Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm

This paper addresses the stochastic production demand problem in a manufacturing company. The objective of this research is to minimize the waiting time of production workstations and reduce stochastic production material problems through coordinating pickup and delivery orders in a warehouse. RFID technology is adopted to visualize the actual status of operations in production and warehouse environments. A mathematical model is developed to address this problem and a meta-heuristic algorithm using genetic algorithm (GA) is also developed to improve performance. Computational experiments are undertaken to examine the performance of the algorithm when dealing with congestion in cases of heavy and normal demand for production material. The overall result shows that the algorithm efficiently minimizes the total makespan of the production shop floor.     

基于改进遗传算法的机器人路径规划

标准遗传算法在解决各类优化问题中获得成功,但它在具体的应用中由于缺乏对特定知识的利用,其性能有待提高.针对机器人路径规划的实际应用,通过优化设计标准遗传算法中的交叉算子和变异算子,提出一种应用于机器人路径规划的改进型遗传算法.在把地图特征信息引入遗传算子的操作过程中提高了算法的进化效率.计算机仿真实验结果证明该算法在收敛速度、最优解输出概率方面相对于基本遗传算法有了显著提高.     

Integration of process planning and production scheduling based on genetic algorithm

Lacking of flexibility in the traditional workshop production, a genetic algorithm is proposed to implement the integration of process planning and production scheduling. In this paper, the processing routes and processing machine are selected through chromosome crossover and mutation, in order to implement the optimal scheduling of the flexible workshop production. Meanwhile, a performance test about the integration of process planning and production scheduling is implemented, and the results shows that the genetic algorithm is efficient to obtain optimal or near optimal process routes which can meet the requirements of production scheduling.     

用于游戏NPC路径规划的改进遗传算法

针对游戏非玩家控制(NPC)路径规划中传统遗传算法计算速度慢、正确率低等问题,设计了改进型遗传算法.提出了最佳种群规模估计方法,设计了基于精英主义思想的遗传算子.根据游戏地图的特点,引入了基于启发式深度优先搜索的变异操作.与传统遗传算法以及其他学者的改进算法进行了对比实验.实验结果表明:算法能够在保证正确率的前提下,提高计算速度,并且在多目标的环境下同样适用.     

知识引导遗传算法实现机器人路径规划

针对传统遗传算法求解机器人路径规划问题存在的收敛速度较慢的缺陷,设计一种知识引导遗传算法,在染色体的编码、初始种群的产生、各种遗传算子和优化算子中加入相关的领域知识.综合考虑机器人路径的长度、安全度和平滑度等性能指标,在对机器人进行路径规划的同时,利用删除、简化、修正和平滑4种优化算子进行路径优化操作.仿真结果表明,所提方法能够有效提高遗传算法求解实际路径规划问题的能力和效率.     

A heuristic genetic algorithm for product portfolio planning

Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.     

A novel congruent organizational design methodology using group technology and a nested genetic algorithm

A key concept in congruent organizational design is the so-called strategic grouping, which involves the aggregation of task functions, positions, and assets into units. Group technology (GT) has emerged as a manufacturing philosophy for improving productivity in batch production systems, while retaining the flexibility of a job shop production. In this paper, a methodology [nested genetic algorithm (NGA)] to group tasks and assets into several clusters [decision makers (DMs), command cells] is proposed; this methodology employs concepts from GT and genetic algorithms (GAs) to minimize the weighted total workload, measured in terms of intra-DM and inter-DM coordination workloads. The numerical results show that the proposed NGA approach obtains a near-optimal layout of the organization, i.e., the assignment of platforms to tasks and the patterns of coordination achieve a nice tradeoff between inter-DM and intra-DM coordination workload.     

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.     

基于遗传算法的主体规划实现

面向主体的软件开发是计算机科学与技术领域的一项重要技术.近年来,在主体体系结构,主体规划、主体通信语言等方面的研究已经得到了一系列进展.探讨了主体规划的实现问题,提出将遗传算法的思想用在主体每一步的动作的选择中.给出了基于遗传算法的主体规划算法,并结合一个应用实例展示了具体的实现过程.     

Adaptive genetic algorithm for advanced planning in manufacturing supply chain

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach. Received: June 2005 / Accepted: December 2005     

A Tabu-enhanced genetic algorithm approach for assembly process planning

Over the past decade, much work has been done to optimize assembly process plans to improve productivity. Among them, genetic algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimization methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighborhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu search (TS) constitutes a meta-procedure that organizes and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behavior can possibly be obtained by incorporating the Tabus intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.     

