http://dspace.cityu.edu.hk:80/handle/2031/769 Wed, 01 May 2013 15:09:07 GMT 2013-05-01T15:09:07Z http://dspace.cityu.edu.hk:80/handle/2031/6502 Title: Pipeline and vehicle transportation problems in the petroleum industry Authors: Zhen, Feng ( 甄峰) Abstract: In the petroleum industry, petroleum product logistics can be divided into two phases: first logistics, which is mainly provided through pipeline transportation or railway, refers to distribution from refineries to oil depots; and second logistics, which is primarily supported by vehicles, pertains to distribution from oil depots to oil stations. This thesis studies three petroleum product transportation problems faced by transportation practitioners in the petroleum industry: one stems from first logistics and two from second logistics. Oil product transportation costs currently account for a proportion of sales fees in the Chinese petroleum industry that is considerably higher than the average international level. Hence, reducing costs incurred from the transportation of oil products has become a highly important problem for the managers of Chinese oil companies. This thesis aims to provide a reference for oil companies for reducing both first and second logistics expenditures. The investigation of these problems was motivated by actual projects for China National Petroleum Corporation (CNPC). For first logistics, a three-phase optimization model for the transportation of multiple petroleum products using pipelines is described. Through this method, we aim to ensure that all depots are able to satisfy the demand for each petroleum product while minimizing costs. The first phase involves solving a mixed integer programming model to create resource allocation plans. This phase minimizes the number of products transported in each time period. The second phase uses the output from the first phase and integrates it into a quadratic mixed integer programming model to create a scheduling plan, which minimizes pumping costs by selecting the optimal pumping configuration and flow rate. We employ dynamic programming to increase the e±ciency of the algorithm, which enables a commercial linear programming solver to address problem instances of a practical scope. Finally, the third phase post-processes the solution from the second phase to minimize mixture costs using dynamic programming. This research was conducted on behalf of CNPC in mainland China, with findings resulting in annual savings exceeding 1 million Yuan. For second logistics, we discuss a new practical variant of the vehicle routing problem with time windows (VRPTW), which originated from the regional transportation planning for oil products at a China National Petroleum Corporation (CNPC) branch in a northwest province of mainland China. Tanker trucks are scheduled to serve each oil station in multiple periods according to a recurring and dynamic time window setting. Refilling at an oil depot is always required after visiting an oil station, so it is safe to assume that the vehicles are uncapacitated. The problem is formulated into a mixed-integer programming model and shown to be NP-hard. We found that the mixed-integer programming model is only solvable for very small impractical cases using exact methods, e.g., branch and cut, which is employed by the state-of-the-art commercial solver ILOG CPLEX. Moreover, due to the floating time windows imposed on the nodes, traditional local search-based heuristics with node interchange operators are not applicable. Thus, we adapt and propose an iterative time window partitioning heuristic that discretizes time windows into multiple time points with dynamic partition widths. Experiments show good quality solutions can be achieved for problem cases with practical sizes. In times of uncertainty, transportation demand changes seasonally as the consumption of oil products fluctuates depending on season. CNPC owns a limited number of vehicles dedicated to transportation requirements during regular seasons. During peak seasons, they need to outsource some transportation jobs to third party logistics (3PL) providers because the demand for oil products (and correspondingly the transportation demand) at this time is considerably higher. Therefore, the solving of two problems of oil product transportation from oil depots to oil stations during peak seasons are necessary: first, determine which of the transportation requirements of oil stations should be outsourced to 3PL providers; second, devise the scheduling plan that determines which of the oil stations' transportation requirements will be handled by the vehicles of the petroleum company. This thesis integrates the combinatorial auction (CA) and vehicle routing problem with time windows (VRPTW) into a single problem. The problem is formulated into a mixed integer programming model and shown to be NP-hard. We devise a heuristic to separate all the stations into two types (depending on whether it is out-sourced to 3PL companies) according to distance. We then obtain an initial solution by separately solving the CA and VRPTW problems. To improve the initial solution, we design and test multiple heuristic operators to interactively solve the CA and VRPTW. Experiments show that good quality solutions are achieved for problem cases of practical scope. Notes: CityU Call Number: HE199.5.P4 Z45 2011; xi, 109 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 99-109) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6502 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6501 Title: Conditional inference in generalized linear mixed models : model identification and robust estimation