ComURS 2026 http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5285 2026-05-17T06:18:09Z 2026-05-17T06:18:09Z Dilanka, M.R. Wasalthilaka, W.V.S.K. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5292 2026-05-15T10:07:16Z 2026-01-28T00:00:00Z

A Multi-Dimensional Analysis of Infrastructure Considerations in Industrial Facility Placement Using Machine Learning Dilanka, M.R.; Wasalthilaka, W.V.S.K. Increasingly complex global economies are making the process of choosing factory locations much more difficult for policymakers and investors. In their traditional form, assessments of factory site locations no longer represent current factors affecting factory placements such as new and improved transportation infrastructure and the growth of Industry 5.0, and as a result do not give a long-term perspective on future industrial site placements. Therefore, the study presents a data-driven approach, combining industrial location theory and modern predictive analytics, to identify potential future factory locations. The approach recommended in this study will require that these factors be analyzed on a national level using predictive analysis techniques to determine the likelihood of future location suitability. Site selection factors will include analyzing six primary determinants (electric service reliability, transport/logistics performance, gross domestic product, inflation, trade openness, and political stability) influencing industrial location decisions in 151 countries from 2000-2024. Various forecasting techniques, such as vector auto-regression, random forest, XGBoost, linear regression, LSTM, and a VAR-XGBoost hybrid, determined each factor’s projected 2024 value. MSE and R2 metrics indicated the model’s accuracy. The random forest combination achieved the highest accuracy. The unique combination random forest achieved the highest level of accuracy. By also aggregating predicted values into a weighted Composite Factory Suitability Index allows for the establishment of a predictor of industrial location potential as well future location of factories. This research offers an adaptive, predictive approach to evaluating factory site suitability, enabling strategic decision-making for policymakers, investors, and industries globally in a rapidly changing business environment.

2026-01-28T00:00:00Z Gnanarathne, S.D.D.S Kumara, P.G.P. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5291 2026-05-15T10:00:38Z 2026-01-28T00:00:00Z

Hybrid Approach for Automated University Academic Timetable using Graph Coloring Techniques and Linear Programming Mathematical Resource Optimization Model Gnanarathne, S.D.D.S; Kumara, P.G.P. University course timetabling is a complex optimization problem that must satisfy multiple hard and soft constraints. These include avoiding clashes among courses and lecturers, allocating limited classroom and laboratory spaces, and ensuring efficient use of academic resources. Traditional manual or semi-manual timetabling methods often result in scheduling conflicts, inefficient utilization of facilities, and significant time wastage, which adversely affects academic activities. To overcome these challenges, this study proposes an integrated approach that combines graph coloring techniques with a linear mathematical optimization model to automate the university timetabling process. The proposed methodology is adaptable to different academic environments and institutional contexts. Its effectiveness is validated through a real-world case study conducted at the Faculty of Computing, Sabaragamuwa University of Sri Lanka, using academic and scheduling data analyzed with MATLAB. The study is organized into two main phases. In the first phase, a graph-based model represents courses as vertices and scheduling conflicts as edges. An adjacency matrix and the Welsh–Powell graph coloring algorithm are employed to assign a minimum number of conflict-free time slots. In the second phase, a linear programming model is applied to optimize room allocation with the objective of maximizing the utilization of available lecture halls and laboratories. The results indicate that the proposed system can produce completely conflict-free timetables while significantly enhancing lecture room utilization. As a next step, the study aims to evaluate seat wastage by analyzing unoccupied seating capacity. Future enhancements include increasing automation through the development of a more user-friendly interface. Overall, this research provides a structured and practical solution to the university timetabling problem, contributing to improved administrative efficiency and effective resource utilization.

2026-01-28T00:00:00Z Premathilaka, M.H.H.D.N. Ekanayake, E.M.U.S.B. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5290 2026-05-15T09:53:45Z 2026-01-28T00:00:00Z

An innovative heuristic algorithm for multi-objective transportation problems using improved ant colony algorithm Premathilaka, M.H.H.D.N.; Ekanayake, E.M.U.S.B. The transportation problem is a well-known optimization challenge that aims at minimizing the total costs for distributing resources from different sources to numerous destinations. In complicated logistical situations, many objectives such as cost, time, and distance are optimized together. This leads to the multi objective transportation problem where a productive compromise solution is sought. Although literature has established various methods like goal and fuzzy programming, these approaches often fall short for large-scale instances due to high computational demands. In this study, an innovative heuristic algorithm is established using an Improved Ant Colony Optimization approach combined with a harmonic cost matrix to aggregate conflicting goals. The incremental novelty of this work is distinguished by the introduction of a static probabilistic penalty mechanism. Unlike traditional methods requiring dynamic recalculations, this deterministic approach utilizes a desirability matrix to simplify decision-making. Furthermore, this research eliminates the standard reliance on dummy variables, maintaining the original problem dimensionality and saving significant computational resources. The efficiency of this technique is validated through benchmarks comparing the Improved ant colony optimization method to other methods. Performance results demonstrate superior outcomes: Example 1 achieves a 3.8% reduction in distance; Examples 2 and 5 yield identical optimal solutions; Example 3 reduces time by 5.8%; and Example 4 achieves a 28.6% cost improvement. It can definitely be concluded that the algorithm could be a highly powerful, flexible, and efficient tool for dealing with large classes of optimization problems likely to occur in real-world logistics.

2026-01-28T00:00:00Z Rodrigo, P.G.N. Perera, A.A.I Mohommad, M.A.M. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5289 2026-05-15T09:42:41Z 2026-01-28T00:00:00Z

Five-color incidence coloring of the recursive modified claw graph Rodrigo, P.G.N.; Perera, A.A.I; Mohommad, M.A.M. Graph theory is a branch of mathematics that studies how objects are connected using vertices and edges. An important concept within this field is incidence coloring, where colors are assigned to vertex edge pairs, called incidences, rather than to vertices or edges alone. The minimum number of colors required to ensure that no two adjacent incidences receive the same color is known as the incidence chromatic number, denoted by χi(G). This research introduces the Recursive Modified Claw Graph, constructed from a four-edge base graph with one central vertex of degree four with four leaves. The graph is expanded level by level attaching new duplicates of the base graph to the leaves created in the previous level according to a fixed recursive pattern, while maximum degree remains four. The general structure (Gn) has V(n) = 6×3n−1 −1 vertices and E(n) = 6×3n−1 −2 edges for all n ≥ 1, where n ∈ Z+. A cycling five color palette is introduced by rotating a level color cn through {1,2,3,4,5} while each new center vertex uses the remain four colors, and all new leaf-side incidences use cn. An induction proof shows this always gives a proper incidence coloring with χi(Gn) ≤ 5 for all n ≥ 1 with potential applications in areas such as timetable scheduling, network optimization and resource allocation, where conflict-free assignments are essential. This work extends existing incidence coloring theory beyond trees and standard cactus graphs to structured claw-based recursive families, contributing both theoretical insights and a scalable algorithmic framework.

2026-01-28T00:00:00Z