Solving a multi-objective overlapping flow-shop scheduling

In flow-shop manufacturing scheduling systems, managers attempt to minimize makespan and manufacturing costs. Job overlaps are typically unavoidable in real-life applications as overlapping production shortens operation throughput times and reduces work-in-process inventories. This study presents an ant colony optimization (ACO) heuristic for establishing a simple and effective mechanism to solve the overlap manufacturing scheduling problem with various ready times and a sequentially dependent setup time. In the proposed approach, the scheduling mechanism and ACO heuristics are developed separately, thereby improving the performance of overlapping manufacturing flow by varying parameters or settings within the ACO heuristics and allowing for flexible application of manufacturing by altering scheduling criteria. Finally, the experimental results of the scheduling problem demonstrate that the ACO heuristics have good performance when searching for answers.     

大规模定制下制造商延迟生产的成本优化模型

为解决大规模定制下延迟生产系统中制造商的生产成本优化问题,建立了大规模定制下制造商实施延迟生产的生产成本优化基本模型和扩展模型,以大规模定制下延迟生产系统的生产成本最小化为目标,综合考虑了固定资产的投资成本、制造成本、在制品持有成本、半成品库存成本,以及客户提前期问题.分析了客户订单分离点最佳位置及其决定因素,并指出了客户需求平均到达率、定制化产品种类数量,以及制造中心平均生产率对客户订单分离点最佳位置的影响.最后,对延迟生产系统的生产成本优化模型进行了仿真.     

Adaptive production control system for a flexible manufacturing cell using support vector machine-based approach

Real-time adaptive production control in the flexible manufacturing cell (FMC) is a complex issue that needs to be addressed to realize good performance and high productivity. In this paper, we have considered a support vector machine (SVM)-based simulation approach to resolve a production control problem in an FMC that operates in a dynamic environment. A SVM-based simulation approach chooses the most relevant scheduling rule out of several predefined ones on the basis of the current states of the system. This paper examines and compares the performance of the SVM-based simulation approach with the competent scheduling rules under two different operational environments which are characterized by the uncertainty of demand. We have also developed a Visual Basic-based simulation approach for scheduling of component parts in the context of FMC under different situations. The SVM methodology to control the production offers better performance than the single-rule-based production control system.     

Application of an efficient modified particle swarm optimization algorithm for process planning

In the modern manufacturing system, many flexible manufacturing system and NC machines are introduced to improve the production efficiency. Therefore, most parts have a large number of flexible process plans. However, a part can use only one process plan in the manufacturing process. So, the process planning problem has become a crucial problem in the manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the parts to be processed. It is a NP-hard problem. In order to solve this problem effectively, this paper proposes a novel modified particle swarm optimization (PSO) algorithm to optimize the process planning problem. To improve the performance of the approach, efficient encoding, updating, and random search methods have been developed. To verify the feasibility and effectiveness of the proposed approach, seven cases have been conducted. The proposed algorithm has also been compared with the genetic algorithm and simulated annealing algorithm. The results show that the proposed modified PSO algorithm can generate satisfactory solutions and outperform other algorithms.     

Models for capacity acquisition decisions considering operational costs

In this article, we study a capacity acquisition problem by considering technology choice and operational factors in a stochastic environment. The motivation for our work comes from developments in modern flexible technologies and a problem encountered in a real industrial setting. We study the impact of operational factors such as setup times, demand patterns, and inventory/back order costs on the decisions of capacity acquisition and technology choice. We consider three alternatives in capacity and technology decisions: (i) a flexible system, (ii) a dedicated system, and (iii) a combination of these two systems. For each system, we develop a model that integrates investment decisions and operational decisions to determine an optimal amount of capacity to purchase and the time and the types of parts to produce. The objective is to minimize the capacity acquisition cost at the beginning of the planning horizon and the total expected operational costs over an infinite planning horizon. To solve the problem in this article, a solution procedure is proposed. Managerial insights are also derived from extensive computational results.     

