Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

基于空间缩放和吸引子的粒子群优化算法

为解决粒子群优化(Particle Swarm Optimization,PSO)算法中粒子越界、算法进化后期收敛速度慢和早熟收敛的问题,通过分析PSO算法中粒子运动行为和算法稳定性,提出了一种基于空间缩放和吸引子的粒子群优化(PSO with search space zoomed factor and attrac...     

A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences

The timetabling problem at universities is an NP-hard problem concerned with instructor assignments and class scheduling under multiple constraints and limited resources. A novel meta-heuristic algorithm that is based on the principles of particle swarm optimization (PSO) is proposed for course scheduling problem. The algorithm includes some features: designing an ‘absolute position value’ representation for the particle; allowing instructors that they are willing to lecture based on flexible preferences, such as their preferred days and time periods, the maximum number of teaching-free time periods and the lecturing format (consecutive time periods or separated into different time periods); and employing a repair process for all infeasible timetables. Furthermore, in the original PSO algorithm, particles search solutions in a continuous solution space. Since the solution space of the course scheduling problem is discrete, a local search mechanism is incorporated into the proposed PSO in order to explore a better solution improvement. The algorithms were tested using the timetabling data from a typical university in Taiwan. The experimental results demonstrate that the proposed hybrid algorithm yields an efficient solution with an optimal satisfaction of course scheduling for instructors and class scheduling arrangements. This hybrid algorithm also outperforms the genetic algorithm proposed in the literature.     

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.     

Integrated process planning and scheduling by an agent-based ant colony optimization

This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm.     

An improved PSO algorithm with genetic and neighborhood-based diversity operators for the job shop scheduling problem

The job shop scheduling problem (JSSP) is an important NP-hard practical scheduling problem that has various applications in the fields of optimization and production engineering. In this paper an effective scheduling method based on particle swarm optimization (PSO) for the minimum makespan problem of the JSSP is proposed. New variants of the standard PSO operators are introduced to adapt the velocity and position update rules to the discrete solution space of the JSSP. The proposed algorithm is improved by incorporating two neighborhood-based operators to improve population diversity and to avoid early convergence to local optima. First, the diversity enhancement operator tends to improve the population diversity by relocating neighboring particles to avoid premature clustering and to achieve broader exploration of the solution space. This is achieved by enforcing a circular neighboring area around each particle if the population diversity falls beneath the adaptable diversity threshold. The adaptive threshold is utilized to regulate the population diversity throughout the different stages of the search process. Second, the local search operator based on critical path analysis is used to perform local exploitation in the neighboring area of the best particles. Variants of the genetic well-known operators “selection” and “crossover” are incorporated to evolve stagnated particles in the swarm. The proposed method is evaluated using a collection of 123 well-studied benchmarks. Experimental results validate the effectiveness of the proposed method in producing excellent solutions that are robust and competitive to recent state-of-the-art heuristic-based algorithms reported in literature for nearly all of the tested instances.     

A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.     

A hybrid particle swarm optimization for job shop scheduling problem

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson’s heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.     

Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.     

基于混合粒子群优化算法的现金流优化

以最大化现金流净现值为优化目标的多模式资源约束调度问题MMRCPSP(Multi-mode Resource-Constrained Project Scheduling Problem)是一类带有复杂非线性特征的NP-hard问题,传统粒子群算法在解决该类离散问题上具有一定局限性。从粒子群算法的优化原理出发,结合遗传算法,在粒子群算法中引入交叉和变异操作,得出一种应用于MMRCPSP现金流优化的快速、易实现的混合粒子群算法,拓宽了粒子群优化算法在离散优化领域的应用。仿真实验结果验证了算法的有效性和高效性。     

求解IPPS顺序柔性调度问题的模型与集成型调度算法研究

针对工艺规划与调度集成(integrated process planning and scheduling, IPPS)问题中的顺序柔性调度问题,提出了基于简单顺序关系的顺序柔性描述模型及调度模型,并改进遗传算法设计了集成型的顺序柔性调度算法。染色体编码同时采用简单顺序关系编码和基于工序的编码,并为两种编码分别设计了多种交叉和变异操作。为避免遗传算子产生违背工序顺序优先关系的不可行解,提出了顺序约束修正策略;针对遗传算法易过早收敛的缺陷,设计了自适应调节变量以强化种群多样性,并引入变邻域搜索算法改变解的搜索邻域,进一步搜索最优调度方案。三种不同规模的实验仿真验证了问题描述模型及调度算法的有效性。     

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.     

基于改进惯性权重的粒子群优化算法

惯性权重是粒子群算法中平衡全局搜索和局部搜索能力的重要参数,提出了一种基于改进惯性权重的粒子群优化算法。该算法在进化初期采用基于不同粒子不同维的动态自适应惯性权重策略,加快收敛速度,在进化后期采用线性递减权重策略,同时为防止陷入局优,适时引入混沌变异增加种群多样性。对5个典型测试函数的测试结果表明,NPSO在收敛速度、收敛精度、稳定性和全局搜索能力等方面比线性权重PSO(LDIWPSO)均有很大程度上的提高。     

求解独立任务调度的离散粒子群优化算法

针对独立任务调度问题,提出一种改进的离散粒子群算法,采用基于任务的编码方式,对粒子的位置和速度更新方法进行重新定义。为防止粒子群算法的早熟收敛,给出利用模拟退火算法的局部搜索能力在最优解附近进行精细搜索,以改善解的质量。仿真结果表明,与遗传算法和基本粒子群算法相比,该混合算法具有较好的优化性能。     

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Over the past decade, the particle swarm optimization (PSO) has been an effective algorithm for solving single and multi-object optimization problems. Recently, the chemical reaction optimization (CRO) algorithm is emerging as a new algorithm used to efficiently solve single-object optimization.In this paper, we present HP-CRO (hybrid of PSO and CRO) a new hybrid algorithm for multi-object optimization. This algorithm has features of CRO and PSO, HP-CRO creates new molecules (particles) not only used by CRO operations as found in CRO algorithm but also by mechanisms of PSO. The balancing of CRO and PSO operators shows that the method can be used to avoid premature convergence and explore more in the search space.This paper proposes a model with modified CRO operators and also adding new saving molecules into the external population to increase the diversity. The experimental results of the HP-CRO algorithm compared to some meta-heuristics algorithms such as FMOPSO, MOPSO, NSGAII and SPEA2 show that there is improved efficiency of the HP-CRO algorithm for solving multi-object optimization problems.     

多群体混合进化算法求解IPPS问题

针对工艺规划与调度集成（Integration of Process Planning and Scheduling, IPPS）问题求解复杂性,为提高求解效率,设计了包含探索种群,寻优种群和最优种群的多群体混合进化算法,通过运用混合遗传算法和基于聚类淘汰机制的差分进化算法分别更新探索种群中工艺链和加工顺序链,保持可行解多样性和差异性。然后利用克隆领域搜索算法完成寻优种群中可行解的克隆和领域搜索,进一步提高种群质量。最后按照精英保留策略更新最优种群获得全局最优解。并通过实例计算对比,结果显示算法搜索效率和求解质量均有明显改善,且稳定性较好,表明该算法求解IPPS问题的可行性及优越性。     

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．