Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.     

Genetic algorithms for studies on AS/RS integrated with machines

This paper proposes the use of genetic algorithms (GAs) for storage and retrieval sequencing in an automated storage and retrieval system that is integrated with machines. Further, it addresses the sequencing when several requests are available and a dual command cycle is performed. The proposed approach is compared with storage and retrieval heuristics such as random, first come first served, and nearest neighbour heuristics. GA rule as machine scheduling is compared with shortest processing time, most work remaining, least operations remaining, least processing time, least work remaining, most operations remaining and random. Case studies demonstrate the effectiveness of the approach.     

Workflow balancing in parallel machines through genetic algorithm

Workflow balancing helps to remove the bottlenecks present in a manufacturing system. A genetic algorithm (GA) is used to solve the parallel machine scheduling problem of the manufacturing system with the objective of workflow balancing. The performance of GA is compared with three workflow balancing strategies namely random (RANDOM), shortest processing time (SPT) and longest processing time (LPT). The relative percentage of imbalance (RPI) is adopted among parallel machines for evaluating the performance of these heuristics. The GA shows better performance for the combination of various job sizes and machines. A computer program has been coded on an IBM/PC compatible system in the C++ language for experimentation to a standard manufacturing system environment in operation.     

An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts

Computer-aided process planning is an important interface between computer-aided design and computer-aided manufacturing in computer-integrated manufacturing environments. In this paper, the complicated process planning is modeled as a combinatorial optimization problem with constraints, and a hybrid graph and genetic algorithm (GA) approach has been developed. The approach deals with process planning problems in a concurrent manner by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the global optimal objective. Graph theory accompanied with matrix theory, as the basic mathematical tool for operation sequencing, is embedded into the main frame of GA. The precedence constraints between operations are formulated in an operation precedence graph (OPG). The initial population composed of all feasible solutions is generated by an elaborately designed topologic sort algorithm to the OPG. A modified crossover operator guaranteeing only feasible offspring generated is used, two types of mutation strategies are adopted, and a heuristic algorithm is applied to adjust the infeasible plan generated by the mutation operator to the feasible domain. A case study has been carried out to demonstrate the feasibility and efficiency of the proposed approach.     

A genetic algorithm approach to machine flexibility problems in an ion plating cell

For a long time, manufacturing industries have been concentrating on increasing productivity by increasing the size of the workforce, but this scenario has changed in last decade since the introduction of the term flexibility. Now, the manufacturers realize that flexibility of the machine environment can provide a better economic solution for improving productivity due to its quick response to the changing environment in the manufacturing industry. However, only very limited research on machine flexibility in the ion plating (IP) industry has been carried out and most of it has focused on product development and quality of coating. The aim of this paper is to determine the optimal level of machine flexibility in an ion plating cell (IPC) to improve the entire system performance. A machine loading sequencing (MLS) model based on a multi-objective genetic algorithm (GA) is developed and the case study of metal finishing company is discussed to validate the proposed model. Different levels of machine flexibility have been assigned to different machines to determine the optimal level to increase the overall system performance based on on-time delivery, quality of product and production cost. The results demonstrated that machine flexibility level in IPC should be zero under recent IP technology. However, when the IP technology is developed enough so that IP machine has the ability to produce different types of coating in high quality, machine flexibility should be introduced to enhance the overall system performance.     

面向大规模定制的生产计划研究

激烈的市场竞争要求企业能快速响应客户的个性化产品需求,为单个客户或小批量多品种的市场定制生产产品。大规模定制能以大规模生产的成本和速度,为客户提供个性化设计的产品和服务。从大规模定制生产模式的特点出发,分析模块化制造物料清单(MBOM,Manufacturing Bill of Material)的基本构成。在MBOM模块化的基础上,基于延迟区分(Postponement Differentiation)策略建立大规模定制模式下的双重MPS计划管理模式,针对零部件的通用需求和定制需求分别制定计划,充分保证客户订单交货期,同时也提高了生产过程的柔性。     

Integrated setup planning and operation sequencing (ISOS) using genetic algorithm

Process planning is an essential component for linking design and manufacturing process. Setup planning and operation sequencing are two most important functions in the implementation of CAD/CAPP/CAM integration. Many researches solved these two problems separately. Considering the fact that the two functions are complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved economically and competitively. This paper presents a generative system and genetic algorithm (GA) approach to process plan the given part. The proposed approach and optimization methodology analysis constraints such as TAD?(tool?approach?direction), tolerance relation between features and feature precedence relations to generate all possible setups and operations using workshop resource database. Tolerance relation analysis has a significant impact in setup planning for obtaining the part accuracy. Based on technological constraints, the GA algorithm approach, which adopts the feature-based representation, simultaneously optimizes the setup plan and sequence of operations using cost indices. Case studies show that the developed system can generate satisfactory results in optimizing the integrated setup planning and operation sequencing in feasible condition.     