The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.     

Printed circuit board scheduling in an openshop manufacturing environment

Printed circuit boards are used in a myriad of commercial and consumer products in today’s marketplace. In this paper, we address the problem of inserting/placing components on printed circuit boards using automated insertion/placement machines. The motivation to examine this problem stems from joint work with a major electronics manufacturer who not only introduced us to the manufacturing environment but also provided data for testing and validating our component allocation and scheduling methodology. The results of our analysis can be used to trade-off operational savings of reduced cycle times versus tactical costs of reorganizing the equipment on the shop floor. Further, we also are able to develop and validate a three step hierarchical scheduling methodology for use in similar manufacturing environments.     

Incorporating CIM optimization in manufacturing management strategy

The objective of Computer Integrated Manufacturing (CIM) or the development of advanced manufacturing systems for a small to medium size company may not be total automation, but rather running a profitable business through improved product quality, improved design, reduction of inventory, shorter lead times and quick response to changes in the market demand. The implementation of the full range of Advanced Manufacturing Technologies (AMT) available to manufacturing industry would be expensive, risky and impractical. Management must find a way to make the transition from the current environment to one that includes several or all of the technologies of the factory of the future. The installation of new manufacturing systems or the modification of existing systems is a capital intensive operation that has been significantly influenced by computer control. As the complexity of the systems increases, so does the critical nature of cost effective alternative selection. In view of this intricately complex nature of most business systems, it has become increasingly difficult to evaluate new management concepts or system designs. Direct experimentation poses almost insurmountable problems due to disruptions, uncontrolled results, length of time required, and possibility of costly mistakes. Recognizing potential benefits and liabilities and measuring their significance is inherent to the usefulness of any CIM planning and implementation study. This paper describes progressive work carried out in constructing a strategy-based quantitative technique, which aims not only to formulate a strategy for CIM optimization (incorporating elements of a financial justification for the investment), but also identifies means of monitoring investment performance against strategy. The operational realization of such a strategy is discussed and illustrative examples are also presented.     

Manufacturing Cost Modeling for Product Design

The process of product design is driven toward achieving design specifications while meeting cost targets. Designers typically have models and tools to aid in functional and performance analysis of the design but few tools and little quantitative information to aid in cost analysis. Estimates of the cost of manufacture often are made through a cost multiplier based on material cost. Manufacturing supplies guidelines to aid in design, but these guidelines often lack the detail needed to make sound design decisions.A need was identified for a quantitative way for modeling manufacturing costs at Motorola. After benchmarking cost modeling efforts around the company, an activity-based costing method was developed to model manufacturing cycle time and cost. Models for 12 key manufacturing steps were developed. The factory operating costs are broken down by time, and cost is allocated to each product according to the processing it requires. The process models were combined into a system-level model, capturing subtle yet realistic operational detail.The framework was implemented in a software program to aid designers in calculating manufacturing costs from limited design information. Since the information tool provides an estimate of manufacturing costs at the design prototype stage, the development engineer can identify and eliminate expensive components and reduce the need for costly manufacturing processing. Using this methodology to make quantitative trade-offs between material and manufacturing costs, significant savings in overall product costs are achieved.     

