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.     

Integrated optimization of production planning and scheduling for a kind of job-shop

This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.     

An integrated decision support system for FMS production planning and scheduling problems

This paper discusses planning and scheduling problems for efficient use of an FMS and presents an integrated decision support system for dealing with these problems.The decision support system, FMSDS (Flexible Manufacturing Systems Decision Support System), includes several modules: data handling module, part selection module, loading module, load adjusting module, scheduling module and simulation module.This paper suggests the solution methodology of each subproblem. Also, an integrated interface scheme between the subproblems is presented. The interface scheme considers the relationships between the subproblems and generates solutions using hierarchical and looping approaches.FMSDS is made up of six alternative models considering three loading objectives and two production order processing strategies. Performance comparison among the six alternatives is shown using the non-terminating simulation techniques.     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.     

CNC机床中刀具交换问题的启发式算法

刀具交换问题是指在一台CNC机床上加工N个作业时，在优化作业加工次序和刀具装载策略的过程中，使CNC机床的换刀次数最少。为了有效地搜索到刀具交换问题的优化解，本文提出了基于定向搜索的启发式算法的求解方法。通过实验数据的验证，本文提出的算法具有简单、计算速度快以及效率高的特点。     

Heuristic solution approaches for combined-job sequencing and machine loading problem in flexible manufacturing systems

Job sequencing and machine loading are two vital and interrelated production planning problems in flexible manufacturing systems (FMSs). In this research, attempts have been made to address the combined job sequencing and machine loading problem using minimization of system unbalance and maximization of throughput as objective functions, while satisfying the constraints related to available machining time and tool slots. This research describes two heuristics to deal with the problems. Heuristic I uses predetermined fixed job sequencing rules as inputs for operation allocation decision on machines, whereas heuristic II uses genetic algorithm based approach for simultaneously addressing job sequences and operation machine allocation issues. Performance of these heuristics has been tested on problems representing three different FMS scenarios. Heuristic II (Genetic algorithm based) has been found more efficient and outperformed heuristic I in terms of solution quality.     

Knowledge-based workcell controller for production planning in the electronics industry

The paper describes the analysis, design and implementation of a workcell controller using knowledge-based system technology in the electronics industry. The central task of the workcell controller is to bridge the gap between the high level production planning system of the logistics department and short-term planning at the shop floor level. Today the latter is mainly based on heuristic knowledge of a foreman. Due to the number of shop floor components involved and their rapid change the foreman is overloaded which leads to large stocks in the production and reduced flexibility. The production planning will be described as an optimisation problem optimising the degree of refinement of the production plan issued by the high level production planning system. As an analytical solution only based on operation research techniques is not feasible the proposed approach is to partly use the empirical knowledge of the foreman to reduce the solution space. To facilitate the formal specification of such combined approaches the optimisation process is presented using the terminology of production management systems (GRAI). The underlying CIM reference model is an extension of the NBS reference model towards the use of knowledge-based system technology.     

An iterative approach to system setup problems in flexible manufacturing systems

System setup problems in flexible manufacturing systems deal with short-term planning problems such as part type selection, machine grouping, operation assignment, tooling, fixture and pallet allocation, and routing. In this article, we consider three of the subproblems: part type selection, machine grouping, and loading. We suggest a heuristic approach to solve the subproblems consistently with the objective of maximizing the expected production rate. The proposed procedure includes routines to generate all possible machine grouping alternatives for a given set of machines, to obtain optimal target workloads for each grouping alternative, and to allocate operations and tools to machine groups. These routines are executed iteratively until a good solution to the system setup problem is obtained. Computational experience is reported.     

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 greedy heuristic and simulated annealing approach for a bicriteria flowshop scheduling problem with precedence constraints—a practical manufacturing case

This paper considers a flowshop scheduling problem with two criteria, where the primary (dominant) criterion is the minimization of material waste and the secondary criterion is the minimization of the total tardiness time. The decision maker does not authorize trade-offs between the criteria. In view of the nature of this problem, a hierarchical (lexicographical) optimization approach is followed. An effective greedy heuristic is proposed to minimize the material waste and a simulated annealing (SA) algorithm is developed to minimize the total tardiness time, subjective to the constraint computed for the primary criterion. The solution accuracy is compared with the optimal solution obtained by complete enumeration for randomly generated problem sets. From the results, it is observed that the greedy heuristic produces the optimal solution and the SA solution does not differ significantly from the optimal solution.     

应用遗传算法研究FMS刀具需求模型

针对柔性制造系统刀具需求模型及求解中存在的问题,提出以零件加工时间最短为优化目标,以刀具购买资金为约束,基于遗传法求解的新型柔性制造系统(FMS)刀具需求规划模型。通过比较分析,验证了该FMS刀具需求规划模型及其遗传算法求解需求问题的合理性和优越性。     

CAPACITATED LOT SIZING AND SCHEDULING PROBLEMS USING HYBRID GA／TS APPROACHES

The capacitated lot sizing and scheduling probbm that involves in determining the production amounts and release dates for several items over a given planning horizon are given to meet dynamic order demand without incurring backloggings. Thi: problem considering overtime capacity is studied. The mathematical model is presented, and a genelic algorithm (GA) approach is developed to solve the problem. The initial solutions are generated after using heuristic method. Capacity balancing procedure is employed to stipulate the feasibility of the solutions. In addition, a technique based on Tabu search (TS) is inserted into the genetic algorithm to d;al with the scheduled overtime and help the convergence of algorithm. Computational simulation is conducted to test the efficiency of the proposed hybrid approach, which turns out to improve both the solution quality and execution speed.     

