Heterogenous Adaptive Ant Colony Optimization with 3-opt local search for the Travelling Salesman Problem

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThe majority of optimization algorithms require proper parameter tuning to achieve the best performance. However, it is well-known that parameters are problem-dependent as different problems or even different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world problems. One method to mitigate this is to introduce adaptivity into the algorithm to discover good parameter settings during the search. Therefore, this study introduces an adaptive approach to a heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ant colony optimization (ACO) to locate near-optimal solutions. This is achievable by introducing a set of rules for parameter adaptation to occur in order for the parameter values to be close to the optimal values by exploring and exploiting both the parameter and fitness landscape during the search to reflect the dynamic nature of search. In addition, the 3-opt local search heuristic is integrated into the proposed approach to further improve fitness. An empirical analysis of the proposed algorithm tested on a range of Travelling Salesman Problem (TSP) instances shows that the approach has better algorithmic performance when compared against state-of-the-art algorithms from the literature.Faculty of Electronics and Computer Engineering (FKEKK), MalaysiaTechnical University of Malaysia Malacca (UTeM)Ministry of Higher Education (MoHE) Malaysi

Cash flow optimization for construction engineering portfolios

One of the main issues in construction projects is finance; proper cash-flow management is necessary to insure that a construction project finishes within time, on budget, and yielding a satisfying profit. Poor financial management might put the contractor, or the owner, in a situation where they are unable to finance the project due to insufficient liquidity, or where they are engaged in excessive loans to finance the project, decreasing the profit, and even creating unsettled debts. Engagement with a portfolio of large construction projects, like infrastructure projects, makes attention to finance more critical, due to large budgets and long project durations, which also requires attention to the time value of money when the project spans over many years and the work environment has a high inflation rate. This thesis aims at the analysis and optimization of the cash-flow request for large engineering portfolios from the contractor\u27s point of view. A computational model, with a friendly user interface, was created to achieve that. The user is able to create a portfolio of projects, and create activities in them with different relationship types, lags, constraints, and costs, as similar to commercial scheduling software. Parameters necessary for the renumeration are also considered, which include the down payment percentage, duration between invoices, duration for payment, retention percentage, etc. The model takes into consideration the time value of money, calculated with an interest rate assigned to the projects by the user; this could be the inflation rate or the (Minimum Attractive Rate of Return) MARR of the contractor. Optimization is done with the objective of maximizing the Net Present Value (NPV) for the projects as a whole, discounted at the start of the portfolio. The variables for the optimization are lags that are assigned for each activity, which, after rescheduling, delays the activities after their early start with the value of those lags, and thus creates a modified cash flow for the project. Optimization of those variables, within scheduling constraints results in a near-optimum NPV. Verification of the model was done using sets of portfolios, and the validation was done using an actual construction portfolio from real life. The results were satisfactory and matched initial expectations. The NPV was successfully optimized to a near optimum. A sensitivity analysis of the model was conducted and it showed that the model behaves as expected for different inputs. A time test was performed, taking into consideration the effect of the size and complexity of a portfolio on the calculation time for the model, and it showed that the speed was satisfactory, though it should be improved. Overall, the conclusion is that the model delivers its goal of maximizing the Net Present Value of a large portfolio as a whole

BIM-based software for construction waste analytics using artificial intelligence hybrid models

The Construction industry generates about 30% of the total waste in the UK. Current high landfill cost and severe environmental impact of waste reveals the need to reduce waste generated from construction activities. Although literature reveals that the best approach to Construction Waste (CW) management is minimization at the design stage, current tools are not robust enough to support architects and design engineers. Review of extant literature reveals that the key limitations of existing CW management tools are that they are not integrated with the design process and that they lack Building Information Modelling (BIM) compliance. This is because the tools are external to design BIM tools used by architects and design engineers. This study therefore investigates BIM-based strategies for CW management and develops Artificial Intelligent (AI) hybrid models to predict CW at the design stage. The model was then integrated into Autodesk Revit as an add-in (BIMWaste) to provide CW analytics. Based on a critical realism paradigm, the study adopts exploratory sequential mixed methods, which combines both qualitative and quantitative methods into a single study. The study starts with the review of extant literature and (FGIs) with industry practitioners. The transcripts of the FGIs were subjected to thematic analysis to identify prevalent themes from the quotations. The factors from literature review and FGIs were then combined and put together in a questionnaire survey and distributed to industry practitioners. The questionnaire responses were subjected to rigorous statistical process to identify key strategies for BIM-based approach to waste efficient design coordination. Results of factor analysis revealed five groups of BIM strategies for CW management, which are: (i)improved collaboration for waste management, (ii)waste-driven design process and solutions, (iii)lifecycle waste analytics, (iv) Innovative technologies for waste intelligence and analytics, and (v)improved documentation for waste management. The results improve the understanding of BIM functionalities and how they could improve the effectiveness of existing CW management tools. Thereafter, the key strategies were developed into a holistic BIM framework for CW management. This was done to incorporate industrial and technological requirements for BIM enabled waste management into an integrated system.The framework guided the development of AI hybrid models and BIM based tool for CW management. Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed for CW prediction and mathematical models were developed for CW minimisation. Based on historical Construction Waste Record (CWR) from 117 building projects, the model development reveals that two key predictors of CW are “GFA” and “Construction Type”. The final models were then incorporated into Autodesk Revit to enable the prediction of CW from building designs. The performance of the final tool was tested using a test plan and two test cases. The results show that the tool performs well and that it predicts CW according to waste types, element types, and building levels. The study generated several implications that would be of interest to several stakeholders in the construction industry. Particularly, the study provides a clear direction on how CW management strategies could be integrated into BIM platform to streamline the CW analytics