Economic and strategic perspectives on investing in RMS and FMS

The evolution of manufacturing systems, according to changing internal and external conditions, requires design and assessment techniques that consider both strategic and financial criteria to evaluate the suitability of the Flexible and Reconfigurable system solutions in addressing these variations. In this paper, a fuzzy multi-objective mixed integer optimization model to evaluate RMS investments used in a multiple product demand environment is presented. The model incorporates in-house production and outsourcing options, machine acquisition and disposal costs, operational costs, and re-configuration cost and duration for the utilized modular machines. The resulting system configurations are optimized for lifecycle costs, responsiveness performance, and system structural complexity simultaneously. A complexity metric that incorporates the quantity of information using an entropy approach is used to represent the inherent structural complexity of the considered system configurations. It accounts for the complexity of the machine modules in a manufacturing system through the use of an index derived from a newly developed manufacturing systems classification code, which captures the effect machine types and technologies on the system’s structural complexity. A metric is proposed to measure the responsiveness ability and efficiency as well as the overall capability of each machine and effectiveness of machines changeover. The application of the developed planning and assessment model that incorporates these three criteria is illustrated with a case study where FMS and RMS alternatives were compared. The suitable conditions for investing in RMS are also discussed.

Batch sizes and lead-time performance in flexible manufacturing systems

Production lead-time performance in flexible manufacturing systems is influenced by several factors which include: machine groupings, demand rates, machine processing rates, product batching, material handling system capacity, and so on. Hence, control of lead-time performance can be affected through the manipulation of one or more of these variables. In this article, we investigate the potential of batch sizing as a control variable for lead-time performance through the use of a queueing network model. We establish a functional relationship between the two variables, and incorporate the relationship in an optimization model to determine the optimal batch size(s) which minimizes the sum of annual work-in-process inventory and final inventory costs. The nonlinear batch sizing problem which results is solved by discrete optimization via marginal analysis. Results show that batch sizing can be a cheap and effective variable for controlling flexible manufacturing system throughput.     

Dynamic cellular manufacturing systems design��a comprehensive model

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular manufacturing systems (DCMSs) under dynamic environment. The proposed model, to the best of the author??s knowledge, is the most comprehensive model to date with more integrated approach to the DCMSs. The proposed DCMS model integrates concurrently the important manufacturing attributes in existing models in a single model such as machine breakdown effect in terms of machine repair cost effect and production time loss cost effect to incorporate reliability modeling; production planning in terms of part inventory holding, part internal production cost, and part outsourcing; process batch size; transfer batch size for intracell travel; transfer batch size for intercell travel; lot splitting; alternative process plan, and routing and sequence of operation; multiple copies of identical copies; machine capacity, cutting tooling requirements, work load balancing, and machine in different cells constraint; machine in same cell constraint; and machine procurements and multiple period dynamic cell reconfiguration. Further, the objective of the proposed model is to minimize the sum of various costs such as intracell movement costs; intercell movement costs and machine procurement costs; setup cost; cutting tool consumption costs; machine operation costs; production planning-related costs such as internal part production cost, part holding costs, and subcontracting costs; system reconfiguration costs; and machine breakdown repair cost, production time loss cost due to machine breakdown, machine maintenance overheads, etc. ,in an integrated manner. Nonlinear terms of objective functions are transformed into linear terms to make mixed-integer linear programming model. The proposed model has been demonstrated with several problems, and results have been presented accordingly.     

Dynamic simulation for performance optimization in just-in-time-enabled manufacturing processes

Computer-aided simulation has received attention in recent years especially in the area of performance improvement in manufacturing environments. Literature shows that simulation and modeling have not been sufficiently applied in just-in-time (JIT) manufacturing environments in order to identify the impact of key JIT techniques on system performance. In this study, computer-based simulation tools and linear mathematical models have been applied to identify the impact of selected key JIT variables on system performance in automotive component manufacturing environments. A novel approach is suggested to identify and narrow down possible JIT techniques to a meaningful and manageable set. Experimental results show that parameters such as line balancing, multifunction employee, total quality control, and setup time have a significant impact on relevant outputs such as process time and takt time. The outcome is a robust mathematical model, which identifies key JIT drivers in a typical mixed-model assembly line in a given manufacturing environment.     

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.     

