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Simulation Optimization for3PL-MRCD System under Capacity Expansion of Auto Manufacturer

Author: ShiWen
Tutor: LiuZhiXue
School: Huazhong University of Science and Technology
Course: Management Science and Engineering
Keywords: Capacity Expansion 3PL-MRCD System Auto Manufacturer Multiple Sequential Bifurcation Metamodel Simulation Optimization Robust (Simulation) Optimization
CLC: N945.13
Type: PhD thesis
Year: 2013
Downloads: 97
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On the basis of field observations and literature review, this dissertation deeply examines the best operating settings of Chinese third-party logistics under the expansion of their serving manufacturer. In the proposed framework, a random (stochastic) dynamic model that agrees with the3PL’s practical operations more; i.e., milk-run pickup and cross-docking distribution (3PL-MRCD for short) is constructed, and then changes of the3PL-MRCD performances (cycle time, CT and number of throughput, NT) due to the addition of the assembly plant capacity are explored. Two types of metamodel-based optimization routines simultaneously---simulation optimization methods and robust (simulation) methods---to identify factor levels that would maximize system potential are employed. More specifically, the main contributions of the dissertation are as follows.To understand the changes of the3PL-MRCD system after the expansion auto maker, a process-centric modeling paradigm that ranges from the start milk-run pickup, middle cross-docking distribution, to the end factory warehouse is contructed using Arena software. Next, by conducting sensitivity analysis, the changes of system performances are observed, and logistics modes; namely, truck scheduling, truck dispatching rule, door assignment and truck type selection to improve system performance of interest are provided.To optimize the3PL-MRCD system, which involves a large number affecting factors, a hybnrid optimization framework that first searches for a few important factors dominating the system performance, and next identify the best settings of them is innovatively presented. Since the3PL-MRCD system contains two types of response; i.e., CT and NT, a novel method for factor screening in random discrete-event simulation with multiple response types, called multiple sequential bifurcation (MSB) is introduced. This MSB extends basic sequential bifurcation (SB) to incorporate multiple responses. The performance of MSB is proven and compared with the original SB procedure in two Monte Carlo experiments. Finally, the MSB is successful in eliminating the unimportant ones among numerous factors within the3PL-MRCD system, which further exhibits its robustness.To determine the optimal levels of these key factors found by MSB, two types of metamodel-based approaches; namely, response surface methodlogy (RSM) and Kriging are adopt. These metamodels are capable of describing the relationship between key factors and multiple system responses of interest, and ultimately enable optimization. After that, simulation outputs from four input combinations, which represent four decisions the3PL may make are compared objectively, and the most desired way to improve system performance after the expansion of the assembly plants’capacity is then provided. In addition, the fitness between MSB-RSM and MSB-Kriging methods, and also the efficiency and efficacy between classical RSM and our MSB-RSM are compared.To discern the robust conditions for the key factors found by MSB, a robust procedure that combines the Latin hypercube sampling (LHS) with RSM and Kriging methods is presented, and an environmental factor is taken into account. LHS can account for the distribution of the environmental factor, and form a cross (combined) design with the decision (key) factors. After that, this design to build LHS-RSM and LHS-Kriging metamodels is exploited, respectively, and find the robust operation conditions for decision factors that minimize the variability of two responses while ensure their mean values stay with a desired targets. Finally, a distribution-free bootstrapping procedure to further compare the results obtained by the simulation optimization and the robust optimization methods are presented.

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CLC: > SCIENCE AND > Journal of Systems Science > Systems Engineering > Systems Analysis > System Simulation
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