Tradeoff analysis for a flyover bridge construction using Evolutionary Algorithm

Purpose In construction projects achieving objectives related to time, cost, quality with safety is a challenging task. The purpose of tradeoff analysis using Evolutionary Algorithms (EA) is to determine the best project strategies that effectively balance the competing factors of time, cost, safety and quality. Design/methodology/approach The present work deals with a multiple objective optimization model to aid decision-makers in balancing the Time-Cost-Quality-Safety (TCQS) tradeoffs, which are often critical factors in project management and various operational decisions. The model uses a Multiple Objective Genetic Algorithm (MOGA) with crossover, mutation and selection parameters to produce a set of Pareto-optimal solutions, offering a range of tradeoff choices for TCQS. Findings To explain the prospects of the new model, a case study of the flyover bridge project at Rau Circle on the Indore-Dewas stretch in the State of M.P. is used. The outcomes of the present model were able to evaluate the shortest flyover project duration at 726 days while keeping the cost of 15,80,95,750 INR, project quality 80.95% and project safety 84.36% respectively of the 1st solution. However, in the 3rd solution with an additional cost of 20,68,9935 INR (13.33% increases), project quality was enhanced to 93.44%, safety increased to 95.33%, and the project duration was 772 days. The 2nd solution lies between the 1st and 3rd solutions in most aspects. Although the increase in cost led to longer duration, it significantly enhanced quality and safety both of which are closely linked. Therefore, 4th solution is recommended as the optimal solution in this study for effectively balancing the Time-Cost-Quality-Safety (TCQS) tradeoffs. Research limitations/implications The present research limitations are based on a Multiple Objective Genetic Algorithm (MOGA) such as parameter sensitivity, fitness function challenges and computational expense for large-scale construction problems. The multi-objective optimization literature by integrating safety and detailed quality quantification into construction optimization models. The application of the model to a real flyover project effectively bridges theory and practice, demonstrating economic and operational relevance. Practical implications Subsequently, the present algorithm is an efficient approach that can assist project leaders in selecting the best course of action for a given construction activity. The proposed MOGA-based framework provides a decision-support tool for project managers to evaluate TCQS tradeoffs and select balanced resource utilization strategies. Social implications This study aims to alleviate congestion caused by heavy vehicles and to reduce pollution, travel time, and costs for passengers. Originality/value The study demonstrates novelty through TCQS integration and validation with real project data by integrating Time-Cost-Quality-Safety (TCQS) objectives within an integrated optimization framework using a Multi-Objective Genetic Algorithm (MOGA) applied to a flyover bridge construction project. Through an iterative evolutionary process that assesses alternative solutions, eliminates inferior options and gradually refines superior candidates toward optimal or nearly optimal outcomes, a MOGA enables efficient balancing of time, cost, quality and safety tradeoffs. The effect of control parameters influences the Pareto-optimal solutions, which were identified through a trial-and-error approach. However, it also depends on the bounds of the decision variables and the constraints of the fitness functions.