流水工序调度与生产效率的关系模型分析

提出一种基于粒子群算法的流水工序调度任务优化模型。利用流水工序调度任务的特点得到流水工序时间约束条件,利用粒子群算法的原理建立流水工序调度任务优化模型,利用粒子群算法对模型进行求解。仿真实验表明,利用该算法能够得到流水工序调度问题的最优解,提高生产效率。     

粒子群算法和灰狼算法的融合

为了解决传统粒子群算法(PSO)容易“早熟”、陷入局部最优以及灰狼算法(GWO)收敛速度慢的问题。首先，采用GWO算法的个体极值更新策略来实现个体包围式向最优值趋近，融入PSO算法的速度更新策略来实现群体向最优值的趋近，并且在原始粒子群算法基础上加入线性惯性权重递减来提高算法的收敛速度，从而提出了一种基于灰狼算法和改进的粒子群算法(IPSO)的融合优化算法(GW-IPSO);其次，通过6个经典算例进行仿真试验，将融合算法与PSO算法、IPSD算法、灰狼和粒子群结合算法(GW-PSO)进行对比；最后，应用融合算法对二级直线倒立摆的控制器设计进行参数寻优。结果表明：针对6个标准测试函数，混合算法的30次试验结果平均值更接近最优值，且标准差几乎都是最小的；应用在倒立摆控制问题上，系统在5 s左右进入稳定状态。融合后的GW-IPSO算法能够在一定程度上避免早熟和陷入局部极值的问题发生，并且能够很好地应用于控制器设计过程中参数寻优问题。     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives – makespan and robustness – are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases.     

Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942–1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.     

基于改进粒子群的分布式通信干扰资源分配

为了解决分布式通信干扰场景下面临的资源分配效率低、干扰效益无保障等问题,结合通信干扰资源分配数学模型,设计了一种改进的粒子群算法。首先设计了分布式通信干扰场景并构建了通信干扰资源分配模型,以最大化干扰效益作为目标函数;其次采用自适应惯性因子和学习因子,并引入遗传变异策略和精英保留策略,提出一种改进的粒子群算法,最后对不同场景规模的通信干扰资源分配进行仿真实验。结果表明,相比小生境遗传算法、粒子群算法、遗传算法,改进的粒子群算法在不同场景规模下,均能获得更优的干扰效益,性能方面具备整体干扰效益更高、算法收敛速度更快、算法收敛误差更小等优势。所设计的改进粒子群算法可应用在分布式通信干扰场景中,为指挥决策提供参考。     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

基于SVR-IPSO的高危行业企业安全投入优化模型及其改进——以煤炭生产企业为例

传统的安全投入模型对解决高危行业领域中模糊复杂的安全投入问题具有一定局限性,尤其当建立目标函数时,采用隐含线性关系假设的函数进行拟合会影响模型的推广能力。基于此,本文首先采用支持向量回归机（SVR）建立事故损失模型,与传统C-D函数拟合结果相比,该模型具有更好的预测能力;然后,以实际安全投入要求为约束,以安全总成本最小化为原则建立企业安全投入优化模型;最后,采用基于捕食搜索策略的粒子群算法对模型进行求解,同时,为保证全局收敛性,引入自适应控制策略对算法进行了改进。结果表明：该模型能够更加准确地描述安全投入与安全成本间的非线性作用关系,并通过粒子群寻优得到具备可行性的全局最优解,为高危行业企业安全投入结构优化提供新的决策思路。     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

A multiple objective particle swarm optimization approach for inventory classification

This paper presents a particle swarm optimization approach for inventory classification problems where inventory items are classified based on a specific objective or multiple objectives, such as minimizing costs, maximizing inventory turnover ratios, and maximizing inventory correlation. In addition, this approach determines the best number of inventory classes and how items should be categorized for the desired objectives at the same time. Experiments are employed to determine the best combination of algorithm parameter values. Extensive numerical studies are conducted and results are compared to other known classification methods. The performance of the algorithm on a practical case is also presented.     

Metaheuristic-based simulation optimization approach to network revenue management with an improved self-adjusting bid price function

Making accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach.     

