W. T. Tsui

The University of Hong Kong, Hong Kong, Hong Kong

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Publications (13)19.78 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a joint optimization of the supply chain network in which supplier selection, lateral transshipment, and vehicle routing are involved. Separate consideration of these decisions involved probably offers only poor-quality local optimal solutions. The contribution of this paper is to study the cost minimization of the supply chain network involving the three decisions simultaneously, using both vertical and preventive lateral transshipment, and considering both single objective and multi-objective approach with the following objectives: a minimize the total ordering cost incurred by the wholesaler, b maximize the amount of savings on the different products, and c find the best sequence for delivering various kinds of products to different retailers. A stochastic search technique called fuzzy logic guided genetic algorithms FLGA is proposed to solve the problems. In order to demonstrate the effectiveness of the FLGA, several search methods are compared with the FLGA through simulations in the single objective approach. In the multi-objective approach, two multi-objective evolutionary algorithms entitled Nondominated Sorting Genetic Algorithms 2 NSGA2 and Strength Pareto Evolutionary Algorithm 2 SPEA2 are adopted for comparison with the FLGA. Results show that the FLGA outperforms others in all three considered scenarios for both single objective and multi-objective approaches.
    Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology. 01/2014; 26(1):173-192.
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    ABSTRACT: This paper deals with the optimization of vehicle routing problem in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time window constraint, the objective regarded in this paper comprises not only the cost due to the total traveling distance, but also the cost due to the total traveling time. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FLGA, a number of benchmark problems are used to examine its search performance. Also, several search methods, branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted to compare with FLGA in randomly generated data sets. Simulation results show that FLGA outperforms other search methods in all of three various scenarios.
    IEEE Transactions on Automation Science and Engineering 05/2010; · 1.67 Impact Factor
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    ABSTRACT: Today, banking firms are heavily affected by the development of electronic commerce (e-commerce) and the internet has had a profound impact on the distribution of non-physical services. Nevertheless, it is believed that banking firms can take advantage of this opportunity to reduce their operating costs and increase revenues. Because of the dramatic increase of competition in this business sector caused by the emergence of internet banking firms, investment consultancy firms, etc. the non-physical nature of the bank's products mean that the whole e-commerce process can be speeded up. The growing popularity of e-commerce and the rapid advances in the development of technology for communications has had a major impact on the traditional procurement process. This article presents different points of view on how a company can move from a paper-based to a digital-based process, how the e-procurement system compares with the traditional methods of operation and how it can add value to the whole organisation. Using the concept and design of the business model created for this article, the main principles and process for achieving these aims will be discussed, covering all aspects of the treatment of information, from data collection to information interpretation and the implementation of the procurement system.
    IJSTM. 01/2010; 14:26-40.
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    ABSTRACT: This paper presents a joint optimization of supply chain network in which supplier selection, lateral transshipment, and vehicle routing are involved. The objective regarded in this problem is to: (a) select one or more suppliers to order and replenish different types of products in order to minimize the total ordering cost spent by a wholesaler, (b) maximize the sum of maximum savings of different products, and (c) find the best sequence of delivering various kinds of products to different retailers in order to minimize the total cost due to the total traveled distance of a vehicle and due to the total time required for a vehicle to serve retailers. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after each ten consecutive generations instead of two used in the past. In order to demonstrate the effectiveness of the FLGA, several search methods, branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the FLGA through simulations. Results show that the FLGA outperforms other search methods in all of three considered scenarios.
    IEEE Transactions on Knowledge and Data Engineering 01/2009; 99. · 1.89 Impact Factor
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    ABSTRACT: In the field of supply chain management and logistics, using vehicles to deliver products from suppliers to customers is one of the major operations. Before transporting products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. This paper deals with the problem of optimization of vehicle routing in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time constraint, the objective considered in this paper comprises not only the total traveling distance, but also the total traveling time. We propose using a multi-objective evolutionary algorithm called the fuzzy logic guided non-dominated sorting genetic algorithm 2 (FL-NSGA2) to solve this multi-objective optimization problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FL-NSGA2, we compared it with the following: non-dominated sorting genetic algorithms 2 (NSGA2) (without the guide of fuzzy logic), strength Pareto evolutionary algorithm 2 (SPEA2) (with and without the guide of fuzzy logic), and micro-genetic algorithm (MICROGA) (with and without the guide of fuzzy logic). Simulation results showed that FL-NSGA2 outperformed other search methods in all of three various scenarios.
    Expert Syst. Appl. 01/2009; 36:8255-8268.
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    ABSTRACT: For pallet loading operations, it is found that space optimization does not necessarily lead to profit optimization, which is the ultimate goal of forwarders after numerous site evaluations and end-user feedbacks. To the best of the authors’ knowledge, there are only a few research studies related to profit optimization in this area. This paper presents a hybrid approach, using heuristic and genetic algorithms (GA), for solving the profit-based multi-pallet loading problem which was mathematically formulated as a nonlinear integer programming problem. The major novelties in this paper are the simultaneous consideration of priority for loading more profitable cargoes and cargo stability in heuristic and innovatively designed crossover and mutation operations in GA to suit the profit optimization. To validate the approach, simulations were carried out with 10 weakly and 10 strongly heterogeneous sets of cargoes. The simulation results obtained by our proposed GA were compared with those obtained by two other stochastic search methods, namely simulated annealing (SA) and Tabu search (TS), as well as a nonlinear integer programming-based method, branch-and-bound (BB). The results showed that GA can search more profitable solutions than SA, TS and BB in this multi-pallet loading problem.