制造协作组织形成过程任务性能参数逆向优化

深入分析了制造协作组织形成过程的市场特性。针对产品制造任务性能参数的优化问题,提出正向优化与逆向优化过程模型,分析双向优化结构的宏观与微观特征。通过探讨可以发现,制造任务优化与制造资源配置是市场协作制造过程中的两个相互影响、密不可分的整体。通过针对逆向优化过程的研究,进行制造任务性能参数逆向优化过程的形式化描述,进一步构建任务参数优化的数学模型,并且运用矢量范数理论对模型的最优解存在性质进行论证。最后,在分析遗传算法的宏观与微观策略的基础上,利用基于实数编码的遗传算法对优化模型进行求解,用实例验证了优化方法与算法的有效性。     

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.     

基于PLC控制的MPS系统加工站设计

M PS系统是一种实现自动化生产加工单元的上料、搬运、加工、安装、分类等功能的模拟操作平台。对M PS的加工站单元进行总体结构及硬件和软件设计。运用PLC技术设计M PS系统加工站，采用西门子S7-200 CPU-224 PLC和EM223扩展模块实现旋转工作台的精确定位、钻孔模块加工模拟、检测模块质检、工作台物流传送。实际应用表明，所设计的加工站能够满足模块化生产系统的实用化要求。     

A Dynamic Tool Requirement Planning Model for Flexible Manufacturing Systems

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.     

Concurrent process/inspection planning for a customized manufacturing system based on genetic algorithm

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system. To assure product quality, finite manufacturing resources (i.e., process workstations and inspection stations) could be available and employed. The manufacturing resource allocation problem then occurs, therefore, process planning and inspection planning should be performed. Both of these are traditionally regarded as individual tasks and conducted separately. Actually, these two tasks are related. Greater performance of an advanced manufacturing system can be achieved if process planning and inspection planning can be performed concurrently to manage the limited manufacturing resources. Since the product variety in batch production or job-shop production will be increased for satisfying the changing requirements of various customers, the specified tolerance of each quality characteristic will vary from time to time. Except for finite manufacturing resource constraints, the manufacturing capability, inspection capability, and tolerance specified by customer requirement are also considered for a customized manufacturing system in this research. Then, the unit cost model is constructed to represent the overall performance of an advanced manufacturing system by considering both internal and external costs. Process planning and inspection planning can then be concurrently solved by practically reflecting the customer requirements. Since determining the optimal manufacturing resource allocation plan seems to be impractical as the problem size becomes quite large, in this research, genetic algorithm is successfully applied with the realistic unit cost embedded. The performance of genetic algorithm is measured in comparison with the enumeration method that generates the optimal solution. The result shows that a near-optimal manufacturing resource allocation plan can be determined efficiently for meeting the changing requirement of customers as the problem size becomes quite large.     

Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using longest processing time rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being a search algorithm, it can explore alternative schedules providing the same results.     

Scheduling of complex manufacturing systems with Petri nets and genetic algorithms: a case on plastic injection moulds

This paper introduces significant improvements on a previous published work that addresses complex production scheduling problems using Petri nets (PNs) and genetic algorithms (GAs). The PN model allows a formal representation of the manufacturing system and of the special constraints of this kind of system, while the GA generates a near-optimal schedule through the structure provided by the PN. The corresponding manufacturing system is associated with a flexible job shop environment with features such as the fabrication of multiple parts and precedence relationships between such parts and assembly operations, in which the objective is the minimisation of the total weighted tardiness. As part of the modelling stage, a mixed integer linear programming formulation is proposed for this framework. The fabrication of a chess mould in a Colombian company is used in two ways: to introduce a proposed normalisation operator that improves the results by reducing the search space of the GA and to illustrate the use of PN modelling the special aforementioned constraints as well as the encoding of the chromosome used by the GA. The proposed approach was tested on randomly generated instances, and their performance was measure against optimal solutions or solutions provided by algorithms presented in previous work. The results confirm the relevance of this approach to schedule such complex manufacturing systems.     

Optimization of flexible process planning by genetic programming

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this paper, a new method using an evolutionary algorithm, called genetic programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and genetic algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method ispromising and very effective in the optimization research of flexible process planning.     

