满足产品需求条件下的车间最优随机生产计划与控制

根据实际需要建立关联方程有延迟且以正好满足产品需求为约束条件的车间生产计划与控制的随机非线性规划模型，即一种求解动态优化问题的静态优化模型，为求解方便将其转化成线性规划模型。提出分别用卡马卡算法和基于卡马卡算法的关联预测法来求解柔性自动化车间（FAM）最优随机生产计划与控制问题，并编制了相应软件。通过算例研究，比较了上述2种方法和Matlab中的线性规划法，结果表明所提方法非常适合将不确定性环境中的FAW产品需求计划最优分解成由FAW中各柔性制造系统（FMS）执行的短期随机计划，尤其适合FMS之间工件传输需经出入库并有1个生产周期延迟的情况。     

敏捷制造环境中车间的随机生产计划方法

研究了敏感制造环境下柔性自动化车间的髹机生产计划方法。     

柔性自动化车间的最优随机生产计划

研究了由多个柔性制造系统组成的柔性自动化车间的最优随机生产计划问题,首先根据实际需要建立车间生产计划的随机非线性规划模型,为求解方便,将其近似转化成确定非线性规划模型,并通过引进约束进一步转化成线性规划模型。由于这种模型规模较大,很难在微机上用单纯形法在可接受的时间内获得其最优解。为此,分别用卡马卡算法和基于卡马卡算法的关联预测法,求解柔性自动化车间最优生产计划问题,并编制了相应软件。最后通过算例研究,比较了卡马卡算法、基于卡马卡算法的关联预测法和Matlab中的线性规划法,结果表明,所提方法非常适合将不确定性环境中的随机产品需求计划,最优分解成由柔性自动化车间中各柔性制造系统执行的短期随机计划。     

Rule-based production planning in flexible manufacturing systems

Production planning in flexible manufacturing may require the solution of a large-scale discrete-event dynamic stochastic optimization problem, due to the complexity of the system to be optimized, and to the occurrence of discrete events (new orders and hard failures). The production planning problem is here approached for a multistage multipart-type manufacturing shop, where each work cell can share its processing time among the different types of parts. The solution of this problem is obtained by an open-loop-feedback control strategy, updated each time a new event occurs. At each event time, two coupled problems are solved: 1) a product-order scheduling problem, conditioned on estimated values of the production capacities of all component work cells; and 2) a production-capacity planning problem, conditioned on predefined sequences of the product orders to be processed. In particular, the article aims at defining a production planning procedure that integrates both analytical tools, derived from mathematical programming, and knowledge-based rules, coming from experience. The objective is to formulate a hybrid (knowledge-based/analytical) planning architecture, and to analyze its use for multicell multipart-type manufacturing systems.     

分布式多工厂单件制造企业提前/拖期生产计划研究

描述了分布式多工厂单件制造企业交货期下的提前／拖期生产计划,以实现最小化提前／拖期惩罚费用、生产成本以及产品运输费用之和为目标,建立了０－１规划数学模型,利用分枝定界算法得到了生产计划调度方案。计算结果证明了模型的有效性。     

Application of chance-constrained programming for stochastic group shop scheduling problem

In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to find a job schedule which minimizes the total weighted completion time. We solve this problem based on the chance-constrained programming. First, the problem is formulated in a form of stochastic programming and then prepared in a form of deterministic mixed binary integer linear programming such that it can be solved by a linear programming solver. To solve the problem efficiently, we develop an efficient hybrid method. Exploiting a heuristic algorithm in order to satisfy the constraints, an ant colony optimization algorithm is applied to construct high-quality solutions to the problem. The proposed approach is tested on instances where the random variables are normally, uniformly, or exponentially distributed.     

A simultaneous planning of production and scheduling operations in flexible flow shops: case study of tile industry

The aim of this paper is to study a simultaneous lot-sizing and scheduling in multi-product, multi-period flexible flow shop environments. A new mixed integer programming (MIP) model is proposed to formulate the problem. The objective function includes the total cost of production, inventory, and external supply. In this study, in case of not meeting the demand of customers, this demand should be met by foreign suppliers in higher price. Due to the high computational complexity of the studied problem, a rolling horizon heuristic (RHH) and particle swarm optimization algorithm (PSO) are implemented to solve the problem. These algorithms find a feasible and near-optimal from production planning and scheduling. Additionally, Taguchi method is conducted to calibrate the parameters of the PSO algorithm and select the optimal levels of the influential factors. The computational results show that the algorithms are capable of achieving results with good quality in a reasonable time and PSO has better objective values in comparison with RHH. Also, the real case study for tile industry with real features is applied. Sensitivity analysis is used to evaluate the performance of the model.     

