Many students need assistance in debugging to achieve progress when they learn to write computer programs. Face-to-face interactions with individual students to give feedback on their programs, although definitely effective in facilitating their learning, are becoming difficult to achieve with ever-growing class sizes. This paper proposes a novel approach to providing practical automated debugging advice to support students' learning, based on the strong relationship observed between common wrong outputs and the corresponding common bugs in students' programs. To implement the approach, we designed a generic system architecture and process, and developed a tool called Virtual Debugging Advisor (ViDA) that was put into use in classes in a university. To evaluate the effectiveness of ViDA, a controlled experiment and a survey were conducted with first year engineering students in an introductory computer programming course. Results are encouraging, showing that (a) a higher proportion of students could correct their faulty code themselves with ViDA enabled, (b) an overwhelming majority of respondents found ViDA helpful for their learning of programming, and (c) most respondents would like to keep ViDA enabled when they practice writing programs.
Genetic algorithms (GAs) have been introduced into site layout planning as reported in a number of studies. In these studies, the objective functions were defined so as to employ the GAs in searching for the optimal site layout. However, few studies have been carried out to investigate the actual closeness of relationships between site facilities; it is these relationships that ultimately govern the site layout. This study has determined that the underlying factors of site layout planning for medium-size projects include work flow, personnel flow, safety and environment, and personal preferences. By finding the weightings on these factors and the corresponding closeness indices between each facility, a closeness relationship has been deduced. Two contemporary mathematical approaches -fuzzy logic theory and an entropy measure -were adopted in finding these results in order to minimize the uncertainty and vagueness of the collected data and improve the quality of the information. GAs were then applied to searching for the optimal site layout in a medium-size government project using the GeneHunter software. The objective function involved minimizing the total travel distance. An optimal layout was obtained within a short time. This reveals that the application of GA to site layout planning is highly promising and efficient.
In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.Construction firm, genetic algorithm, loan and finance, optimization,
This paper reports the results of an investigation into capital budgeting evaluation practicesin the construction industry of Hong Kong. The aim of this study was to identify thepopularity and extent of usage of various techniques for capital budget evaluation, investmentappraisal, risk analysis, and management science. The current study was comparedwith a similar survey conducted in 1994 to establish the changes in the capital budgetingevaluation practices of contracting firms over time. The results indicate that there was ageneral increase in the popularity and extent of usage in certain capital budget evaluationtechniques such as “best/worst estimate” and “formal financial evaluation”. In addition,the evaluation techniques examined were fitted into a discriminant function analysis (DFA),and a model has been developed which allows contracting firms to be classified accordingto their predominant characteristics in capital budget evaluation.
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