http://dspace.cityu.edu.hk:80/handle/2031/714 2013-06-15T00:39:39Z http://dspace.cityu.edu.hk:80/handle/2031/6985 Title: Supply chain network design under facility disruptions Authors: Peng, Peng (彭鵬) Abstract: Key players in the supply chain, including manufacturers, retailers and distributors, have realized the value of comprehensive network planning in which they make detailed plans for constructing new facilities, expanding distribution networks, partnering with new suppliers, and other important logistics activities. During the design phase, many parameters in supply chain design problems are assumed to be fixed. However, the impact of the design decisions spans over a long horizon, during which many parameters such as costs, demands, and capacities will fluctuate. Therefore, it will be dangerous to neglect data uncertainties, since a little change in data input may lead to solutions which are far from optimal in the long run. Possible fluctuations and the reactive strategies have to be taken into account in order to cope with the uncertain environment. Many techniques have been derived to deal with optimization problems with uncertainties, such as sensitivity analysis, stochastic programming methods, robust optimization and so on. Sensitivity analysis procedures are usually tedious to implement, while stochastic programming methods often lead to objective functions that are hard to evaluate. The theoretical framework of robust optimization has been well developed in recent years. It has received more attention in both academy and industry. This thesis studies a variety of problems on designing supply chain networks which are able to achieve well performance in uncertain environment, with methods derived based on recent development in robust optimization techniques. Disruptions represent a form of uncertainty which usually causes capacity loss, transportation blockage, price inflation and other fluctuations to supply chain networks. In this thesis, we first study a strategic supply chain management problem to design reliable networks that perform as well as possible under normal conditions, while achieving relatively well performance when various forms of disruptions strike. We present a mixed-integer programming model whose objective is to minimize the nominal cost (the cost when no disruptions occur) while reducing the disruption risk by applying the p-robustness criterion (which bounds the cost in disruption scenarios). We demonstrate the tradeoff between the nominal cost and system reliability, showing that substantial improvements in reliability are often possible with minimal increases in cost. We also show that our model produces less conservative solutions than those generated by common robustness measures. We propose a hybrid metaheuristic algorithm that is based on genetic algorithms, local improvement, and the shortest augmenting path method to solve the problem. Numerical tests show that the heuristic greatly outperforms CPLEX in terms of solution speed, while still delivering excellent solution quality. The disadvantage of p-robust approach is that it allows less complete description of the scenario space, since the set of scenarios may grow exponentially large as the problem size increases. On one hand, only small problems can be solved due to limited computational power, which makes this model impractical in real world application, where supply chain networks are usually consisted of hundreds of facilities. On the other hand, conserving computational power by considering only a small portion of the total number of scenarios would harm the accuracy of our results. Traditional stochastic programs which are risk neutral in the sense that they consider optimization of expected system-wide cost, are also difficult to solve, since exact evaluation of the expected value is either impossible or prohibitively expensive. To cope with this computational difficulty, we adopt a Monte Carlo simulation based method called sample average approximation (SAA), to solve a stochastic p-robust logistic network design problem in which we minimize the expected total costs, while enforcing p-robust constraints for each scenario. SAA approximates the expected objective function of the stochastic problem by a sample average estimate derived from random samples. It usually results in MIP counterpart problems and can be then solved by deterministic optimization techniques. SAA not only approximates, but also produces confidence intervals on the problem's optimal objective values, which makes this method more attractive. We propose a method based on SAA to solve the stochastic robust model. Statistical lower and upper bounds are derived as well to evaluate the solution quality. Numerical test results show that high quality solutions can be obtained with reasonable computational efforts. Notes: CityU Call Number: HD38.5 .P465 2011; xvi, 144 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 134-144) 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6984 Title: Convex bounds for dependent risks with applications to robust optimization Authors: Li, Xiaobo (李曉波) Abstract: Consider a portfolio that consists of multiple assets for which the risks are dependent. Robust bounds for the risk of the portfolio given the partial dependency structure of the asset returns have received considerable attention. In this paper, we develop new convex bounds for the case with overlapping multivariate marginal dependencies. We propose an infinite dimensional linear programming based method to find these bounds in Conditional Value-at-Risk version for sum risk function and general multivariate marginal structure. Polynomial complexity results for discrete distribution case are developed for this problem. The results are extended to the approximation on the distribution of sum risk. With the tight bound on conditional value at risk of the joint portfolio, we propose a novel robust portfolio selection model that can deal with overlapping multivariate distributional information. Under some mild assumptions, the optimization problem can be solvable in polynomial time. Some numerical examples are presented. Notes: CityU Call Number: HD61 .L568 2012; viii, 72 leaves 30 cm.; Thesis (M.Phil.)