Order Release and Product Mix Coordination in a Complex PCB Manufacturing Line with Batch Processors

In this paper, we study the role of order releases and product mix coordination in a complex manufacturing line with batch processors. We develop a planning methodology for synchronizing production in such manufacturing lines and discuss the decision-making process in the context of a PCB production environment at Northern Telecom's Fiberworld Division. The planning methodology includes developing mathematical programming models for determining a configuration of batch processors, order releases to the shop floor, and daily loading decisions at the batch processors. The optimization models are linked to a simulation model of the shop, which provides key statistics like lead time, work in process, and utilization rates. The objective is to reduce lead time for manufacturing different products in this environment while meeting the demand. We analyze the performance of such a line, study the efficacy of various types of shop floor synchronization policies, and establish the role of batch processors in managing such complex lines effectively. We exhibit how batch processors (which are bottleneck operations) could be scheduled effectively to incorporate the logical constraints that govern their operations and react to variabilities in the manufacturing line.     

APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

A concept of an intelligent optimal design approach is proposed, which is organized by a kind of compound knowledge model. The compound knowledge consists of modularized quantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By using this compound knowledge model, the abundant quantity information of mathematical programming and the symbolic knowledge of artificial intelligence can be united together in this model. The intelligent optimal design model based on such a compound knowledge and the automatically generated decomposition principles based on it are also presented. Practically, it is applied to the production planning, process schedule and optimization of production process of a refining & chemical work and a great profit is achieved. Specially, the methods and principles are adaptable not only to continuous process industry, but also to discrete manufacturing one.     

Manufacturing-to-Sale Planning Model for Fuel Oil Production

The fuel oil refinery production industry in Taiwan has entered a completely competitive free open market. In such an environ-ment there exist a variety of uncertain factors, and a traditional production planning model will not be able to deal with the new situation. This study develops a responsive and flexible manufacturing-to-sale planning system to deal with uncertain manufacturing factors. The major objective of this study is to model the problem of the uncertain nature faced by the Chinese Petroleum Corporation (CPC) and to establish a manufacturing-to-sale planning model for solving the problem. A linear programming technique is suggested for developing the optimal strategy for use in production plans. Fuzzy theory is adopted for dealing with demand/cost uncertainties. A possibilistic linear programming model is thus formulated in this study. The possible uncertain fluctuations on demand/cost are included in the model. Therefore, the strategy for creating maximum profit for the company can be obtained via the proposed modelling procedures.     

基于JIT的加工和装配计划集成方法及其应用

针对订单生产型企业JIT准时供货的要求,建立了加工和装配计划集成模型,该模型的目标函数是为了保证产品交货期的要求;为保证计划的可行性,约束函数包括加工能力约束、装配能力约束、加工和装配顺序约束;模型为两层混合规划模型,运用了遗传算法和启发式规则,提出了混合启发式算法。最后,针对某按订单制造型企业进行了实例应用,对产品制造过程进行了分解,采用先由装配计划得出各工件的交货期,然后根据工件的交货期确定工件的加工和装配计划。     

面向状态的车间控制策略研究

当前的生产计划和控制系统较好地解决了企业中期的物料需求计划与能力需求计划问题,而车间以下层次的生产作业计划与控制是其薄弱环节。这里通过对现代车间生产活动控制的功能、目标和现代车间制造系统基本特征的考察、分析,揭示了现代车间控制的根本矛盾,提出了面向状态的车间控制策略和方法并进行了应用研究。     

Hierarchical Production Planning in Flexible Automated Workshops with Delay Interaction

This paper addresses the hierarchical production planning (HPP) problem for flexible automated workshops (FAWs) with delay interaction, each with a number of flexible manufacturing systems (FMSs). The delay interaction aspect arises from taking into consideration the transfer of parts between FMSs. Any job which requires processing on more than one FMS cannot be transferred directly from one FMS to the next. Instead a semi-finished-product completed in one period must be put into shop storage until the next period at which it can be transferred to the next FMS for further processing. The objective is to decompose medium-term plans (assigned to an FAW by ERP/MRP II) into short-term plans (to be executed by FMSs in the FAW) so as to obtain the lowest production cost. The HPP problem is formulated in this paper by a nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the nonlinear programming model, it is transformed into a linear programming model. Because the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar’s algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for medium- or small-scale problems and the latter for large-scale problems. With the implementations of these algorithms and with many HPP examples, Karmarkar’s algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, showing that the proposed approaches are very effective.     

