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The Multi-Objective Integration Optimizing of TPL Services on the Project Basis

Author: WuJie
Tutor: PengQiYuan
School: Southwest Jiaotong University
Course: Management Science and Engineering
Keywords: project management (PM) the third part logistics (TPL) evaluation of service project rough sets (RS) structure communication
CLC: F252
Type: PhD thesis
Year: 2008
Downloads: 1298
Quote: 3
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The phenomenon of trade-off stands out in service project of the Third Part Logistics (TPL). It has become a management trap, which embarrasses the enterprise’s development. Therefore, this paper presents a method about the multi-objective integrated optimization in TPL’s service project. Being the basic trade-off factors, the Time - Quality - Cost (TQC) of logistics service is taken into account. By the reason of the fact that this optimization has the spatiotemporal complex, this paper tries to extend the research from spatiality to temporality regulation. The main works include the following aspects.With the foundation of research about correlation between domestic and overseas studies, this paper explains the general rule of Project Management (PM) as well as the service project in TPL. This paper focuses on combinatorial innovation of them. Under the system analysis, this integrated optimization presents the characteristics of multi-objective, multi-layer, and relative priority. We can see that these three characteristics are the frame of this paper, which are familiar to us in the category of PM. That is to say it is reasonable by using PM method to study logistics service project. Moreover this paper presents the representative variabled of TQC, which are increasion or decreasion among them in logistics system. The multi-objective trade-off phenomenon is expressed. At the same time, this study is combined with PM idea of step-by-step. It searches out the result of multi-objective optimization. Furthermore, it is the fact that upper manage layer’s decision environment is more uncertain than lower manage layer’s. The task of upper layer should be supposed to deduce priority rank of objectives, because of relatively uncertain domain. While, the task of lower layer should be supposed to get the value of multi-objective optimization. On account of hiberarchy, this paper constructes the frame model about solution of spatial complex.According to deeply researches on TPLs, the auther found out that priority or weight of objective is the key to decided resources distribution by the decision maker of strategic layer (multi-project layer). Thus the main task of the upper layer is how to objectively deduce the rank of trade-off objectives based upon the history data. Significance of the attribute is confirmed by the theory of condition entropy. According to this method, this paper improves on the classical Analytic Hierarchy Process (AHP), which has the shortage of subjectivity. Thus it acquires the frame work based on data driven. The rank of priorities is reliability. Accorging to this, the objectives of primary and secondary are made sure. The classic method of benchmark can be applied in operation layer. This paper gives possible optimization value of each factor objective by linear programming. The decision maker deals with logistics service by significancy of factors in order to reach at the project’s requirement. In this method, this paper extends the study about trade-off problem from one layer to more layers. The scope of this study enlarges its views to space.Whereas PM and logistics service management belong to different category, there is few studies combined with both of them at present. The key of PM model application is the transplantation in industries, because the target of application PM is promoting TPLs’ business. In order to achieve this aim, this paper’s study takes dynamic viwe of decision rules’s deduced from evaluation PM. Thereby this method makes use of the technic of Data Mining (DM) based on history logistic service project data. It provides an objectively method to support of decision. This part of study is to constructe the frame work of carrying out PM evaluation. Naturally, this method focuses on the time dimension, which combines with TPLs’ development. Vertically spreading out the study, this paper breaks through the transplantation of PM. Hidden knowledge translates to obvious knowledge as a result of being coded. The problem of temporal complex had been settled down by some extents.The orientation of this paper is to study application theory, which comes from practise and goes to practise. The aim is to improve the efficiency of decision by simple ways. The route of solution is applying Granularity theory, which combines with Rough Set theory to propose multil-layer in the proper order thought. Theoretically the finer the decision attribute value of a decision table is, the lower the information granularity is. This paper provides a method of different decision attributes granularity according to different manage layer’s multi-objective decision rules. From this analysis, evaluation project should be made by different fine decision attribute. Because the indexes group is made up by both the upper and the lower layers objectives’ direction in TPLs, the indexes group must consider both information granularity and acquired data. Thus this method objectively ensures that logistics service project management’s requirement need flat to reduce management layer. Moreover it can deduce communication information between the upper and the lower layer, which is usually neglected at practise. Furthermore this information can be collected from the reduce attributs. The priorities of attributs can be solved by the reduction on PM in TPLs. The decision support will be provided by this reduction method, under the situation of structured communication for coordination between the upper layer and the lower’s.Finally, this paper sums up the whole result of study. Moreover it proposes some creative thinking in both technic implement and application domain. They are the main developing aspects. Furthermore it puts forward prospect for future researching work.

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CLC: > Economic > Economic planning and management > Material economic > Material flow
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