Authors: Yu, Dalei ( 喻達磊) Abstract: In this thesis, statistical inference problems in generalized linear mixed models (GLMMs) are considered. In particular, model identification and robust residual maximum likelihood (REML) estimation for the GLMMs are studied in detail. The formulation and estimation for the GLMMs are first reviewed, and the differences between conditional likelihood based and marginal likelihood based methods are then discussed. Simulation results indicate that both methods are promising when the sample size is relatively large. The REML estimation method is effective in reducing the negative bias in the estimation of the variance component parameters when the sample size is small. To address the problem of model selection in the GLMMs, a model identification instrument based on the conditional Akaike information (cAI) is developed. In particular, an asymptotically unbiased estimator of the cAI (denoted as cAICC) is derived as the model selection criterion, which takes the estimation uncertainty in the variance component parameter into consideration. The relationship between bias correction and generalized degree of freedom for GLMMs is also explored. Simulation results show that the estimator performs well. An adjusted model selection criterion (denoted as cAICA), which is based on heuristic arguments, is also proposed as an alternative tool for model identification. Both criteria demonstrate high proportion of correct model identification for GLMMs. Three sets of real data (i.e. epilepsy seizure count data, polio incidence data and US strike data) are used to illustrate the proposed model identification methods. To limit the effect of outliers, a robust version of the REML estimation for Poisson log-linear mixed model is developed. The method not only provides robust estimation for the fixed effect and variance component parameters, but also gives robust prediction of the random effects. Theoretical and numerical aspects of the estimators are examined. Simulation results show that the proposed method is effective in limiting the effect of outliers under different contamination schemes. The epilepsy seizure count data are used to illustrate the method. The robust REML estimation method is then extended to the k-component Poisson mixture model with random effects. The behavior of the estimator is studied, and the formulae for obtaining the asymptotic covariance matrix are derived. Simulation study shows that the performance of the proposed robust REML estimator is comparable with the conventional REML estimator for regular data, and it outperforms in the presence of outliers. The urinary tract infections data are taken to demonstrate the proposed robust estimation method. Following similar lines of derivations, extensions of the developed methodologies are possible for a general class of hierarchical generalized linear models and generalized additive models. These topics are considered as future research directions. Notes: CityU Call Number: QA276 .Y8 2011; x, 135 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 101-110) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6501 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6499 Title: Essays on supply chain contracting under system parameter uncertainties Authors: Wang, Yaoyu ( 王要玉) Abstract: Consider a basic two-echelon supply chain with a manufacturer selling a product to a retailer, who in turn retails it to the ending consumers. A fundamental interaction of the two players (i.e., the manufacturer and the retailer) is the "contract scheme" specifying how the "players" have to compensate each. The three essays of this dissertation comprise an analysis of these conventional contract forms as well as developing new schemes under different channel structures with asymmetric information. In the first essay, we consider three "revenue sharing" variants (hereafter "[RS]") and illustrate their significant performance differences under system-parameter uncertainties. For a product with price-dependent demand, it is well-known that if a dominant manufacturer knows the system parameters deterministically, then the conventional [RS] gives him the "perfect power" of simultaneously coordinating the channel and allocating profit arbitrarily. Unfortunately, [RS]'s power deteriorates as the manufacturer's knowledge of the system parameters becomes increasingly uncertain. This essay shows that this deterioration can be substantially reduced by using slightly modified versions of [RS]; these modifications roughly amount to sharing a retailer's "gross profit" instead of "revenue." In other words, this essay presents simple modifications to the "classical" [RS], leading to contract formats that perform substantially better under system-parameter uncertainty. In the second essay, we use a simple and parsimonious model to investigate the performance of volume discounting schemes (hereafter "[VD]") in a with-effort channel; i.e., one where demand is sensitive to both retail price and sales effort. The problem is analyzed as a manufacturer-leading Stackelberg game. We first present, for the deterministic-system parameter situation, contract-designing procedures under two contract formats; namely, a regular "version" of [VD] (hereafter "[RVD]") and a continuous "version" of [VD] (hereafter "[CVD]"). Our solutions show that [RVD] cannot perfectly coordinate this with-effort channel; moreover, [RVD] often leads to a lower channel efficiency than the simple price-only contract. In contrast, we show that [CVD] leads to perfect channel coordination - a significant result since most contract formats have been