Production engineering-oriented virtual factory: a planning cell-based approach to manufacturing systems design

In the traditional process of designing a manufacturing system, a sequential approach treats each of the design steps individually, without considering the requirements of concurrent design activities. The lack of systematic and concurrent consideration of the interactive impact of design decisions leads to repeated and excessive changes in the design and process. To resolve this problem, this research develops a production engineering-oriented virtual factory — a planning cell-based manufacturing systems design approach. The manufacturing systems design process based on a planning cell is reengineered according to the concept of concurrent engineering. The process modeling of a production engineering-oriented virtual factory is proposed at generic and particular levels. An illustrative example of an engine assembly plant demonstrates the effectiveness of the new approach.     

Production engineering-oriented virtual factory: a planning cell-based approach to manufacturing systems design

In the traditional process of designing a manufacturing system, a sequential approach treats each of the design steps individually, without considering the requirements of concurrent design activities. The lack of systematic and concurrent consideration of the interactive impact of design decisions leads to repeated and excessive changes in the design and process. To resolve this problem, this research develops a production engineering-oriented virtual factory – a planning cell-based manufacturing systems design approach. The manufacturing systems design process based on a planning cell is reengineered according to the concept of concurrent engineering. The process modeling of a production engineering-oriented virtual factory is proposed at generic and particular levels. An illustrative example of an engine assembly plant demonstrates the effectiveness of the new approach.     

Production planning of FMS under tool magazine constraints: A dynamic programming approach

Production planning is one of the most important activities for efficient operation of a flexible manufacturing system. This complex acivity is concerned with the decisions related to system set-up, involving solving the problems of selection of a set of part types for simultaneous processing, determining the production ratios, assigning the pallets and fixtures, and assigning operations and tools to machines. In this paper, a dynamic programming algorithm is developed to solve the above problems simultaneously by considering the flexibilities and constraints of the system in order to minimise the unbalanced workload of machines. In this context, various factors such as the tools required for operations, alternative routes available for operations, tool magazine capacity and the limited number of pallets and fixtures are considered. The method is validated with a case study.Notation B a large number - i part type,i=1,...,N - o operation,o=1,...,O _i - k machine,k=1,...,K - j stage,j=1,...,N (N is maximum number of stages required, i.e. equal to the number of part types) - n indicates the number of parts to be introduced into the system (represents the state in DP),n=j,...,nmax. In stagej at least one number of each part type should be introduced into the system. The maximum number of parts can be a user defined maximum,nmax. - P _iok processing time of part typei, operationo on machinek - OA _io set of operation alternatives available for part typei, operation - o an alternative consists of machine number, tool number along with its processing time - pw _ik average workload required by a part typei on machinek - a _i number of parts (ratio) of typei - f _i number of fixtures available to part typei - S(j) {S ₁(j),S ₂(j),...,S _p (j),...}, whereS _p (j) ispth set of selected part types with cardinalityj - |S(j)| ^N C _j - W _k a constant, indicating the average unbalanced workload on a machinek and is a user defined value - L _j,k (S _p (j),n,a _i ) load of machinek at stagej when selection isS _p (j), number of part types isn, and number of parts of type isa _i - L* _j,k (S _p (j),n) load of machinek in stagej at minimum unbalanced workload when selection isS _p (j) and number of parts in system isn - F _j (S _p )(j),n,a _i ) unbalanced workload of all machines at stagej, when selection isS _p (j), number of part types isn, and number of parts of type isa _i - F _j (S _p (j),n) minimum unbalanced workload of all machines, whenS _p (j) is the set of selected part types andn is the number of parts in the system     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

Production planning of a flexible manufacturing system in a material requirements planning environment

