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Wang Dingwei

Bio: Wang Dingwei is an academic researcher from Northeastern University. The author has contributed to research in topics: Dynamic programming & Price discrimination. The author has an hindex of 1, co-authored 10 publications receiving 87 citations.

Papers
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Proceedings ArticleDOI
30 Aug 2006
TL;DR: The paper makes the attempt to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint, and shows that the proposed algorithm can find competitive solutions even not all of the best solutions within rational time, especially for large scale problems.
Abstract: Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP). The MTSP can be generalized to a wide variety of routing and scheduling problems. It is known that classical optimization procedures are not adequate for this problem. The paper makes the attempt to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint. In this paper, we compare it with MGA by testing several standard problems from TSPLIB. The computational results show that the proposed algorithm can find competitive solutions even not all of the best solutions within rational time, especially for large scale problems

88 citations

Proceedings ArticleDOI
23 May 2007
TL;DR: A new e-Commerce model is presented to support consumer buying decision making and some new intelligent technologies like software agent, neural computing, Analytical Hierarchy Process, data mining are used.
Abstract: Facing the flood of information and increasing competitive environment, the e-Commerce seller must make its software facility more customer-oriented, more intelligent, and build one-to-one marketing A new e-Commerce model is presented to support consumer buying decision making Some new intelligent technologies like software agent, neural computing, Analytical Hierarchy Process, data mining are used Some application testing results prove the algorithm's efficiency and the model's practicality

1 citations

Proceedings ArticleDOI
Guo Zhe1, Wu Junxin1, Tang Jiafu1, Wang Dingwei1, Liu Shixin1 
02 Jul 2008
TL;DR: There are a lot of illogical factors in traditional pricing business process, such as low level of information, automation and poor net, which decrease pricing efficiency, especially in electronic commerce.
Abstract: There are a lot of illogical factors in traditional pricing business process, such as low level of information, automation and poor net, which decrease pricing efficiency, especially in electronic commerce. To solve this problem, traditional pricing business process is analyzed, valued and reengineered with a method of business process quantitative analysis based on process algebra. Then a new pricing business process that is suitable for electronic commerce environment is established according to the principles of business process reengineering and the characteristics of pricing in electronic commerce environment. The evaluation criterions before and after process reengineering are investigated, and the results are satisfied. The results verify that the methods are practical.

1 citations

Proceedings ArticleDOI
08 Aug 1988
TL;DR: This work proposes a new approach to estimate the parameters by human-computer interaction that is used to identify the boiler efficiency curves in a power plant and has proved that it is a very efficient and useful tool.
Abstract: It is often difficult to estimate the facility performance parameters in a complex industrial system. The estimated parameters by only regression methods Iike contradictory to the people's experiences so they can't he accepted by the operators. We propose a new approach to estimate the parameters by human-computer interaction. At first the computer acquires the knowledge about the facility performance from experts then it cut out the unreasonable samplers and determines the above and below boundaries of parameters. The acceptable parameters call be obtained by a bounded parameter estimation algorithm. This approach is used to identify the boiler efficiency curves in a power plant, the results have proved that it is a very efficient and useful tool.

1 citations

Proceedings ArticleDOI
Guo Zhe1, Tang Jiafu1, Wang Dingwei1, Liu Shixin1, Wu Junxin1 
02 Jul 2008
TL;DR: In this article, a dynamic pricing model considering competition from congener commodity is built, in which commodity copyright cost, advertisement cost, competitor price, customerpsilas expectation of commodity and customer behavior are considered, for the sake of maximizing the sale and the profit of an online seller.
Abstract: The distinctive characteristics of digital knowledge commodity (DKC) make its pricing method different from traditional commoditypsilas. One hand, cost characteristic of DKC gives us more price choices. On the other hand, characteristics else of DKC bring us more difficulties to price. To solve this problem, the first-degree price discrimination is adopted, with respect to the stage in which the on-line sellerpsilas sale number does not exceed critical sale number. Then dynamic pricing model considering competition from congener commodity is built, in which commodity copyright cost, advertisement cost, competitorpsilas price, customerpsilas expectation of commodity and customer behavior are considered, for the sake of maximizing the sale and the profit of an on-line seller. In customer behavior model, the positive exterior influence of Web on customer purchase behavior is considered, and the influence of commodity brand, history sale number and price are quantized.

1 citations


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BookDOI
01 Jan 2011
TL;DR: The methodology for calculating distortion parameters of newly created attributes after both discretisation and binarisation of attributes for quantitative association rules mining has been proposed and the new application of MMASK for finding frequent sets in discovering quantitative association Rules with preserved privacy has been presented.
Abstract: This paper deals with discovering frequent sets for quantitative association rules mining with preserved privacy. It focuses on privacy preserving on an individual level, when true individual values, e.g., values of attributes describing customers, are not revealed. Only distorted values and parameters of the distortion procedure are public. However, a miner can discover hidden knowledge, e.g., association rules, from the distorted data. In order to find frequent sets for quantitative association rules mining with preserved privacy, not only does a miner need to discretise continuous attributes, transform them into binary attributes, but also, after both discretisation and binarisation, the calculation of the distortion parameters for new attributes is necessary. Then a miner can apply either MASK (Mining Associations with Secrecy Konstraints) or MMASK (Modified MASK) to find candidates for frequent sets and estimate their supports. In this paper the methodology for calculating distortion parameters of newly created attributes after both discretisation and binarisation of attributes for quantitative association rules mining has been proposed. The new application of MMASK for finding frequent sets in discovering quantitative association rules with preserved privacy has been also presented. The application of MMASK scheme for frequent sets mining in quantitative association rules discovery on real data sets has been experimentally verified. The results of the experiments show that both MASK and MMASK can be applied in frequent sets mining for quantitative association rules with preserved privacy, however, MMASK gives better results in this task.