Process planning optimization for parallel drilling of blind holes using a two phase genetic algorithm

Determining the optimal process parameters and machining sequence is essential in machining process planning since they significantly affect the cost, productivity, and quality of machining operations. Process planning optimization has been widely investigated in single-tool machining operations. However, for the research reported in process planning optimization of machining operations using multiple tools simultaneously, the literature is scarce. In this paper, a novel two phase genetic algorithm (GA) is proposed to optimize, in terms of minimum completion time, the process parameters and machining sequence for two-tool parallel drilling operations with multiple blind holes distributed in a pair of parallel faces and in multiple pairs of parallel faces. In the first phase, a GA is used to determine the process parameters (i.e., drill feed and spindle speed) and machining time for each hole subject to feed, spindle speed, thrust force, torque, power, and tool life constraints. The minimum machining time is the optimization criterion. In the second phase, the GA is used to determine the machining sequence subject to hole position constraints (i.e., the distribution of the hole locations on each face is fixed). The minimum operation completion time is the optimization criterion in this phase. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm in solving the process planning optimization problem for parallel drilling of blind holes on multiple parallel faces. In order to evaluate the performance of proposed algorithm, the simulation results are compared to a methodology that utilizes the exhaustive method in the first phase and a sorting algorithm.     

Trajectory planning for a manipulator with nonlinear Coulomb friction using a dynamically incremental genetic algorithm

Global warming and environmental destruction are caused in part by the mass consumption of energy by industries that use robotic manipulators. Hence, there is a need to minimize the energy used for manipulator control systems. It is relatively easy to analytically obtain an optimal solution for a linear system. However, a multi-link manipulator is governed by a nonlinear dynamical equation that is difficult to solve as a two-point boundary value problem. Here, the manipulator angles are approximated by Taylor and Fourier series, whose coefficients are sought by a genetic algorithm (GA) to optimize the objective function subject to the boundary conditions. A search method is proposed for planning the trajectory of a manipulator with nonlinear friction and geometrical constraints.     

用遗传算法优化航班规划问题

运用遗传算法解决了飞机调度问题中的航班规划问题.通过对求解变量,即各机型在航线上的航次的整数编码,实现了求解目标在遗传算法中的表达.并且通过利用基于惩罚方式的有效修补策略对染色体的实用性进行修补,减少非法解的出现,使得求解结果符合实际情况.最后通过了广州白云国际机场与其它城市间的航线航班的规划实例,验证了所采用的算法是正确、有效的,具有很强的可扩展性和实用性.     

基于混沌遗传算法的机器人路径规划方法研究

针对机器人路径规划中,应用遗传算法时容易陷入局部最优解以及收敛速度较慢等问题,设计出一种基于混沌遗传算法的路径规划方法。在基本遗传算法的基础上采用自适应调整的选择概率,并引入混沌操作,从而增强移动机器人路径规划算法的鲁棒性,解决一般遗传算法的早熟和收敛速度慢问题。经MATLAB仿真,证明该方法具有良好的避障性能。     

Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling

Journal of Intelligent Manufacturing - To adapt to the flexibility characteristics of modern manufacturing enterprises and the dynamics of manufacturing subsystems, promote collaboration in...     

基于改进遗传算法的狭窄空间路径规划

针对室内或地下等狭窄而复杂环境下的移动机器人全局路径规划,提出了一种基于Dijkstra算法的改进遗传算法路径规划策略,以解决传统遗传算法在狭窄环境下难以有效初始化的问题。首先借助Dijkstra算法得出基准路径,然后以此基准路径为基础,通过改进的编码方式与搜索空间进行初始种群的编码,最后通过遗传算法获得最优路径。提出了全局通行度和路径安全度的概念,用来评估当机器人不可视为质点时的环境状态与路径优劣。仿真实验结果表明,与传统遗传算法和人工势场法相比,本方法在保证路径距离较短的情况下,能使路径安全度提高50%以上,或者将时间复杂度降低一半以上,表明了所提方法的实用性和有效性。     

遗传算法在UMTS自动小区规划中的应用

针对需要高维优化的通用移动通信系统(UMTS)无线网络自动小区规划(ACP)问题,应用了改进的遗传算法.该算法用特殊的正交方法产生初始解,采用精英选择策略,并自适应地改变交叉概率和变异概率的值.仿真结果显示:相对于其他遗传算法,该算法的性能有较大的提升,可以更加有效地找到高维UMTS-ACP问题的优化解.     

A modified genetic algorithm for maximizing handling reliability and recyclability of distribution centers

Nowadays, many 3PL providers usually equip their distribution centers with different facilities, enabling them to be specialized in handling certain products types, and enhancing their ability of reuse and recycle the waste produced from packaging and repackaging. In practice, this problem type has been attracted much attention by researchers and environmental protectionisms. More importantly, because of the difference in product handling specialty, this induces different processing efficiency, handling reliability, and costs. In this connection, the objective of this paper is to propose a modified genetic algorithm to deal with the problem. The new chromosome encoding enhances the searching ability of the genetic algorithm in finding location, allocation, and routing solutions with high handling reliability and recycling ability for the distribution centers. To test the optimization reliability of the modified genetic algorithm, a number of numerical experiments have been carried out. The results demonstrated that the modified algorithm is able to obtain the Pareto solutions under multi-criterion decision making. Meanwhile, the handling reliability and recycling of the distributed centers are increased and the overall performance of the distribution network is improved.