Early flexible manufacturing system (FMS) production planning models exhibited a variety of planning objectives; typically, these objectives were independent of the overall production environment. More recently, some researchers have proposed hierarchical production planning and scheduling models for FMS. In this article, we examine production planning of FMS in a material requirements planning (MRP) environment. We propose a hierarchical structure that integrates FMS production planning into a closed-loop MRP system. This structure gives rise to the FMS/MRP rough-cut capacity planning (FMRCP) problem, the FMS/MRP grouping and loading (FMGL) problem, and the FMS/MRP detailed scheduling problem.We examine the FMRCP and FMGL problems in detail and present mathematical programming models for each of these problems. In particular, the FMRCP problem is modeled as a generalized assignment problem (GAP), and a GAP-based heuristic procedure is defined for the problem. We define a two-phase heuristic for the FMGL problem and present computational experience with both heuristics. The FMRCP heuristic is shown to solve problems that exhibit a dependent-demand relation within the FMS and with FMS capacity utilization as high as 99 percent. The FMGL heuristic requires very little CPU time and obtains solutions to the test problems that are on average within 1.5 percent of a theoretical lower bound.This FMS/MRP production planning framework, together with the resulting models, constitutes an important step in the integration of FMS technology with MRP production planning. The hierarchical planning mechanism directly provides for system-level MRP planning priorities to induce appropriate production planning and control objectives on the FMS while simultaneously allowing for necessary feedback from the FMS. Moreover, by demonstrating the tractability of the FMRCP and FMGL problems, this research establishes the necessary groundwork upon which to explore systemwide issues pertaining to the coordination of the hierarchical structure.     

Assembly planning using a novel immune approach

Inspired by the vertebrate immune system, artificial immune system (AIS) has emerged as a new branch of computational intelligence. This paper explores the application of AIS in the problem of assembly planning and proposes a novel approach, called the immune optimization approach (IOA), to generate the optimal assembly plan. Based on the bionic principles of AIS, IOA introduces manifold immune operations including immune selection, clonal selection, inoculation and immune metabolism to derive the optimal assembly sequence. Maintenance of population diversity, attention to the local as well as the global search, and employment of heuristic knowledge to direct the search of optimized assembly sequences are the major concerns of IOA. The details of IOA are presented and the immune operations are discussed. Two practical products are taken as examples to illustrate the validity of IOA in assembly planning, and encouraging solutions in quality and efficiency are achieved. Comparisons with genetic algorithm demonstrate that IOA finds the optimal assembly solution or near-optimal ones more reliably and more efficiently, indicating that IOA has potential and advantages in dealing with assembly planning.     

An integrated planning approach for a nanodeposition manufacturing process

This paper deals with the planning problem in a nanodeposition manufacturing process, in which a toolbit that consists of a multilayer grid of micro/nanofluidic channels is used to deposit nanoscale liquid materials to desired positions on workparts to form solid patterns. The objective is to obtain a planning procedure that achieves efficient throughput for the studied nanodeposition manufacturing systems. We break down the studied problem into several sub-problems as design pattern decomposition, nanopore assignment, liquid material routing in the multilayer grid fluidic network, and toolbit path planning. Efficient algorithms are proposed to solve these sub-problems individually, and then finally integrated into a framework that systematically plans the nanodeposition manufacturing process. A software tool that plans, simulates, and controls the nanodeposition manufacturing process by implementing the proposed algorithms is reported in this paper.     

Hybrid approach to distribution planning reflecting a stochastic supply chain

Today’s business environment is experiencing as a period of expansion and the globalization. Therefore, a distribution plan with low cost and high customer satisfaction in supply chain management (SCM) has been widely investigated. The purpose of this study is to establish optimal distribution planning in the supply chain. In this paper, a hybrid approach involving a genetic algorithm (GA) and simulation is presented to solve this problem. The GA is employed in order to quickly generate feasible distribution sequences. Considering uncertain factors such as queuing, breakdowns and repairing time in the supply chain, the simulation is used to minimize completion time for the distribution plan. The computational results for an example of a simple supply chain are given and discussed to validate the proposed approach. We obtained a more realistic distribution plan with optimal completion time by performing the iterative hybrid GA simulation procedure which reflects the stochastic nature of supply chains.     