182 citations

29 Dec 2017
TL;DR: The implementation of an extension of the Lin-Kernighan-Helsgaun TSP solver, which is able to solve a variety of well-known problems, including the sequential ordering problem, the traveling repairman problem, variants of the multiple travel-ing salesman problem, as well as vehicle routing problems with capacity, time windows, pickup-and-delivery and distance constraints.
Abstract: This report describes the implementation of an extension of the Lin-Kernighan-Helsgaun TSP solver for solving constrained traveling salesman and vehicle routing problems. The extension, which is called LKH-3, is able to solve a variety of well-known problems, including the sequential ordering problem (SOP), the traveling repairman problem (TRP), variants of the multiple travel-ing salesman problem (mTSP), as well as vehicle routing problems (VRPs) with capacity, time windows, pickup-and-delivery and distance constraints. The implementation of LKH-3 builds on the idea of transforming the problems into standard symmetric traveling salesman problems and handling constraints by means of penalty functions. Extensive testing on benchmark instances from the literature h s s own that LKH-3 is effective. Best known solutions are often obtained, a d in so e cases, new best solutions are found. The program is free of charge for academic and non-commercial use and can be downloaded in source code. A comprehensive library of benchmark instances and the best obtained results for these instances can also be downloaded.

107 citations

Journal ArticleDOI
01 Jun 2010
TL;DR: The theory of Collective Intelligence (COIN) is discussed using the modified version of Probability Collectives (PC) to achieve the global goal and the optimum results to the Rosenbrock function and both the MDMTSP test cases are obtained at reasonable computational costs.
Abstract: Complex systems generally have many components. It is not possible to understand such complex systems only by knowing the individual components and their behavior. This is because any move by a component affects the further decisions/moves by other components and so on. In a complex system, as the number of components grows, complexity also grows exponentially, making the entire system to be seen as a collection of subsystems or a Multi-Agent System (MAS). The major challenge is to make these agents work in a coordinated way, optimizing their local utilities and contributing the maximum towards optimization of the global objective. This paper discusses the theory of Collective Intelligence (COIN) using the modified version of Probability Collectives (PC) to achieve the global goal. The paper successfully demonstrated this approach by optimizing the Rosenbrock function in which the coupled variables are seen as autonomous agents working collectively to achieve the function optimum. To demonstrate the PC approach on combinatorial optimization problems, two test cases of the Multi-Depot Multiple Traveling Salesmen Problem (MDMTSP) with 3 depots, 3 vehicles and 15 nodes are solved. In these cases, the vehicles are considered as autonomous agents collectively searching the minimum cost path. PC is successfully accompanied with insertion, elimination and swapping heuristic techniques. The optimum results to the Rosenbrock function and both the MDMTSP test cases are obtained at reasonable computational costs.

72 citations

Proceedings ArticleDOI
25 May 2009
TL;DR: An ant colony optimization (ACO) algorithm for the multiple traveling salesmen problem (MTSP) with two objectives: the objective of minimizing the maximum tour length of all the salesmen and the objectives of minimize the maximumTour length of each salesman.
Abstract: The multiple traveling salesmen problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Though the MTSP is a typical computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing and scheduling problems. The paper proposed an ant colony optimization (ACO) algorithm for the MTSP with two objectives: the objective of minimizing the maximum tour length of all the salesmen and the objective of minimizing the maximum tour length of each salesman. In the algorithm, the pheromone trail updating and limits followed the MAX-MIN Ant System (MMAS) scheme, and a local search procedure was used to improve the performance of the algorithm. We compared the results of our algorithm with genetic algorithm (GA) on some benchmark instances in literatures. Computational results show that our algorithm is competitive on both the objectives.

59 citations

Journal Article
TL;DR: In this article, a non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the multi-objective version of the Multiple Traveling Salesman Problem (MOmTSP), in particular, the minimization of the total traveled distance and the balance of the working times of the traveling salesmen.
Abstract: Article history: Received March 29, 2015 Received in revised format: May 12, 2015 Accepted May 12, 2015 Available online May 14 2015 This paper considers a multi-objective version of the Multiple Traveling Salesman Problem (MOmTSP). In particular, two objectives are considered: the minimization of the total traveled distance and the balance of the working times of the traveling salesmen. The problem is formulated as an integer multi-objective optimization model. A non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the MOmTSP. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. Tests on real world instances and instances adapted from the literature show the effectiveness of the proposed algorithm. Growing Science Ltd. All rights reserved. 5 © 201

43 citations