解质量和稳定性上优于ABC算法,验证了IABC算法不仅正确,而且适用于圆度误差的评定优化。