基于递阶优化的钢铁企业集团战略排产计划模型研究

针对集团型钢铁企业决策层和执行层递阶分离的特点,提出了基于递阶优化理论的钢铁企业集团战略排产计划模型。模型上层由一个中心决策者组成,以集团单位产出利润最大为决策目标;下层由N个平行的子系统组成。在充分考虑了集团下属各股份公司设备能力和资源配置约束的基础上,以单个股份公司的每道工序成本消耗最小为目标进行优化。该模型解决了集团型钢铁企业生产能力粗平衡和物料需求粗平衡,实现了集团全局的战略优化。引入遗传算法求解模型。最后给出了应用实例,证明了该模型的有效性和可行性。     

不确定需求和能力约束下的多目标多工厂生产计划

针对多个具有供需关系的制造工厂和多个地域分散的客户组成的供需网络,研究了不确定需求、生产能力和运输能力条件下的多周期多目标生产计划方法,决策变量为每个周期上工厂的生产计划及工厂间的运输计划,优化目标为最大化总利润与最大化任意客户的平均客户满意度.利用模糊数描述不确定参数,在建立问题的混合整数规划模型后,将此模糊数学模型转化为最大化满意度的加权和模型,并提出了禁忌搜索与后向启发式方法相融合的B-TS算法.计算实例证明,B-TS算法能够有效获得多目标多工厂生产计划.     

基于蚁群算法的产品拆卸序列规划研究

为了能以较高的效率求解出产品拆卸序列的方案，首先阐述了拆卸可行性信息图的概念，将产品的拆卸序列规划问题转述成对该加权有向图中具备最优值的路径搜索和寻优问题。提出了一种蚁群优化算法，并结合对产品元件的拆卸路径求解工具，以实现对产品拆卸可行性信息图的构建和对拆卸方案的搜索和寻优。蚂蚁的一条遍历路径代表了一个描述产品元件拆卸的方案；蚂蚁已经遍历过的路径上代表可行操作的节点数决定了其留下的信息素。启发式信息的求解分为两个部分，包括了确定启发式向量和求出启发式信息值，它们分别表征了方案的可行性及其优异程度。最后，通过一个实例，验证了这一方法的可行性及其计算效率。     

多级车间生产计划和调度的集成优化

针对一类多级制造系统建立了生产计划和调度的集成优化模型。该多级制造系统为三种类型车间的一个串联,主要有作业车间、并行生产车间和流水车间,且终端产品和上游车间制造的零部件之间为装配关系,因此生产关系十分复杂。在分析批量生产特点的基础上给出了一个批量动态分割合并算法,并构造了一个基于遗传进化的启发式算法对该模型进行求解。与递阶分解方法的比较试验表明所提出的集成优化方法是有效的。     

A modular simulator for design,planning, and control of flexible manufacturing systems

Flexible manufacturing systems are designed to produce a variety of different part types with high machine utilisation, short lead times and little work-in-progress inventory. Simulation is an efficient tool to verify design concepts, to select machinery, to evaluate alternative configurations and to test system control strategies of an FMS. This paper discusses a general-purpose, user-oriented discrete simulator (the Modular FMS Simulator) which can be used for design as well as for operation and scheduling of FMSs. The package can be implemented in different hierarchical steps of FMS production planning. It is also a useful tool in validating the results of analytical models or heuristic procedures developed for FMS problems. This package contains features for the system hardware and the control hierarchy. The model can study multiple part families, various station types, different number of work-in-process buffers and carts and almost any system layout. It is also possible to analyse the performance of the system. The package contains a set of decision rules from which the user can make his choice.     

Solving reverse logistics vehicle routing problems with time windows

A vehicle routing problem with simultaneous pick-up and delivery in closed-loop logistics network optimization is studied in this paper. Since, in practice, material pick-up and delivery are only allowed to take place on certain time periods, we consider the reverse logistics vehicle routing problem with time windows. A mixed integer programming model is proposed to formulate the considered problem. A heuristic solution approach for solving the model is developed due to the NP-hard nature of solving the model. The heuristic solution is then used as an initial solution of a simulated annealing procedure for improved solutions. The proposed heuristic method and the simulated annealing procedure yield very promising solutions in much less computational time when compared with optimal solutions generated by exact solution procedures. Numerical examples are presented to illustrate the developed model and solution methods.     

A production planning in highly automated manufacturing system considering multiple process plans with different energy requirements

This paper focuses on a production planning problem in a highly automated manufacturing system considering multiple process plans with different energy requirements. The system consists of several closely interconnected sub-systems such as the processing system, the material (part) handling system, the tool transport system and the auxiliary system responsible for a supply of cooling/lubricants and a waste disposal. We propose a methodology for an estimation of energy consumption and material flows that are incurred at a system level with respect to multiple process plans for a part type. In addition, this study focuses on a production planning problem with the objective to minimize the weighted sum of energy consumption, inventory holding cost and backorder cost on a FMS considering multiple process plans. The production planning model is developed as a linear programming model. The benefit coming from the adoption of suggested model has been addressed with reference to a real industrial use case study.