本文選題：作業(yè)島　切入點：生產調度　出處：《電子科技大學》2014年碩士論文

【摘要】：本課題是基于企業(yè)橫向項目“精密空調制造設施布局及生產管理平臺的研發(fā)”的研究。面向作業(yè)島和流水線共存車間的調度問題是一類重要的調度問題,這種調度是將車間的各個生產裝配區(qū)域作為研究對象,將訂單中要求生產的所有產品合理的分配到各個裝配區(qū)域(剩余產能的分配),以達到某些目標函數(shù)最優(yōu)的目的;同時也要對多個零件加工單元不同零部件協(xié)同排序研究,因為這為裝配區(qū)域上面產品分配提供必要的物料齊套時間約束。所以,本課題的研究主要分為兩個階段的研究:第一個階段是面向多個零部件加工單元協(xié)同排序問題的研究,可以核算物料齊套時間,為下一階段的研究提供重要的約束依據(jù);第二階段,在是否具有物料齊套時間約束的條件下裝配區(qū)域剩余產能分配問題。對于面向多個零部件加工單元協(xié)同排序問題,文章將以最小化N個產品的物料齊套時間加權和作為目標函數(shù)建立連續(xù)型的數(shù)學規(guī)劃模型,為產品物料齊套時間的綜合優(yōu)化和求解提供理論基礎。由于隨著問題規(guī)模的擴大,數(shù)學模型的可行解也會呈現(xiàn)指數(shù)增長,一般的求解方法難以實現(xiàn),鑒于遺傳算法具有很好的全局搜素能力而后期迭代不足,對于初始種群的要求較高,所以通過啟發(fā)式算法迭代出效果相對不錯的初始種群,運用遺傳算法進行迭代。通過實例的驗證,改進的遺傳算法可以很好的解決面向多個零部件單元協(xié)同排序問題的求解。在對面向多個零部件加工單元協(xié)同排序問題的研究的基礎之上,對于裝配區(qū)域剩余產能分配問題進行研究,建立以最小化拖期懲罰費用為目標函數(shù)的數(shù)學規(guī)劃模型。首先,在不考慮物料齊套時間約束下建立數(shù)學模型,然后在此模型的基礎之上,將物料齊套時間約束考慮進調度模型,建立相對完善的模型;其次,對于考慮物料齊套時間約束情況下剩余產能分配模型進行算法設計,也是通過啟發(fā)式算法對遺傳算法的改進進行實現(xiàn);最后,結合相關的實例對模型及其算法進行驗證。最后,本文通過搜集某精密空調制造廠的相關數(shù)據(jù),對本文改進的算法和傳統(tǒng)的算法,從幾個給定的指標進行評估,驗證改進的算法比較優(yōu)越。
[Abstract]:This project is based on the horizontal project of the precision air-conditioning manufacturing facility layout and production management platform research and development.Job-island and pipeline co-existence job-shop scheduling problem is a kind of important scheduling problem, which takes each production and assembly area of the workshop as the research object.In order to achieve the purpose of optimizing some objective functions, all the products required in the order should be reasonably distributed to each assembly area (the distribution of surplus capacity). At the same time, it is also necessary to study the collaborative sorting of different parts in multiple parts processing units.This provides necessary material alignment time constraints for product allocation above the assembly area.Therefore, the research of this topic is divided into two stages: the first stage is the research of collaborative scheduling problem for multiple parts processing units, which can calculate the material alignment time, and provide an important constraint basis for the next stage of research;In the second stage, the problem of distribution of surplus capacity in assembly area is given under the condition of whether or not there is a time constraint on the whole set of materials.In this paper, a continuous mathematical programming model is established based on minimizing the weighted sum of the materials of N products as the objective function to solve the problem of collaborative scheduling for multiple parts processing units.It provides a theoretical basis for the comprehensive optimization and solution of product material alignment time.With the expansion of the scale of the problem, the feasible solution of the mathematical model will increase exponentially, and the general solution method is difficult to be realized. In view of the fact that the genetic algorithm has a good global search ability and the late iteration is insufficient,The requirement of initial population is high, so the heuristic algorithm is used to iterate out the initial population with relatively good effect, and the genetic algorithm is used to iterate the initial population.Through the verification of examples, the improved genetic algorithm can solve the cooperative sorting problem of multiple parts units well.Based on the research of collaborative scheduling problem for multiple parts processing units, the problem of spare capacity allocation in assembly area is studied, and a mathematical programming model is established, which takes minimizing the penalty cost of tardiness as the objective function.First of all, the mathematical model is established without considering the time constraints of the whole set of materials, and then, on the basis of the model, a relatively perfect model is established by considering the time constraints of the whole set of materials into the scheduling model.For the algorithm design of the residual capacity allocation model considering the time constraint of the whole set of materials, the genetic algorithm is improved by heuristic algorithm. Finally, the model and its algorithm are verified with relevant examples.Finally, by collecting the relevant data of a precision air conditioning factory, this paper evaluates the improved algorithm and the traditional algorithm from several given indicators, and verifies that the improved algorithm is superior.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB497;TP18
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本文編號：1720687