Flexible Manufacturing System Loading Research Articles

A heuristic approach based on time-indexed modelling for scheduling and tool loading in flexible manufacturing systems

In this paper, we simultaneously consider the scheduling and tool loading problems in flexible manufacturing systems. There are various jobs that must be processed on a number of parallel computer numerical control machines. The processing of each job requires a set of machine tools. However, the number of tool copies available in the system is limited due to economic restrictions. The problem, therefore, is to schedule the jobs and the required tools in such a way that the makespan is minimized. We present a time-indexed mathematical model of the problem. A heuristic approach based on the mathematical model is also developed and the computational results are presented. The goal of this study is to develop a new approach for simultaneously scheduling the jobs and loading the tools in flexible manufacturing systems and benefit from the advantages of time-indexed modelling.

Due date and cost-based FMS loading, scheduling and tool management

In this study, we consider flexible manufacturing system loading, scheduling and tool management problems simultaneously. Our aim is to determine relevant tool management decisions, which are machining conditions selection and tool allocation, and to load and schedule parts on non-identical parallel CNC machines. The dual objectives are minimization of the manufacturing cost and total weighted tardiness. The manufacturing cost is comprised of machining and tooling costs (which are affected by machining conditions) and non-machining cost (which is affected by tool replacement decisions). We used both sequential and simultaneous approaches to solve our problem to show the superiority of the simultaneous approach. The proposed heuristics are used in a problem space genetic algorithm in order to generate a series of approximately efficient solutions.

A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model

This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised.

A Review of Different Approaches to the FMS Loading Problem

Loading in flexible manufacturing systems (FMSs) is affected by the characteristics of the FMS under analysis, by the type of plant where the FMS is introduced, and by the production planning hierarchy where the loading module operates. We propose an analysis of the various aspects that influence the problem formulation, identifying the alternatives available in real systems and possible future evolutions. We then provide a survey of different approaches proposed in the literature to tackle the loading problem. Articles are classified according to the type of FMS analyzed, the objective function, and the constraints. Finally, based on our analysis, we suggest some problem issues which need to be addressed, and also directions for future research.

Short-Term Capacity Adjustment with Offline Production for a Flexible Manufacturing System under Abnormal Disturbances

Large production variations caused by abnormal disturbances can significantly reduce the production capacity of a flexible manufacturing system (FMS). To prevent production delays, short-term capacity adjustment strategies can be used to augment the capacity of the FMS, such as working overtime, using alternative tools that are suited for faster processing, and producing parts outside of the FMS. We propose a mixed integer programming (MIP) model to obtain an optimal production plan for a multi-machine FMS. Our model evaluates both the FMS loading decision and the effective use of short-term capacity adjustment strategies to minimize the total part production cost. We develop an iterative procedure to solve the model that uses the Lagrangian relaxation method for finding lower bounds and a Lagrangian heuristic for obtaining feasible solutions. The procedure exploits certain special structures found in the Lagrangian multipliers which enable us to obtain good solutions to reasonably large test problems quickly.

Part dispatching and machine loading in flexible manufacturing systems using central queues

This paper describes a dispatching approach for FMSs where all parts are stored in a central buffer. When a machine becomes available, a part from the buffer, which can be processed by that machine, will be dispatched. If there is more than one candidate part, one part will be selected according to pre-determined loading rules. Results of the simulation experiments, used to compare the performance of the proposed dispatching algorithm with the traditional approach where parts were sent directly to the local machine buffers, which have been pre-selected during the process planning stage, show that the proposed dispatching approach outperforms the traditional one with respect to makespans, average flow time, and average tardiness. Makespans are improved by 22% while average flow time and tardiness are improved by 15% and 30% respectively. Machine failures and loading rules have also been shown to have less impact on the proposed dispatching algorithm than the traditional one

Machine loading in flexible manufacturing systems considering routeing flexibility

The key competitive strength of a manufacturing system lies in its flexibility, which represents the ability to respond effectively to changing circumstances. Flexible manufacturing systems (FMSs) react appropriately to change. The relevance of a particular type of flexibility depends upon the system and problem being considered. In this paper, the machine loading problem in a flexible manufacturing system is addressed. The loading problem is concerned with the allocation of part operations and required tools to the machines, so as to optimise some objective(s) subject to some technological constraints. Several objectives have been considered in the past such as maximisation of the utilisation of resources, minimisation of processing and tooling costs and maximisation of throughput rates. In such procedures, it is quite probable that a rigid loading schedule is obtained and in cases of machine breakdowns and tool failures alternate routeings are often not available. Therefore, in this paper, we develop a mathematical model which considers maximisation of the production routeings available for the parts and hence increases the routeing flexibility. The model is illustrated by examples.

