A heuristic procedure for loading problems in flexible manufacturing systems

The loading problem in a flexible manufacturing system (FMS) is viewed as selecting a subset of jobs from a job pool and allocating the jobs among machines. In this paper a heuristic solution to the loading problem has been suggested by developing the concept of essentiality ratio for the objective of minimizing the system unbalance and thereby maximizing the throughput. The proposed heuristic is tested on ten problems and the results show that the algorithm developed is very reliable and efficient.     

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.     

Solving the machine-loading problem in a flexible manufacturing system using a combinatorial auction-based approach

The next generation manufacturing system is conceived to be intelligent enough to take decisions and automatically adjust itself to situations such as variations in production demand and machine breakdowns. The manufacturing control system must have the intelligence to ensure real time operational control by interacting with different manufacturing subsystems. One of the prominent methodologies to deal with the problem of distributed manufacturing systems is the auction-based heuristic control strategy in which various entities bid themselves, accept bids and make selection amongst bids. The present paper addresses the flexible manufacturing system machine-loading problem where job selection and operation allocation on machines are to be performed such that there is a minimization of system unbalance and a maximization of throughput. The methodology of winner determination using the combinatorial auction process is employed to solve the flexible manufacturing system machine-loading problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way to enhance the efficiency of allocating the assets. The performance of the proposed approach is tested on 10 sample problems and the results thus obtained are compared with the existing models in the literature.     

The machine loading and tool allocation problem in a flexible manufacturing system

In this paper the machine loading and tool allocation problem of an FMS is discussed, A mathematical model is developed to determine the routings of parts through the machines and to allocate appropriate cutting tools to each machine to achieve minimum overall machining cost. Computational experience with this model is presented under various system and operation parameter values. Computational refinements based on lagrangean relaxation are also discussed.     

Some solution methodologies for loading problems in a flexible manufacturing system

The loading problem in a flexible manufacturing system (FMS) is viewed as selecting a subset of jobs from the job pool and allocating jobs among machines. A two-stage branch and backtrack procedure is developed with the objective of maximizing the assigned workload. Heuristic procedures are also developed with a bicriterion objective of minimizing the workload imbalance and maximizing the throughput for critical resources such as the number of tool slots on machines and the number of working hours in a scheduling period. The case of machine-dependent processing times is also dealt with. An illustrative numerical example accompanies each procedure.     

Joint consideration of grouping and loading problems in a flexible manufacturing system

The combined problem of grouping and loading in a flexible manufacturing system is formulated as a multistage multiobjective optimization model. The min-max approach to multiobjective optimization is used to obtain a compromise solution. The application of the model is illustrated by an example.     

An efficient hybrid evolutionary heuristic using genetic algorithm and simulated annealing algorithm to solve machine loading problem in FMS

In this paper, a machine loading problem in a flexible manufacturing system (FMS) is discussed, with bi-criterion objectives of minimising system imbalance and maximising system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools in order to minimise the system's imbalance while maximising the throughput. An efficient evolutionary algorithm by hybridising the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The influence of genetic operators on the evolutionary search in GASA is studied and reported. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature.     

Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm

We develop a genetic algorithm (GA) to solve the uncapacitated multilevel lotsizing problem in material requirements planning (MRP) systems. The major drawback of existing approaches is undoubtedly their inability to provide costefficient solutions in a reasonable computation time for realistic size problems involving general product structures. By contrast, the proposed GA can easily handle large product structures (more than 500 items) with numerous common parts, a problem type for which standard optimization software memory becomes rapidly insufficient. Based upon several hybrid operators and an original way to build up the initial population, the resultant GA provides in a moderate execution time high cost-effectiveness solutions compared with other techniques, in the extensive tests we performed.     

A scheduling approach for a flexible manufacturing system

This paper discusses the scheduling problem of a particular flexible manufacturing system (FMS). The two main components of the FMS are a CNC turret lathe and a CNC machining centre. In the system a wide range of different jobs has to be processed. Each job consists of one or more processing operations on one or both machines. Important characteristics of the scheduling problem are sequence-dependent change-over times (on the turret lathe) and transfer times (on both machines and between the machines). The change-over times are caused by the need to exchange tools in the turret when a new part is going to be processed. The transfer times reflect the time needed to perform manual transportation and clamping activities between two subsequent processing (machining) operations of a part. In this paper a branch and bound algorithm is described based on an active schedule strategy. Solutions are compared to results obtained by a simple dispatching rule     

A loading and dispatching problem in a random flexible manufacturing system

A scheduling problem in a flexible manufacturing system (FMS) is considered to be a composite of two interdependent tasks: loading and sequencing. Formulations are presented for the loading problem with two objectives:

