A Decision Investment Model to Design Manufacturing Systems based on a genetic algorithm and Monte-Carlo simulation

Abstract

The flexibility of manufacturing systems is one of the key factors to stay competitive when the companies face increasingly frequent market changes due to the rapid introduction of new products and constantly varying product demand. Flexible manufacturing systems are characterised by high investment costs, and often offer more flexibility that what is really needed. The emergent paradigm of Focused Flexible Manufacturing System concerns hybrid systems composed both of general purpose and dedicated resources. The research proposed in this paper concerns a genetic algorithm to design hybrid manufacturing systems composed of dedicated, general purpose (flexible) and reconfigurable machines. It has been considered a production mix with the customer demand, and the manufacturing operations required. The proposed model determines the machines to acquire among dedicated, flexible and reconfigurable machines and the production allocation over the planning horizon. Moreover, the combination of the genetic algorithm with the Monte Carlo simulation allows to include the market uncertainty in the decision model. A simulation environment has been developed to integrate the genetic algorithm and Monte Carlo approach

The Impact of Manufacturing Flexibility and Multi-Criteria Optimization on the Sustainability of Manufacturing Systems

“…Of course, devoting to optimizing only the FJSSP production type, for example with other researchers [28], is a limitation. Given that, other research focuses on demonstrating the impact of flexible line redesign planning problems [28] and determining the impact of the market uncertainty [29] on the adaptability and feasibility of using flexible and dedicated machines on the occupancy of manufacturing utilization using the Monte Carlo simulation method. The presented research work represents the originality with the economical and sustainable optimization approach.…”

Optimization of Reconfigurable Manufacturing Systems Configuration: A Literature Review

“…Nonlinear problems were mostly solved by approximate or hybrid approaches using GA singly [13,17,50,52] or coupled with other methods, like Monte Carlo [30] and dynamic programming [42]. Linear problems were also solved by approximate methods, but papers mostly tried to validate a new heuristic [43] or metaheuristic method [44,54] by comparing their solution with those obtained by the well-known NSGA-II.…”

Research on music creation and audition based on wireless environment

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