shown in the literature to be unable to coordinate a with-effort channel. Next, we consider the more realistic situations in which the manufacturer is uncertain about one of the system parameters -- specifically, either the market size "a" or the effort cost "ƞ". Our results show that, if Manu is uncertain about a, [RVD] becomes useless but the manufacturer can still use [CVD] to benefit himself. When the manufacturer is uncertain about ƞ, [CVD] remains useful (as expected); however, surprisingly, [RVD] can outperform [CVD] when both the mean value and the uncertainty of ƞ are sufficient high. These results underline the necessity of evaluating a contract format under various forms of system-parameter uncertainties--often at the expense of analytical tractability. In the third essay, we study a new and controversial "slotting fee" contract (hereafter "[SF]"); i.e., an upfront fee a manufacturer is required to pay a retailer in order to have his product sold on the retailer's shelves. The question we pose is: given that a Stackelberg-dominant retailer of a newsvendor product has to choose a pricing contract with which she transacts with the supplier, how would the supply-chain stakeholders fare when the retailer implements [SF] instead of another practical pricing contract? We show that, contradicting its negative public image, choosing [SF] can often provide a better outcome for all the stakeholder-groups. That is, the supplier's and the retailer's profits are higher, the production workers are asked to produce more, and the consumers pay a lower retail price. We also propose a new "composite" contract format that incorporates both the slotting-fee and "buyback" features. This composite format performs even better than the basic [SF]. Notes: CityU Call Number: HD38.5 .W3655 2011; xi, 120 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 110-120) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6499 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6498 Title: Application of dynamic programming model in inventory management Authors: Tao, Feng ( 陶峰) Abstract: This thesis aims to apply dynamic programming approach to formulate three main topics related to inventory management under three real world situations and then propose the corresponding optimal inventory control policies by analyzing the objective functions and computational simulations. In Chapter 3, the problem of inventory management in a car rental company is considered. We develop a two-stage dynamic programming model, in which we determine the vehicle transfer policy in the second stage and the optimal fleet size in the first stage. Although the objective function could be neither concave nor quasi-concave, we can find the optimal fleet size and vehicle transfer policy by solving a series of linear programming problems. A sensitivity analysis is conducted and managerial insights are drawn upon. We propose a heuristic solution based on a special case analysis, for the first-stage fleet size problem. A numerical study reveals that our heuristic solution for fleet size determination performs well. However, if the corresponding vehicle transfer policy is not appropriately determined, the overall performance can deteriorate drastically even when the fleet size is optimal. Our research not only sheds light on the optimal vehicle transfer policy and fleet size, but also underscores the importance of optimizing vehicle transfer. Chapter 4 considers the issue of replenish the inventory of seasonable goods, which evolves rather rapidly as time elapses. We propose a periodic-review inventory model for planning changes of inventory of seasonable goods with state-dependent demand and cost parameters. We jointly optimize product change and inventory replenishment to maximize the total expected discounted profits over the planning horizon. First, we consider a single-period model and show that the optimal product change policy is a threshold policy for the initial inventory of goods that are soon to become unseasonable. Second, we demonstrate that the corresponding optimal inventory policy follows a PKD (Purchase-Keep-Dispose) policy if the incumbent product is kept or a base-stock policy if a new seasonable product is released. Third, we propose a heuristic approach for a multi-period model. Finally, we conduct the numerical test and demonstrate the performance of our heuristics. Our research provides insights about managing seasonable goods in a dynamic environment. In Chapter 5, we introduce a dynamic programming model for inventory control policy under VMI system in the first step; and consider routing selection in the second stage. The system examined in this chapter consists of one supplier, one item, one vehicle and multi-locations. Firstly, as homogenous depots are considered, a (s,S) policy is proved to be optimal for the inventory strategy. Secondly, based on the previous outcome, routing selection is absorbed in the model. The two most used policies, namely, decreasing order and increasing order of routing costs, are executed in the computational study with (s,S) strategy and full capacity replenishment strategy, respectively. We found that (s,S) policy is indeed more optimal than the corresponding full capacity policy. On the other hand, in both strategies, the decreasing order visiting is the better choice. We also do the sensitivity analysis in the numerical section to find out the effectiveness of the parameter. Notes: CityU Call Number: TS160 .T36 2011; viii, 94 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 85-92) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6498 2011-01-01T00:00:00Z