(i) minimization of the system workload unbalance, and

(ii) minimization of system unbalance and the number of late jobs;

including constraints such as the number of tools slots with duplications, unique job routing, nonsplitting of jobs and machine capacity. For both the objectives, heuristic methods are developed and performance is compared with the exact mixed integer programming solutions. A simulation model is developed for investigating the system performance for the problem of minimizing the system unbalance using heuristic and sequential loading methods in conjunction with four—FIFO, SPT, LPT and MOPR—dispatching rules.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

A flexible costing system for flexible manufacturing systems using activity based costing

Flexible manufacturing systems (FMSs) are designed to integrate the flexibility of job shops and the efficiency of mass production systems. Product costing methods have to adapt to this new technological environment. On one hand, the high production overhead cost of these systems requires a special attention to overhead allocation. On the other hand, the constantly changing setup configuration and production plans require a constant recalculation of overhead allocation and an a priori estimation of the expected production cost. This paper introduces the concept of flexible costing in FMSs, and proposes a method that modifies the overhead allocation based on the results of the production plan and on the simulated performance of the process. This approach is illustrated with some numerical examples.     

Tool design problems in a punch press flexible manufacturing system

In this paper, we study tool design problems encountered in using a punch press Flexible Manufacturing System (FMS) for producing flat sheet-metal parts. We consider the problem of designing the minimum number of tools needed to punch a given set of holes in the parts. Holes described by a single attribute as well as two attributes are considered. We model the tool design problems as graph theoretic problems. Such an approach is believed to be new for the problem studied. We have made the following major contributions: First, we show that the two-attribute tool design problem is equivalent to the minimum clique cover problem on the intersection graph of rectangles, which is a well known NP-complete problem. Second, we develop a fast algorithm to construct a set-covering formulation from the underlying graph model. In addition, we show that our approach has applications beyond the tool design problem (e.g., location problems).     

Solving a fixture configuration design problem using genetic algorithm with learning automata approach

Proper fixture design is crucial to workpiece quality assurance in manufacturing. Incorrect fixture design may lead to workpiece deformation during machining. The fixture configuration design is one of the important aspects of fixture design. This paper deals with fixture layout optimization problem. The objective is to minimize the norm of all the passive contact forces satisfying Coulomb friction constraint, work-piece static equilibrium constraint and contact constraint, for the entire cutting operation. To solve this problem, the paper proposes Genetic Algorithm with Learning Automata (GALA) algorithm, which is a population based interconnected learning automata algorithm incorporating genetic operators. The algorithm enjoys the good characteristics of both GA and LA. It is validated with an example of face milling operation. The optimal layout is found to be in tune with empirical facts. Also, for the further investigation of the algorithm, it has been tested on a different problem sets and a comparative study is carried out.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.     

Loading a flexible manufacturing system

This paper examines the problem of master scheduling for an FMS in Scotland containing six CNC horizontal boring machines. A multiple criteria approach is used to choose the compatible subset of candidate orders for processing by this FMS, subject to resource constraints and potentially conflicting performance objectives. A structured framework for conflict resolution is described and compromise solutions are obtained using standard mathematical programming software.     

Introducing a flexible manufacturing system

Flexible Manufacturing Systems represent a significant investment and their introduction involves a major project for a company. This paper refers to the introduction of FMS in a company. It discusses various management aspects of the project, and its main subject is the simulation modelling of the system. The objectives of the simulation studies are given and the model described. Results are given and commented on, whereby certain capacity limitations were identified. As a result of the studies, weaknesses in the supplier's control software were highlighted and modifications made.     

Regression trees approach for flow-time prediction in wafer manufacturing processes using constraint-based genetic algorithm

Understanding the factors associated with the flow-time of wafer production is crucial for workflow design and analysis in wafer fabrication factories. Owing to wafer fabrication complexity, the traditional human approach to assigning the due-date is imprecise and prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due-date to each customer order becomes a challenge to production planning. The paper proposes a constraint-based genetic algorithm approach to determine the flow-time. The flow-time prediction model was constructed and compared with other approaches. Better computational effectiveness and prediction results from the constraint-based genetic algorithm are demonstrated using experimental data from a wafer-manufacturing factory.     

Heuristic solution to the scheduling problems in flexible manufacturing system

The scheduling problems in flexible manufacturing systems deal with (1) tool allocation (2)parts scheduling (3) pallets scheduling (4) machines scheduling and (5) material handling equipment scheduling. This paper presents an approach to determine an optimal schedule of parts integrating all the above scheduling criteria. The problem is formulated as a hierarchical process and solved through eigenvector analysis of priority ordering. Effectiveness of the heuristic is illustrated with an example.     

Solving electromagnetic problems using a novel symmetric FEM-BEM approach

This paper presents a novel symmetric finite element method-boundary element method (FEM-BEM) formulation for solving unbounded electromagnetic problems. The proposed method offers two very attractive features: 1) it is variational, leading to a symmetric system of equations and 2) the meshes for the computations of FEM and BEM can be nonconformal, leading to decoupled computations of FEM and BEM. The accuracy and efficiency of the method are studied for both electromagnetic radiation and scattering problems.