产品网络计划及其算法的研究

研究了产品网络计划及其特点,以实际应用为背景,提出了从工艺设计到产品网络计划的转换算法,通过分解工艺信息和物料信息,计算提前期和关键路径,完成从工艺设计到生产管理的产品网络计划的转换。另外,在产品网络计划的基础上提出了一个工时能力平衡的模型并设计了一个近似求解算法。设计实现的系统弥补了计算机辅助工艺规划无法和生产直接关联、生产计划管理系统又无法自动生成计划的缺陷,为工艺设计和生产计划建立起通信的桥梁,在实际生产中有着广泛的应用前景。     

Determining dimensions for process planning: A backward derivation approach

Computer-aided process planning has shown its great importance in computer integrated manufacturing systems. Dimensioning and tolerancing plays a key role in process planning since the final workpiece must ensure conformance with the dimensions in its blueprint. This paper proposes a simple, efficient approach to derive working dimensions for process planning, based on the tolerance chart technique. The paper first reviews briefly some approaches previously presented, such as the mathematical linear equation approach and the graph tracing approach, it then proposes the backward derivation approach. By setting up the origin of a coordinate system at the left-most surface in a blueprint (B/P), the approach first determines the distances from all the B/P surfaces to the origin by use of the B/P dimensions. Then by updating these dimensions, the final working dimensions are calculated backwards by a simple arithmetical equation.     

A hybrid approach for set-up planning

In this paper, various constraints on set-up planning are identified and discussed. Rules and heuristics are created based on these constraints to determine the bilateral precedence relationships between the machining process of features and tool approach directions. By looking at the problem caused by the conflict between the feature precedence relationships and set-up precedence relationships, an optimisation approach is developed to find the optimal plan(s) from all the feasible set-up candidates. The criterion used is the minimum number of set-ups. As a result, alternative set-up plans are generated with a certain number of set-ups required. This research is limited to machining prismatic components only.     

Integer programming approach to process planning

In this paper an integer programming approach to the process planning problem is presented. Although the presented approach can be applied to any type of manufacturing system, some implications of the flexible manufacturing environment on process planning models are discussed. The relationship between the process planning models and the FMS software environment is shown. Algorithms for solving the developed models as well as computational results are discussed.     

基于DEA的加工单元绩效改进

加工单元是制造企业的最小生产单位,其绩效评价涉及许多指标,包括质量指标、时间指标和财务指标等.如何将不同指标进行综合评价是企业生产管理者所关注的问题.现对DEA的不同模型进行了研究,并进一步通过C2R的效率排序模型对水平比较和连续改进方法进行了探讨.通过一个实际的对20个加工单元进行相对绩效分析的案例,讨论了加工单元绩效评价和改进的具体运用方法.     

Object-oriented and fuzzy-set-based approach for set-up planning

Set-up planning is not only a key element of computer-aided process planning (CAPP), but also a key factor for the integration of CAPP with computer-aided fixture planning (CAFP) and production planning and scheduling (PPS). In this work, an object-oriented and fuzzy-set-based approach is developed for set-up planning. Object-oriented technology is employed to represent set-up planning knowledge and generate alternative set-ups which can respond to the dynamic manufacturing resource and facilitate the CAPP/PPS integration. Based on fuzzy-set theory, a fuzzy evaluation function is proposed to produce optimal set-ups. Combined with a real project, a set-up planning system is developed to generate alternative and optimal set-up plans for the machining of prismatic parts.     

A model-based approach to assembly sequence planning

This paper presents a systematic approach for automatic assembly sequence planning (ASP) by using an integrated framework of assembly relational model (ARM) and assembly process model (APM), which are established by object-oriented method. ARM, consisting of assembly, components and liaisons, is the static model of assembly to describe the geometric relationships between components in terms of contact, constraint and interference matrixes. APM, containing states and sub-processes, is the dynamic model of assembly to represent all potential alternatives in assembly process. An algorithmic procedure is also presented by which APM of an assembly can be constructed systematically by analyzing states from lower levels to higher levels. Through the procedure, all feasible sequences of a product can be generated, and the optimum one can be determined by further evaluating each sequence. To explain the approach more clearly, a practical assembly with five components is used as an illustration in the paper.     

An assembly sequence planning approach with a discrete particle swarm optimization algorithm

In this paper, a discrete particle swarm optimization (DPSO) algorithm is proposed to solve the assembly sequence planning (ASP) problem. To make the DPSO algorithm effective for solving ASP, some key technologies including a special coding method of the position and velocity of particles and corresponding operators for updating the position and velocity of particles are proposed and defined. The evolution performance of the DPSO algorithm with different setting of control parameters is investigated, and the performance of the proposed DPSO algorithm to solve ASP is verified through a case study.