A new manufacturing resource allocation method for supply chain optimization using extended genetic algorithm

In distributed manufacturing environments, the real competitive edge of an enterprise is directly related to the optimization level of its supply chain deployment in general, and, in particular, to how it allocates diverse manufacturing resources optimally. This is faced with increasing challenges caused by the conflicting objectives in manufacturing integration over distributed manufacturing resources. This paper presents a new manufacturing resource allocation method using extended genetic algorithm (GA) to support the multi-objective decision-making optimization for supply chain deployment. A new multi-objective decision-making mathematical model is proposed to evaluate, select, and sequence the candidate manufacturing resources allocated to sub-tasks composing the supply chain, by dealing with the trade-offs among multiple objectives including similarity, time, cost, quality, and service. An extended GA approach with problem-specific two-dimensional representation scheme, selection operator, crossover operator, and mutation operator is proposed to solve the mathematical model optimally by designing a chromosome containing two kinds of information, i.e., resource selection and resource sequencing. A case study is carried out to demonstrate the effectiveness and efficiency of the proposed approach.     

Optimisation of opportunistic maintenance of a multi-component system considering the effect of failures on quality and production schedule: A case study

For a manufacturing equipment, any unplanned breakdown during the production period results into a high production loss. To keep the manufacturing facilities in good condition, preventive maintenance is planned. However, because of limited time and availability of resources, not all the system components can be or need to be repaired/replaced during a planned opportunity. Hence, the unplanned breakdowns can also be considered as an opportunity to do the maintenance activities for other components to take the advantage of economic dependency in multi-component system. However, when the system is under maintenance, it is very conservative to take the decision of maintenance actions on the components because of limited available time and resources. For such situation, this paper consider an opportunistic maintenance model for a multi-component system to take maintenance decision with a constraint on available time and the system availability requirements. The maintenance decisions for each component involves one of the three actions namely, repair, replace or do nothing to achieve the target availability with minimum maintenance cost. The model also considers the effect of component failures on the quality of product being manufactured as well as the production schedule on the machine. The cost of rejections is considered in the total failure cost along with the maintenance and downtime costs. The production schedule delay factor is considered as a constraint for the maintenance decision to account for the effect on production schedule delay. The optimal solution for the model is obtained using three solution methodologies namely simulated annealing, genetic algorithm and sequence heuristics. Using a real-life example of high pressure die casting machine, the opportunistic maintenance approach is demonstrated and results are discussed.     

实时动态排产系统研究

针对众多企业在多品种小批量生产且缺乏稳定的主生产计划时难以实施MRPⅡ与JIT的困境，研究了一种新的生产计划与控制体系——实时动态排产系统。详细介绍了该系统的整体架构、订单处理流程和排产与插单算法。对所提出的两种排产算法及相应的插单算法进行了数值模拟，给出了不同算法的优缺点及应用条件。受运算速度的制约，该系统目前较适合产品结构简单、生产工序较少的企业。但随着计算机技术的发展与排产算法的进一步优化，该系统将会有更加广阔的应用前景。     

An artificial neural network approach to multiple-response optimization

In many manufacturing cases, engineers are required to optimize a number of responses simultaneously. A common approach for the optimization of multiple-response problems begins with using polynomial regression models to estimate the relationships between responses and control factors. Then, a technique for combining different response functions into a single scalar, such as a desirability function, is employed and, finally, an optimization method is used to find the best settings for the control factors. However, in certain cases, relationships between responses and control factors are far too complex to be efficiently estimated by polynomial regression models. In addition, in many manufacturing cases, engineers encounter qualitative responses, which cannot be easily stated in the form of numbers. An alternative approach proposed in this paper is to use an artificial neural network (ANN) to estimate the quantitative and qualitative response functions. In the optimization phase, a genetic algorithm (GA) is considered in conjunction with an unconstrained desirability function to determine the optimal settings for the control factors. Two manufacturing examples in which engineers were asked to optimize multiple responses from the semiconductor and textile industries are included in this article. The results indicate the strength of the proposed approach in the optimization of multiple-response problems.     

Analysis on critical success factors for agile manufacturing evaluation in original equipment manufacturing industry-an AHP approach

Manufacturing industries are facing challenges in the implementation of agile manufacturing in their products and processes. Agility is widely accepted as a new competitive concept in the manufacturing sector in fulfilling varying customer demand. Thus, evaluation of agile manufacturing in industries has become a necessity. The success of an organisation depends on its ability to manage finding the critical success factors and give them special and continued attention in order to bring about high performance. This paper proposes a set of critical success factors (CSFs) for evaluating agile manufacturing considered appropriate for the manufacturing sector. The analytical hierarchy process (AHP) method is applied for prioritizing the success factors, by summarizing the opinions of experts. It is believed that the proposed CSFs enable and assist manufacturing industries to achieve a higher performance in agile manufacturing so as to increase competitiveness.