Management and Marketing - Research Publications

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End date: 2013 × Author: Ho, William ×

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Now showing 1 - 10 of 41
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    A genetic algorithm for the generalised transportation problem
    Ho, W ; Ji, P (Inderscience, 2005-07-11)
    The generalised transportation problem (GTP) is an extension of the linear Hitchcock transportation problem. However, it does not have the unimodularity property, which means the linear programming solution (like the simplex method) cannot guarantee to be integer. This is a major difference between the GTP and the Hitchcock transportation problem. Although some special algorithms, such as the generalised stepping-stone method, have been developed, but they are based on the linear programming model and the integer solution requirement of the GTP is relaxed. This paper proposes a genetic algorithm (GA) to solve the GTP and a numerical example is presented to show the algorithm and its efficiency. Copyright © 2005 Inderscience Enterprises Ltd.
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    A multi-depot travelling salesman problem and its iterative and integrated approaches
    Ho, W ; Ji, P ; Dey, PK (Inderscience, 2006)
    Resource allocation is one of the major decision problems arising in higher education. Resources This paper formulates a logistics distribution problem as the multi-depot travelling salesman problem (MDTSP). The decision makers not only have to determine the travelling sequence of the salesman for delivering finished products from a warehouse or depot to a customer, but also need to determine which depot stores which type of products so that the total travelling distance is minimised. The MDTSP is similar to the combination of the travelling salesman and quadratic assignment problems. In this paper, the two individual hard problems or models are formulated first. Then, the problems are integrated together, that is, the MDTSP. The MDTSP is constructed as both integer nonlinear and linear programming models. After formulating the models, we verify the integrated models using commercial packages, and most importantly, investigate whether an iterative approach, that is, solving the individual models repeatedly, can generate an optimal solution to the MDTSP.
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    An integrated multiple criteria decision making approach for resource allocation in higher education
    Ho, W ; Higson, HE ; Dey, PK (Inderscience, 2007)
    Resource allocation is one of the major decision problems arising in higher education. Resources must be allocated optimally in such a way that the performance of universities can be improved. This paper applies an integrated multiple criteria decision making approach to the resource allocation problem. In the approach, the Analytic Hierarchy Process (AHP) is first used to determine the priority or relative importance of proposed projects with respect to the goals of the universities. Then, the Goal Programming (GP) model incorporating the constraints of AHP priority, system, and resource is formulated for selecting the best set of projects without exceeding the limited available resources. The projects include 'hardware' (tangible university's infrastructures), and 'software' (intangible effects that can be beneficial to the university, its members, and its students). In this paper, two commercial packages are used: Expert Choice for determining the AHP priority ranking of the projects, and LINDO for solving the GP model
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    Applied operational research with SAS
    Emrouznejad, A ; Ho, W (CRC Press (Taylor & Francis Group), 2011-12)
    Using a wide range of operational research (OR) optimization examples, Applied Operational Research with SAS demonstrates how the OR procedures in SAS work. The book is one of the first to extensively cover the application of SAS procedures to OR problems, such as single criterion optimization, project management decisions, printed circuit board assembly, and multiple criteria decision making. The text begins with the algorithms and methods for linear programming, integer linear programming, and goal programming models. It then describes the principles of several OR procedures in SAS. Subsequent chapters explain how to use these procedures to solve various types of OR problems. Each of these chapters describes the concept of an OR problem, presents an example of the problem, and discusses the specific procedure and its macros for the optimal solution of the problem. The macros include data handling, model building, and report writing. While primarily designed for SAS users in OR and marketing analytics, the book can also be used by readers interested in mathematical modeling techniques. By formulating the OR problems as mathematical models, the authors show how SAS can solve a variety of optimization problems.
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    Optimal Production Planning for PCB Assembly
    Ho, W ; Ji, P (Springer Verlag, 2007-01-01)
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    Strategic Logistics Outsourcing: An Integrated QFD and AHP Approach
    Ho, W ; Bennett, DJ ; Mak, KL ; Chuah, KB ; Lee, CKM ; Hall, MJ (IEEE, 2009)
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    Component scheduling for chip shooter machines: a hybrid genetic algorithm approach
    Ho, W ; Ji, P (PERGAMON-ELSEVIER SCIENCE LTD, 2003-12)
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