Storage location assignment and order picking optimization in the automotive industry

The objective of this study is to design storage assignment and order picking system using a developed mathematical model and stochastic evolutionary optimization approach in the automotive industry. It is performed in two stages. At the first stage, storage location assignment problem is solved with a class-based storage policy with the aim of minimizing warehouse transmissions by using integer programming. At the second stage, batching and routing problems are considered together to minimize travel cost in warehouse operations. A warehouse in the automotive industry is analyzed, and an optimum solution is obtained from an integer programming model. Due to the computational time required for solving the integer programming problem, a faster genetic algorithm is also developed to form optimal batches and optimal routes for the order picker. The main advantage of the algorithm is the quick response to production orders in real-time applications. The solutions showed that the proposed approach based on genetic algorithms can be applied and integrated to any kind of warehouse layout in automotive industry.     

Scheduling in aerospace composite manufacturing systems: a two-stage hybrid flow shop problem

This research investigates a real-world complex two-stage hybrid flow shop scheduling problem which is faced during the manufacturing of composite aerospace components. There are a number of new constraints to be taken into account in this special hybrid flow shop, in particular limited physical capacity of the intermediate buffer, limited waiting time between processing stages, and limited tools/molds used in both stages in each production cycle. We propose a discrete-time mixed integer linear programming model with an underlying branch and bound algorithm, to solve small- and medium-size problems (up to 100 jobs). To solve the large instances of the problem (up to 300 jobs), a genetic algorithm with a novel crossover operator is developed. A new heuristic method is introduced to generate the initial population of the genetic algorithm. The results show the high level of computational efficiency and accuracy of the proposed genetic algorithm when compared to the optimal solutions obtained from the mathematical model. The results also show that the proposed genetic algorithm outperforms the conventional dispatching rules (i.e., shortest processing time, earliest dues date and longest processing time) when applied to large-size problems. A real case study undertaken at one of the leading aerospace companies in Canada is used to formulate the model, collect data for the parameters of the model, and analyze the results.     

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.     

Flexible job shop scheduling with tabu search algorithms

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.     

A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem

Job shop scheduling is an important decision process in contemporary manufacturing systems. In this paper, we aim at the job shop scheduling problem in which the total weighted tardiness must be minimized. This objective function is relevant for the make-to-order production mode with an emphasis on customer satisfaction. In order to save the computational time, we focus on the set of non-delay schedules and use a genetic algorithm to optimize the set of dispatching rules used for schedule construction. Another advantage of this strategy is that it can be readily applied in a dynamic scheduling environment which must be investigated with simulation. Considering that the rules selected for scheduling previous operations have a direct impact on the optimal rules for scheduling subsequent operations, Bayesian networks are utilized to model the distribution of high-quality solutions in the population and to produce the new generation of individuals. In addition, some selected individuals are further improved by a special local search module based on systematic perturbations to the operation processing times. The superiority of the proposed approach is especially remarkable when the size of the scheduling problem is large.     

一种改进的遗传算法在车间调度中的应用

研究现代生产先进制造车间调度问题 ,对于发挥先进制造车间的高效性和灵活性具有十分重要的意义。生产过程的计划调度问题 ,是实现制造系统运筹技术、管理技术与优化技术的核心。因此 ,优化调度算法的性能直接影响到FMS的生产效率和应变能力。本文在传统的遗传算法的基础上 ,对车间调度算法进行了深入的研究。基于机床负荷平衡的思想 ,提出了面向先进制造车间的平衡遗传算法 ,并与传统的遗传调度算法进行了对比。实验表明 ,该调度方法具有较高的可行性、有效性和稳定性。保证了各个机床的负荷平衡 ,满足了交付期的要求     

Inspection Allocation Planning for a Multiple Quality Characteristic Advanced Manufacturing System

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system. To assure product quality, finite automatic inspection systems should be used. Inspection planning to allocate inspection stations should then be performed to manage the limited inspection resource. Except for finite inspection station classes, in this work, the limited number of inspection stations, of each inspection station class, is considered for solving the inspection allocation problem in a multiple quality characteristic advanced manufacturing system. Since the product variety in batch production or job shop production increases to satisfy the changing requirements of the various customers, the tolerances specified will vary from time to time. This inspection allocation problem is solved using a unit cost model in which the manufacturing capability, inspection capability, and tolerance specified are concurrently considered for a multiple quality characteristic product. The situation of unbalanced tolerance design is also considered. The inspection allocation problem can then be solved according to customer requirements. Since determining the optimal inspection allocation plan seems to be impractical, as the problem size becomes large, two decision criteria (i.e. sequence order of workstation and tolerance interval) are employed separately to develop two different heuristic solution methods in this work. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. The result shows that a feasible inspection allocation plan can be determined efficiently. ID="A1"Correspondance and offprint requests to: Dr Yau-Ren Shiau, Department of Industrial Engineering, Feng-Chia University, 100 Wenhwa Road, Seatwen, PO Box 25–097, Taichung 407, Taiwan