基于改进粒子群算法的无人机航迹规划

针对当前基本粒子群算法无人机航迹规划在后期收敛速度比较慢、效率不高、易陷入局部最优等问题,提出一种改进粒子群算法。首先,在迭代前期和后期分段设置惯性权值的调整,实现粒子惯性和寻优行为的平衡;其次,设置一个定值与相邻2次适应度函数最优值比较策略,防止陷入局部最优;最后,引入遗传算法的交叉、变异机制,得出更优的结果。并通过仿真验证了改进粒子群算法在三维空间航迹规划的有效性和可行性。结果表明,与其他航迹规划算法相比,新算法具有路径长度更短、耗时更少、路径更平滑等优点,加快了收敛速度,提高了航迹规划效率和稳定性。因此,改进算法的航迹规划可得到满足约束关系的最优航迹,对实现自主飞行有重要的参考价值。     

Optimization analysis of supply chain scheduling in mass customization

How to deal with the contradiction between scale production effect and customized demand is the key problem on studying mass customization (MC). When MC is operating in supply chain environment, on one hand, the excellent operating character of the supply chain will give conditions for solving this problem. On the other hand, it will bring out several complicated contradictions and increase the difficulties of the analysis and research on the supply chain operating and scheduling, so it is important to settle the contradictions. Based on our earlier work, the dominant contradictions of the supply chain scheduling in MC and the ways to relieve them are briefly summarized in this paper. A dynamic and multi-objective optimization mathematical model and the appropriate solving algorithm are set up by introducing these relieving methods into the operating process. It is pointed out that the characteristics of the model and algorithm cannot only reflect the unique operating requirements for this special production mode, but also merge with the thought of relieving the dominant contradictions. The feasibility of the model and algorithm in practical application to improve the scheduling efficiency and to settle the key problem mentioned above ultimately gets validated through the analysis of an application case we followed and through the algorithm simulation of a numerical scheduling case.     

Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

基于CPSO的基础设施模糊投资组合优化模型

针对基础设施效益模糊、难以度量的特点,结合模糊集理论,建立了模糊投资组合优化模型,改进粒子群算法,加入混沌思想,使用混沌粒子群算法(CPSO)求解基础设施的模糊投资组合优化模型。以4个城市投资公司的数据为样本,验证该方法的科学性与有效性。研究结果表明:模糊投资组合优化模型可较好地表征基础设施的模糊效益,提高基础设施投资决策的科学性;混沌寻优思想改进的粒子群算法可求得模糊投资组合优化模型的全局最优解,增强算法的鲁棒性。     

A modified artificial bee colony algorithm for order acceptance in two-machine flow shops

We consider a two-stage make-to-order production system characterized by limited production capacity and tight order due dates. We want to make joint decisions on order acceptance and scheduling to maximize the total net revenue. The problem is computationally intractable. In view of the fact that artificial bee colony algorithm has been shown to be an effective evolutionary algorithm to handle combinatorial optimization problems, we first conduct a pilot study of applying the basic artificial bee colony algorithm to treat our problem. Based on the results of the pilot study and the problem characteristics, we develop a modified artificial bee colony algorithm. The experimental results show that the modified artificial bee colony algorithm is able to generate good solutions for large-scale problem instances.     

基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。     

A tabu search approach for cell scheduling problem with makespan criterion

Scheduling problem in a cellular manufacturing system is treated as the group scheduling problem, assuming that intercellular moves can be eliminated by duplicating machines. However, in a typical CMS, duplicating bottleneck machines may be costly and infeasible. This fact limits the applicability of group scheduling. Scheduling problem in the presence of bottleneck machines is termed as cell scheduling. A mixed-integer linear programming model is proposed for the attempted cell scheduling problem and a nested application of tabu search approach is investigated in this paper to solve the problem heuristically. The effectiveness of the proposed nested tabu search (NTS) algorithm is evaluated on 16 problems selected from the literature. Comparison of the results of NTS with SVS-algorithm reveals the effectiveness and efficiency of the proposed algorithm.     

Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design

This paper proposes a new approach to determining the Supply Chain (SC) design for a family of products comprising complex hierarchies of subassemblies and components. For a supply chain, there may be multiple suppliers that could supply the same components as well as optional manufacturing plants that could assemble the subassemblies and the products. Each of these options is differentiated by a lead-time and cost. Given all the possible options, the supply chain design problem is to select the options that minimise the total supply chain cost while keeping the total lead-times within required delivery due dates. This work proposes an algorithm based on Pareto Ant Colony Optimisation as an effective meta-heuristic method for solving multi-objective supply chain design problems. An experimental example and a number of variations of the example are used to test the algorithm and the results reported using a number of comparative metrics. Parameters affecting the performance of the algorithm are investigated.