Evaluation and Selection of HazMat Transportation Alternatives: A PHFLTS- and TOPSIS-Integrated Multi-Perspective Approach

Abstract

Outsourcing the hazardous materials (HazMat) transportation is an effective way for manufacturing enterprises to avoid risks and accidents as well as to retain sustainable development in economic growth and social inclusion while not bringing negative impacts on the public and the environment. It is imperative to develop viable and effective approaches to selecting the most appropriate HazMat transportation alternatives. This paper aims at proposing an integrated multi-criteria group decision making approach that combines proportional hesitant fuzzy linguistic term set (PHFLTS) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to address the problem of HazMat transportation alternative evaluation and selection. PHFLTSs are adopted to represent the congregated individual evaluations in a bid to avoid information loss and increase the reliability of results. Two weight assignment models are then proposed to determine the comprehensive weights of experts and criteria. Furthermore, several novel manipulations of PHFLTS are also defined to enrich its applicability. The TOPSIS method is subsequently extended to the context of PHFLTSs to rank alternatives and choose the best one. Eventually, the feasibility and validity of the proposed approach are verified by a practical case study of a HazMat transportation alternative evaluation and selection decision and further comparison analyses.

Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management

“…Taking node 2 generated from the root shown in Figure 11a as an example, node 2 represents the accidents caused by equipment malfunction, with a total of 44. The decision tree then classified the specific types of accidents 1,7,8,9,10, and 11 to node 7 in Figure 11c, which represents the number of accidents caused by factor 3, that is, equipment malfunction As shown in Figure 11c, based on the data of HTAs that belong to the specific types and the corresponding proportion of different consequence levels, there was a 5.6% chance that can lead to the specific types of accidents at node 7, that is, collision with fixed objects, multivehicle collide, rollover, run-off-road, tank and safety accessory failure and vehicle-related defect. If it happens, there was an 87.5%, 9.4%, and 3.1% chance for Level II, III, and IV accidents occurring, respectively.…”

“…The degree of danger of transport together with the risk of life is increasing. In light of possible catastrophic losses of lives and economics, as well as damages to the environment, there is an urgent theoretical and practical need to reduce hazmat transportation accidents (HTAs) associated with consequence and severity, which has drawn increasing concerns from the public [5][6][7]. In contrast to general traffic accidents, HTAs can lead to more severe results due to the inherent physical and chemical instabilities of the substances during the road transportation process.…”

Linguistic Einstein aggregation operator‐based TOPSIS for multicriteria group decision making in linguistic Pythagorean fuzzy environment

“…Combining hesitant fuzzy (HF) sets 15 (HFSs) and linguistic variables, 6,7 HF linguistic term sets, 16 proportional HF linguistic term sets, 17 HF linguistic sets, 18 proportional interval type‐2 HF linguistic sets, 19 and linguistic HFSs 20 are developed. In a similar way, Chen et al 21 integrated IFSs 2 with linguistic variables 6,7 and introduced linguistic intuitionistic fuzzy numbers (LIFNs).…”

Risk Cloud Model for Evaluating Nautical Navigational Environments

“…In decision-making, there are some approaches that deal with uncertainty. For example, Chen et al used fuzzy terms and the technique for order preference by similarity to an ideal solution (TOPSIS) method from the perspective of MADM for decision-making and conducted in-depth research on theory and application of fuzzy terms and MADM methods [2][3][4][5]. Wey proffered an integrated approach based on the fuzzy Delphi method and dynamic network process to identify sustainable urban transportation planning strategies to improve quality of life [6].…”