    Expert Systems with Applications 01/2009; 36(3):4296–4312. · 1.85 Impact Factor
  • H. Lau, T.M. Chan, W.T. Tsui
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    ABSTRACT: In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the item-location assignment problem, faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the required sum of the total traveling time of the workers to complete all orders is minimized. A stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) is proposed to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. The performance of this novel crossover operation is tested and is shown to be more effective by comparing it to other crossover methods. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make comparisons with the FLGA through simulations, various search methods such as branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, tabu search, differential evolution, and two modified versions of differential evolution are adopted. Results show that the FLGA outperforms the other search methods in all of the three considered scenarios.
    IEEE Transactions on Evolutionary Computation 01/2009; · 4.81 Impact Factor
  • Source
    H.C.W. Lau, T.M. Chan, W.T. Tsui
    [Show abstract] [Hide abstract]
    ABSTRACT: In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the 'Item-location assignment problem', faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the sum of the total traveling time of the workers required for all orders is minimized. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make a comparison with the FLGA through simulations, several search methods, branch and bound, standard GA (i.e. without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted. Results show that the FLGA outperforms the other search methods in the considered scenario.
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on; 10/2007
  • Source
    H. C. W. Lau, W. T. Tsui
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    ABSTRACT: Shipment consolidation is a laborious and, sometimes, tedious task for airfreight forwarders since there is enormous information to be considered and literally quite a number of practical constraints to be fulfilled. In Hong Kong, the unique forwarding operation and rapid cargo flow has further complicated the consolidating process in such a way that local forwarders are almost impossible to achieve the best selection of logistics workflow through the functions of human brain solely. However, none of the currently available intelligent logistics system is able to aid forwarders in making decisions on this crucial operation through the entire supply chain. This paper presents a Heuristics Iterative Reasoning System (HIRS) for solving shipment consolidation problem, adopting rule-based reasoning to provide expert advice for cargo allocation and subsequently applying container loading specific heuristics to support the cargo loading process. Afterwards, the iterative improvement mechanism of HIRS undertakes all outcomes until the most optimal solution is found. A presentation of the concept of HIRS and its development are included in this paper with a case study conducted in Oriented Delivery Limited (a Hong Kong-based company) to validate its feasibility.
    01/2007;
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    ABSTRACT: In today's competitive logistics business environment, airfreight forwarders need to optimize every aspect of their logistics operations. However, forwarders still heavily rely on human brain and working experiences for calculating complex cargo packing and scheduling problems. Although recent research studies related to cargo packing and scheduling problems have resulted in the development of a number of advanced techniques of cargo planning, it can be seen that most of the research work is focused on the optimization of space in order to achieve the maximum possible amount of cargo to be packed in the minimum of space. After numerous site evaluation and end-user feedbacks, it is found that space optimization does not necessarily cause profit optimization, which is the ultimate aim of logistics providers. A study of contemporary research publications indicates that there are inadequate research studies related to profit-based optimization in cargo packing areas. This paper presents a profit-based air cargo loading information system (ACLIS) that embeds an innovative technology known as heuristics iterative reasoning technology (HIRT) that supports loading plan generation, focusing on maximization of the profit margin. In general, the proposed system is meant to maximize the profit in the airfreight forwarding business. It adopts an objective function governed by a list of constraints together with rule-based reasoning to provide expert advice to support the generation of appropriate loading plans
    IEEE Transactions on Industrial Informatics 12/2006; · 8.79 Impact Factor
  • IEEE Trans. Industrial Informatics. 01/2006; 2:303-312.
  • Henry C. W. Lau, W. T. Tsui
    Intelligent Information Processing III, IFIP TC12 International Conference on Intelligent Information Processing (IIP 2006), September 20-23, Adelaide, Australia; 01/2006
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    ABSTRACT: Recent research related to the aircraft container loading and scheduling problem for airfreight forwarding business has seen significant advances in terms of load plan optimization, taking into account the cost and volume of packed boxes. In today's competitive industrial environment, it is essential that freight forwarders are able to collaborate with carriers (airline companies) to achieve the best possible selection of logistics workflow. However, study of contemporary research publications indicates that there is a dearth of articles related to the design and implementation of an intelligent logistics system to support decision-making on carrier selection, aircraft container loading plans as well as carrier benchmarking.This paper presents an intelligent logistics support system (ILSS) which is able to provide expert advice related to the airfreight forwarding business, enhancing the logistics operations in relevant activities within the value chain of tasks. ILSS comprises a heuristics-based intelligent expert system which supports carrier searching and cargo trading planning as well as load plan generation. The proposed approach is meant to enhance various operations in the airfreight forwarding business, adopting computational intelligence technologies such as rule-based reasoning to provide domain advice and heuristics to support the generation of load plans. After potential outcomes are generated by the heuristics-based intelligent expert system, a neural network engine is applied to support prediction of unexpected events. To validate the viability of this approach, a production system using the ILSS has been developed and subsequently applied in an emulated airfreight forwarding environment. The application results indicate that the operation time from searching for potential carriers to the execution of the order is greatly reduced. In this paper, details related to the structure, design and implementation of the ILSS are also covered with the inclusion of the actual program codes for building the prototype.
    Expert Systems 10/2004; 21(5):253 - 268. · 0.77 Impact Factor