An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems

Abstract

In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.