An Integrated Decision-Making Framework to Evaluate the Route Alternatives in Overweight/Oversize Transportation

Abstract

Overweight and oversized transport (O&OT) has become one of the most critical elements of project logistics, driven by advancements in transportation and lifting technologies that now allow high-volume loads to be moved across long distances. This type of transportation operation, also called abnormal transportation, is greatly affected by technical factors such as the weight and geometry of the load, road surface, axle load limitations, slope, and ground strength, as well as external variables such as weather conditions, traffic density, and legal regulations. In planning and operational processes, Decision-Makers (DMs) and practitioners who plan and execute operations without adequately considering these factors and variables can lead to delays in operations, serious risks, and loss of productivity. This research proposes a flexible decision support model that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and a ranking technique; i.e., Mixed Aggregation by Comprehensive Normalization Technique (MACONT) techniques to address the decision problems related to route selection, one of the most critical problems in transporting heavy and bulky loads, and to produce reasonable solutions. The proposed model significantly reduces information losses by processing subjective and objective information and integrating subjective (SWARA) and objective (LOPCOW) methods. Unlike traditional ranking approaches, the MACONT method combines three different normalization techniques to determine the ranking performance of alternatives. In this way, it provides more reliable and accurate results by reducing the deviations of the results provided by the single normalization technique. In addition, it shows each alternative's good and bad performance compared to the others and is more convincing about the results obtained. According to the results obtained by applying the proposed model, fuel consumption (0.096) is determined as the most effective and critical factor in selecting the route on which heavy and bulky loads will be transported. In this context, choosing routes that allow lower fuel consumption can contribute to reducing carbon emissions and external costs arising from transportation. The extensive robustness and validation check to test the proposed model prove that the proposed model is a reliable, robust, and practical decision-making tool for making reasonable and rational decisions in O&OT.