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- W2769318607 abstract "A hybrid genetic algorithm is proposed for multi-objective flexible job-shop scheduling problem, where the time, cost and equipment utilization rate are used as objective functions. First, the scheduling model for this problem is set up. Second, the matrix chromosome based on job-scheduling encode is adopted and it makes the decode and the use of belief operator much easier. Third, The objective functions are normalized in order to avoiding the preference to some objective with bigger quantity and AHP is used to transform the multi-objective problem into single objective problem. In order to accelerate convergence, a belief operator and neighborhood-search operator are integrated into the genetic algorithm. Finally, the simulations shows that the proposed method is efficient. Keywords-Multi-objective optimization; flexible job-shop scheduling; hybrid genetic algorithm; belief operator; neighborhood-search operator I. INTRODUCTION Flexible Job Shop Scheduling Problem(1)(FJSP) is a kind of scheduling with multiple choice of machines and processes. Generally, it consists of both the machines-distribution and the working procedure scheduling. The technique for job shop scheduling problems is a key technique for improving the work efficiency, the flexibility and reliability of the production. It is of great realistic significance to study deeply the flexible job shop scheduling problem and to propose efficient algorithm for it, especially for an enterprise in a strong competition environment. Compared with the traditional job shop scheduling problem, FJSP is with fewer constraints and thus search space of the feasible solutions is larger. Thus it is more difficult and an NP hard problem. The existing algorithms for this problem include the heuristic methods, the genetic algorithms (GAs) and the tabu search (1)(2)(3). A hybrid genetic algorithm is proposed for multi-objective flexible job-shop scheduling problem, where the time, cost and equipment utilization rate are used as objective functions. It combines the global search ability of GA with the local search ability of the neighborhood -search operator. The matrix representation is designed based on the working procedure precedence. AHP is adopted to find the weights of every objective. The each objective is normalized in order to avoid the preference of some objective and the original problem is transformed into single objective optimization problems by the linear sum of all objective functions. II. MATHEMATICAL MODEL OF MULTI-OBJECTIVE FJSP" @default.
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- W2769318607 date "2010-01-01" @default.
- W2769318607 modified "2023-09-23" @default.
- W2769318607 title "Hybrid Genetic Algorithm for the Multi-objective Flexible Schedu ling Problem" @default.
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