Heuristic solutions for loading in flexible manufacturing systems

Production planning in flexible manufacturing system deals with the efficient organization of the production resources in order to meet a given production schedule. It is a complex problem and typically leads to several hierarchical subproblems that need to be solved sequentially or simultaneously. Loading is one of the planning subproblems that has to be addressed. It involves assigning the necessary operations and tools among the various machines in some optimal fashion to achieve the production of all selected part types. In this paper, we first formulate the loading problem as a 0-1 mixed integer program and then propose heuristic procedures based on Lagrangian relaxation and tabu search to solve the problem. Computational results are presented for all the algorithms and finally, conclusions drawn based on the results are discussed.

A note on the problem of scheduling a flexible manufacturing system for Just-in-time customers

In supplying just-in-time customers, one common industry practice is for the vendor to set up a warehouse near the point of demand. We assume that the plant is a flexible manufacturing system (FMS) where the changeover times between parts of the same family axe. negligible, but not between parts of different families. Thus the FMS must be configured to produce one family of parts at a time. For each part type, we assume that the vendor makes deliveries to the warehouse at fixed intervals. We examined two related issues: the run length for each part family and the number of deliveries to the warehouse in each cycle. We first showed that it is not necessary for the vendor to produce for each delivery, nor is it desirable to complete the entire production lot before delivery. We then investigated the FMS loading problem for two or more part-families. We developed the expressions for the optimal run length and delivery frequency, (1) when loading the part types sequentially and (2) when loading the part types...

Note: On the set-union knapsack problem

The 0-1 knapsack problem can be defined as follows. Given a set of items, select the subset with largest total value, subject to the constraint that the total weight of the items selected does not exceed a fixed capacity. The total value of a set of items is the sum of the individual values and the total weight is the sum of the individual weights. In the Set-Union Knapsack Problem, the items each have a value, but instead of a weight, each corresponds to a set of elements. Each element has a weight. The total value of a set of items is sum of the individual values, but the total weight is the sum of the weights of the elements in the union of the corresponding sets. This problem has applications to database partitioning and to machine loading in flexible manufacturing systems. This dissertation concerns the computational complexity and probabilistic analysis of this problem, as well as algorithms for solving it.

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.

A note on planning station loading in flexible manufacturing systems

SUMMARYIn a 1985 issue of this journal, Kusiak presented a formulation of the single period station loading problem related to planning of Flexible Manufacturing Systems. This note makes several observations about this formulation and the linear programming solution technique used. Possible variations and some structural properties of the fomulation are highlighted.

A hierarchical approach to solving machine grouping and loading problems of flexible manufacturing systems

A flexible manufacturing system (FMS) is an integrated system of computer numerically controlled (CNC) machine tools, each having an automatic tool interchange capability, and all connected by an automated material handling system. One or more computers control most real-time functions. Flexible manufacturing is realized to be an efficient alternative to conventional manufacturing that allows simultaneous machining of small to medium batches of a variety of part types. Parts can flow through the system in unit batch sizes. These systems typically machine five to forty different part types. In managing these systems, technological requirements indicate that several decisions must be made prior to system start-up. With these requirements in mind, previous research has defined a set of production planning problems, providing a conceptual framework to aid an FMS manager in setting up his/her system to enable efficient production. Several approaches have been taken to solve several of these problems and we describe those here. The main focus in this paper is on only two of these planning problems, the machine grouping and loading problems. In brief, the FMS machine grouping problem is to partition the m i machine tools of type i into g i groups to maximize expected production, subject to FMS technological and capacity constraints. Machines in a group are identically tooled and hence can perform the same operations during production. The FMS loading problem is to allocate operations and associated tooling of a selected set of part types among the machine groups, according to some appropriate (system dependent) loading objective, also subject to technological and capacity constraints. This paper ties some previous results together by suggesting a hierarchical approach to solve actual grouping and loading problems. Both problems are first defined at an aggregated level of detail and in the context of a queyeing network model. At this level, much information is suppressed. However, the robustness of the model allows the application of the obtained theoretical results to a lower level in the hierarchy that considers all details of these problems. In addition, results obtained using the aggregate model can be used as input to the detailed models. Here, the grouping and loading problems are formulated in all detail as nonlinear integer programs, using all available and required information. The use of these models to solve realistic machine grouping and loading problems is then described. Finally, future research needs are suggested.