Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm (original) (raw)
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Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2006
Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, i.e. part type selection and operation allocation on machines. The combination of these problems is termed the machine-loading problem, which is a well-known complex puzzle and treated as a strongly NP-hard problem. In this research, a machine-loading problem has been modelled, taking into consideration several technological constraints related to the flexibility of machines, availability of machining time, tool slots, etc., while aiming to satisfy the objectives of minimizing the system unbalance, maximizing throughput, and achieving very good overall FMS utilization. The solution of such problems, even for moderate numbers of part types and machines, is marked by excessive computation complexities and therefore advanced random search and optimization techniques are needed to resolve them. In this paper, a new kind of genetic algorithm, termed a genetic algorithm with chromosome di...
ECTI Transactions on Computer and Information Technology (ECTI-CIT)
This paper addresses two NP-hard and strongly related problems in production planning of flexible manufacturing system (FMS), part type selection problem and machine loading problem. Various flexibilities such as alternative machines, tools, and production plans are considered. Real coded genetic algorithms (RCGA) that uses an array of real numbers as chromosome representation is developed to handle these flexibilities. Hybridizing with variable neighbourhood search (VNS) is performed to improve the power of the RCGA exploring and exploiting the large search space of the problems. The effectiveness of this hybrid genetic algorithm (HGA) is tested using several test bed problems. The HGA improves the FMS effectiveness by considering two objectives, maximizing system throughput and minimizing system unbalance. The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum soluti...
MACHINE LOADING IN FLEXIBLE MANUFACTURING SYSTEM
The paper addresses a vital pre-release decision that directly affects the operational effectiveness of a flexible manufacturing system- the machine-loading problem. Flexible manufacturing is a concept that allows manufacturing systems to be built under high customized production requirements. Issues such as cutting down of inventories and shortened product life cycles, reducing the cost of products and services to grab more market shares, etc have made it almost compulsory for many companies to switch over to flexible manufacturing systems (FMSs) as a viable means to accomplish the above goals while producing consistently good quality and cost effective products. The combinatorial and NP-hard nature of this problem makes it arduous to secure the best solutions. The objective is minimization of the system unbalance whereas the system's technological constraints are determined by the availability of machining time and tool slots. Due to the large number of random sequences generated as the number of jobs increase, an eliminator function displays and computes the system unbalance only for a fixed number of sequences, thus improving the quality of the solution and reducing the computational burden. The proposed algorithm is test
The International Journal of Advanced Manufacturing Technology, 2006
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.
2012
In this research paper, application of genetic algorithms (GA) to flexible manufacturing systems (FMS) tooling system reliability in the context of the machineloading and part assignment problem is investigated. As manufacturing systems become increasingly complex, competition and cost grow more rapidly. Flexible manufacturing systems became the means to narrow the gap between the various different pressures. FMS promises more efficient and effective ways of utilizing resources, information and assets, due to its capability to carry a variety of different tools so that FMS can perform different operations required in the production of a variety of low to mid size part types. 0/1 integer-programming model is developed. The formulation considers an objective function with a set of governing constraints. Initially a reliability level is decided for the tooling system. The model will simultaneously return with optimum number of tools and tool copies for each tool type as well as the ass...
2012
The Flexible Manufacturing Systems (FMS) belong to class of productive systems in which the main characteristic is the simultaneous execution of several processes and sharing a finite set of resource. Nowadays, the FMS must attend the demand of the market needs for personalized products. Consequently the product life cycle tends to be shorter and a greater variety of products must be produced in a simultaneous manner. In this paper, we present a Genetic Algorithm based scheduling of Flexible manufacturing system. This work is considering multiple objectives, i.e., minimizing the idle time of the machine and minimizing the total penalty cost for not meeting the deadline concurrently. Software is developed for getting optimum sequence of operation. FMS considered in this work has 16 CNC Machine tools for processing 43 varieties of products. In this paper, various meta-heuristic methods are used for solving same scheduling problems taken from the literature. The results available for t...
A genetic algorithm for scheduling flexible manufacturing systems
The International Journal of Advanced Manufacturing Technology, 1998
General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules "pdrs" (independent pdrs one each for one Work Cell "WC"), to resolve the conflict among the contending jobs in the Giffler and Thompson "GT" procedure. The performance is compared with regard to makespan criteria and computational time. The optimal WCwise-pdr is proved to be eJficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in realtime are illustrated with examples. The scope of the genetic search process and future research directions are discussed.
A multiobjective genetic algorithm for scheduling a flexible manufacturing system
The International Journal of Advanced Manufacturing Technology, 2003
Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearerto-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.