Optimization for Library Materials Acquisition Problems

• Main Authors: Wu, Yi-Ling, 吳怡菱
• Other Authors: Bertrand Miao-Tsong Lin
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/63781403201727918417

博士 === 國立交通大學 === 資訊管理研究所 === 102 === The price inflation of library materials, the shrinking of library budget, and the growth of electronic resources continue to challenge library materials acquisition. Subject to the requirements of various fields of patrons, one of the most challenging issues is to acquire materials fairly, and to ensure that the acquired materials attain the highest and best use of the budget. This study proposes an optimization framework of the library materials acquisition problems. To demonstrate the applicability of the proposed framework, four variants are formulated in integer programs and tailored discrete particle swarm optimization (DPSO) is deployed to produce approximate solutions. The first variant, Average Preference Maximization Problem with Centralized Budget (APMP with CB), is to maximize the average preference of the acquired materials. The decisions are to determine which materials should be acquired under the constraints of centralized budget and the limit on the number of materials in each category. To demonstrate the feasibility and applicability of the proposed DPSO algorithms, computational experiments are conducted. Computational results show that the proposed approaches are able to provide quality solutions for the problem in assorted scenarios within a reasonable time. The second variant, Total Preference Maximization Problem with Decentralized Budget (TPMP with DB), is to maximize the total preference of the acquired materials. The decisions are to determine which materials should be acquired by which departments under the constraints of departments’ budgets and the limit on the number of the acquired materials in each written language and in each category. Two different constraint-handling mechanisms are designed for the applied DPSO algorithm. It is evident from the computational results that one constraint–handling mechanism can solve the problem effectively and efficiently, while the repair operator takes more execution time. With the same decision and constraints as the second variant, the third variant, Average Preference and Execution Rate Maximization Problem with Decentralized Budget (APERMP with DB), is to maximize the average preference and execution rate of the acquired materials. The decisions are to determine which materials should be acquired and which departments should cover the cost associated with those materials under the constraints of departments’ budgets and the limit on the number of the acquired materials in each written language and in each category. To tackle the constrained problem, a DPSO with scout particles is presented. A series of computational experiments are designed and conducted. The results are statistically analysed, and it is evinced that the proposed DPSO is an effective approach for the studied problem. The fourth variant, Total Preference Maximization Problem with Centralized Budget (TPMP with CB), is to maximize the total preference of acquired materials. The decisions are to determine which materials should be acquired from which vendor by which acquisition method under the constraints of centralized budget and the limit on the cost of the acquired materials of titles, packages, acquisition methods, each written language, and each category.