--City University of Hong Kong, 2012.; Includes bibliographical references (leaves 68-72) 2012-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6983 Title: Vehicle and manpower routing problems Authors: Che, Chan Hou (謝振濠) Abstract: This thesis studies a set of vehicle and manpower routing problems for multitrip/ multiperiod planning, which can be widely applied in practice. Planning the routes of vehicles, e.g. trucks or ambulances, is usually applied in product and people transportation, while manpower routing occurs when scheduling field technicians to perform tasks at customer sites. Making an efficient and practical routing plan for vehicles and manpower can greatly affect the effectiveness and service levels of a company. Most existing literature on vehicle routing problems studies single period routing (e.g. a day), where the vehicles usually depart and return to the depot only once within the period, and the objective is to minimize the operational cost. However, this assumption may not satisfy other important business requirements, such as labor law regulation. Even when the planning is performed within a single period, the vehicles may depart and return to the depot multiple times. Therefore, more exible routing approaches that consider these factors (such as multiperiod and multitrip planning) are more likely to be practically applicable. The contributions of this thesis are threefold. First, we developed some effective algorithms to resolve real world routing problems for mulitrip and multperiod planning. Second, we introduce new variants of vehicle and manpower routing problems to the literature. Third, we contributed a set of real-world data to research community. The problems investigated in this thesis are all derived from the real world projects, namely an inspector routing problem in a major retail distributor, a periodic vehicle routing problem in one of the largest restaurant chains in Hong Kong, and a non-emergency ambulance transfer service in public hospitals in Hong Kong. The first problem studied in this thesis is an inspector scheduling problem for a major retail distributor. Most existing literature on variations of the vehicle routing problem assumes that all vehicles are in service within the entire planning horizon. However, this assumption may not be valid in practice for some applications due to working time regulations. We formulate the inspector scheduling problem as a multiperiod vehicle routing problem with profit (mVRPP), where the goal is to determine routes for a set of vehicles that maximizes the amount of reward collected from the visited locations, and the vehicles can only travel during working hours within each period in the planning horizon. Furthermore, the vehicles are only required to return to the depot at the end of last period. We propose an effective memetic algorithm with a giant-tour representation to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy procedure to partition a given giant-tour into individual routes, and prove that the resultant partition is optimal. We evaluate the effectiveness of our memetic algorithm with extensive experiments on a set of modified benchmark instances. The results indicate that our approach generates high-quality solutions that are reasonably close to the upper bounds and significantly better than the solutions obtained using heuristics employed by human schedulers. We next study a periodic vehicle routing problem encountered by a restaurant chain in Hong Kong. To develop customer relationships and increase service efficiency due to familiarity, some companies prefer their service personnel to visit regular customers at approximately the same time each day when service is required. However, this type of service consistency may result in increased operational cost, especially when the customers' demands uctuate significantly day by day. In this paper, we investigate a variant of the periodic vehicle routing problem with time windows that includes a limited visiting quota constraint (PVRPTW-LVQ), which requires that any particular customer may be serviced by at most R different vehicles over the planning horizon. The objective is to first serve all customers with a minimum number of vehicles, and then reduce the total distance traveled. We formulate the problem as a mixed-integer linear program, and also propose a three-stage approach combining several search techniques for the problem. Extensive computational experiments on benchmark instances show that the proposed method outperforms previously published approaches for both the PVRPTW-LVQ and the consistent vehicle routing problem (ConVRP), which is a related problem where R=1. We also empirically examine the effects of varying levels of service consistency and demand uncertainty using our approach, which provides additional insights on the trade-offs between these two factors in terms of operational cost. The third problem we investigated is the non-emergency ambulance routing problem. This problem is derived from the non-emergency ambulance transfer service (NEATS) in Hong Kong public hospitals. The NEATS provides transportation service for disabled and elderly patients between hospitals and residences. The efficiency of the NEATS can greatly affect the operations of the hospital. We model this problem as a multitrip pickup and delivery problem with time windows (MT-PDPTW). We devised a fast heuristic to solve the problem, and performed experiments on various sets of data to evaluate the performance of the algorithm. We then tested the algorithm on real world data, which showed that our proposed algorithm can solve the problem quickly. We also discussed the relationship between service level and capacity in the NEATS system. Notes: CityU Call Number: QA402.6 .C45 2011; xii, 119 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 109-119) 2011-01-01T00:00:00Z 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) 2011-01-01T00:00:00Z