基于内分泌调节原理的制造任务与资源动态协调机制研究

针对制造系统在动态环境下的任务与资源协调优化问题,在保证按时完工的前提下,分析了制造任务与资源协调优化过程,建立了数学模型。受人体内分泌调节机制的启发,设计了两种可以相互影响的激素(任务相关激素和资源相关激素),同时,考虑到突发事件对制造系统工作性能的影响,提出了基于激素的资源与任务的动态协调算法。实例验证表明,该算法切实可行,具有任务分配质量高、动态协调性能好、协调过程通信量小、控制系统鲁棒性较好等优点。     

面向大批量定制的用户需求协同配置思想及其应用

面向大批量定制的设计(DFMC)是大批量定制研究和实施的关键技术之一。现有的DFMC模式的重要特点之一在于产品的设计和制造会受到用户订单的极大限制,从而使得企业的制造属性难以实现优化。为此提出了面向大批量定制的用户需求协同配置思想。其内涵在于用户需求参数与企业的产品设计、制造以及生产经营等参数具有密切关系,而用户需求通常都具有相当的变动范围。因此,通过用户与企业集成以及协调用户需求参数与企业的制造参数,就可以实现各参数的综合优化,从而提高用户满意度和企业效益。建立了用户需求协同配置的多目标优化模型,提出了网络化运行模式及一种实现方案。通过工程应用实例说明了用户需求协同配置的成功应用。     

Modelling the effects of machine breakdowns in the generalized cell formation problem

Machines are key elements in manufacturing systems and their breakdowns can dramatically affect system performance measures. This paper proposes a new multi-objective pure integer linear programming approach for the cell formation problem with alternative process routings and machine reliability consideration. The model minimizes total cost and maximizes system reliability simultaneously. Traditional reliability evaluation approaches attempt to model the reliability of the manufacturing system as a function of its elements. These approaches have some negative aspects; therefore, instead of modeling the system reliability as an explicit objective function, we use an approach to model the effects of the machine unreliability in terms of cost and time-based effects. Using the ?-constraint method as an optimization tool for multi-objective programming, a numerical example is solved to demonstrate the capability of the proposed model in evaluating various effects of the reliability consideration.     

绿色制造的战略环境评价研究及实证分析

绿色制造战略环境评价是贯彻可持续发展战略,从根本上解决环境污染问题,真正实现经济、社会、环境三者协调发展的重要手段.为了客观地对绿色制造的战略环境进行评价,从绿色生产与运营、资源与能源、环境保护三个方面设计了绿色制造战略环境的评价指标体系,在此基础上构建模糊综合评价模型,并根据所建立的评价方法对某电子企业进行实例分析.实例证明该模型的有效性和可靠性,将为企业开展绿色制造提供有效方法和依据.     

Genetic algorithm-based integrated production planning considering manufacturing partners

This paper deals with an integrated production planning problem in the presence of manufacturing partners. In this environment, although most of the products and parts are produced in local plants, they can also be purchased from any desirable manufacturing partners at reasonable costs. The objective of this study is to provide efficient integrated production plans for both local plants with finite production capacity and manufacturing partners, while minimizing the total production costs. For solving this problem, we formulated an integrated production planning problem by modifying a multi-level lot-sizing (MLLS) problem, and proposed an efficient genetic-algorithm-based heuristic for minimizing the costs of production, inventory, and subcontracting with manufacturing partners. The proposed heuristic consists of a unique chromosome structure, a chromosome-generation method, and genetic operators. The experimental analysis shows that the proposed heuristic generates quite good solutions at low computational costs, in comparison with a commercial optimization package.     

A mechanism for the integration of TQM and MRPII

This paper presents a quality management system based on Manufacturing Resource Planning (MRPII). A real-time monitoring system is used to acquire the information necessary for MRPII to drive quality management over this framework. Firstly, the concept of Total Quality Management (TQM) is overlaid on the structure of MRPII. Secondly, quality management activities in manufacturing systems are used to shape the manufacturing database concept and information models of various manufacturing activities. Finally, a complete dynamic system model setting out the architecture of a proactive resource planning environment that encapsulates quality management is derived.