Purpose: The construction industry today is one of the biggest industries in the world. As projects continue to grow in complexity, project management continues to evolve. Contractor selection is a difficult task that owners and project managers face. Although previously researchers have worked on the subject of contractor selection, a comprehensive decision support system for contractor selection has not yet been developed. Recent reports of major delays and cost overruns in mega projects highlight the need for a model that is able to be flexible and comprehensive becomes evident. The paper aims to discuss these issues. Design/methodology/approach: The research focuses on obtaining insights from field experts using both quantitative and qualitative methods. Then, a model was developed in the light of the data collected. Accordingly, the model was tested on a case study. Findings: This paper presents a model for contractor selection that is wholesome in its take on the topic. The model incorporates both managerial and technical aspects of the problem. The model was tested on a case study and it was proven to be feasible in real world applications. The contractor selection decision support system serves the needs of both academics and industry managers, as an integral part of project management. Originality/value: The model presented in this paper is innovative in its take on the problems. MCDA tools have been uniquely modified in this paper to cater to the needs of the selection problem while accounting for the criteria hierarchy that incorporates aspects that are instrumental for proper evaluation of a contractor’s likelihood of success.

As part of the global action towards the environmental challenges our world has been facing recently, an urgent call for promoting environmental sustainability has emerged to tackle the deterioration of the quality of the environment. The improper management of chemical wastes generated by laboratories of higher education institutions has become a great concern, with minimal universities taking the initiative to tackle the issue. In this study, a chemical waste management system consisting of four interrelated functions has been developed in order to properly manage the chemical wastes generated at the engineering laboratories of the University Of Balamand. The use of ChemSorter can help the university in developing real-time chemical inventories with minimal cost using a user-friendly experimental setup. The study employed standard software engineering methodology, starting with identifying system requirements and designing the system accordingly. The ChemSorter prototype consisted of four modules: Experiment, Inventory, Library, and Reporting, which were designed and tested during implementation. The development of this on-campus chemical waste database application helps reduce data entry time and improves decision support for waste storage and disposal.

Purpose: The purpose of this paper is to develop a stochastic detailed schedule for a preventive/scheduled/periodic maintenance program of a military aircraft, specifically a rotorcraft or helicopter. Design/methodology/approach: The new model, entitled the military “periodic aviation maintenance stochastic schedule” (PAM-SS), develops a stochastic detailed schedule for a PUMA SA 330SM helicopter for the 50-h periodic inspection, using cyclic operation network (CYCLONE) and Monte Carlo simulation (MCS) techniques. The PAM-SS model identifies the different periodic inspection tasks of the maintenance schedule, allocates the resources required for each task, evaluates a stochastic duration of each inspection task, evaluates the probability of occurrence for each breakdown or repair, develops the CYCLONE model of the stochastic schedule and simulates the model using MCS. Findings: The 50-h maintenance stochastic duration follows a normal probability distribution and has a mean value of 323 min and a standard deviation of 23.7 min. Also, the stochastic maintenance schedule lies between 299 and 306 min for a 99 per cent confidence level. Furthermore, except the pilot and the electrical team (approximately 90 per cent idle), all other teams are around 40 per cent idle. A sensitivity analysis is also performed and yielded that the PAM-SS model is not sensitive to the number of technicians in each team; however, it is highly sensitive to the probability of occurrence of the breakdowns/repairs. Practical implications: The PAM-SS model is specifically developed for military rotorcrafts, to manage the different resources involved in the detailed planning and scheduling of the periodic/scheduled maintenance, mainly the 50-h inspection. It evaluates the resources utilization (idleness and queue), the stochastic maintenance duration and identifies backlogs and bottlenecks. Originality/value: The PAM-SS tackles military aircraft planning and scheduling in a stochastic methodology, considering uncertainties in all inspection task durations and breakdown or repair durations. The PAM-SS, although developed for rotorcrafts can be further developed for any other type of military aircraft or any other scheduled maintenance program interval.

This study evaluates eight biodiesel blend types and determines the overall optimal blend by applying two established multi-criteria decision-making methods: the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The selected blends represent widely produced and utilized feedstocks that are reported in the previous literature. In the proposed methodology, AHP is employed to determine the weights for both emissions-related subcriteria, quantified through Global Warming Potential scores and property-related subcriteria, thereby reducing the subjectivity often encountered in earlier studies. Furthermore, two boundary alternatives, defined as the “Best” and “Worst” based on international standards, are introduced to enhance the robustness of the normalization procedure. The weights determined via AHP are subsequently integrated into the TOPSIS framework to rank the biodiesel alternatives. This combined AHP-TOPSIS approach addresses a gap in the literature, as no previous study has compared the best performing blends from different sources to identify a single optimal alternative. The results indicate that a 20% sunflower biodiesel blend (SN20) achieves the highest ranking. Sensitivity analyses, including the incorporation of an additional economic criterion, consistently reaffirm SN20’s superior performance. This study offers a transparent and reproducible method that can guide future biodiesel blend evaluations and reduce subjectivity in comparative assessment.