Fuzzy property is one of the most popular properties of mankind thinking and objective things. Fuzzy set theory is an effective tool to deal with fuzzy phenomena. Fuzzy decision is just the result of combination of fuzzy set theory and decision theory. When solving scheduling problems, owing to some random factors, it is more appropriate to consider processing time and due date as fuzzy numbers. This kind of scheduling problems is called fuzzy scheduling ones. This paper studies fuzzy decision and fuzzy scheduling problems, Main works and innovations of this dissertation are as follows. 1. The broad sense Hamming weight distance method for individual multi-objective decision-making is generalized to group one. The definition of relative optimal membership degree used in engineering fuzzy set theory is adopted. The concept of group broad sense Hamming weight distance is introduced. The method of solving multi-objective group decision-making problem is proposed, and an example is presented to illustrate the validity and universality of the given method. 2. The fuzzy scheduling problem is introduced under the basis of single machine problem. Two common kinds of fuzzy number, which are triangular and trapezoid, are introduced. We formulate three kinds of scheduling model according to fuzzy processing time and fuzzy due date. Genetic algorithm is adopted to find the optimal sequencing. Compared with heuristic algorithm, simulation experiment shows the validity of given method. 3. Fuzzy multi-machine scheduling problems, including fuzzy flow shop scheduling problem and fuzzy job shop scheduling problem, are studied. Three kinds of fuzzy flow shop scheduling model and two kinds of fuzzy job shop scheduling model are presented. According to the characteristics of job shop scheduling problem, a new kind of genetic algorithm is given. Researches are made in aspects such as coding, decoding, producing initial population, crossover and mutation, etc. The given method can meet the sequence constraint automatically. In order to show the validity of the algorithm, numerical simulation examples are given. 4. We studies non-linear programming: problem by using improved genetic algorithm and evolutionary programming. Discussions are made in aspects such as coding, crossover and mutation, constraint processing etc. Experimental results show the effectiveness of our proposed algorithm. In addition, we compare the three kinds of typical evolution algorithm that are gene, tic algorithm, evolution strategy and evolutionary programming. We also discuss the mathematical basis of genetic algorithm simply. 5. The expert system based sale management intelligent decision support system (SMIDSS) is researched and developed. The decision support system and expert system are treated equally in the SMII)SS. The SMIDSS can implement the contract's technique feasibility analysis, the optimum due date de
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