TY - JOUR
PY - 2023//
TI - A combined AHP-TOPSIS model for the evaluation and selection of truck drivers
JO - Journal of theoretical and applied information technology
A1 - Benallou, Imane
A1 - Azmani, Abdellah
A1 - Azmani, Monir
SP - 2837
EP - 2847
VL - 101
IS - 7
N2 - Accidents involving heavy trucks result in severe human and material damage. This severity is mainly due to the weight and difficulty controlling the truck. Human error is often the cause of road accidents, hence the interest in carefully choosing the appropriate driver to deliver an order. In fact, the drivers likely to deliver an order must be evaluated according to a set of criteria to choose the one with the least risk of causing an accident. In solving selection problems, multi-criteria decision support methods are often used in most domains. In this paper, we address the problem of decision-making in the transportation domain and, more precisely, the driver selection problem. We propose a model based on two multi-criteria decision support methods, AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), to select the driver who has the most negligible probability of causing an accident. A driver's choice is justified by ranking all the candidate drivers according to a range of criteria using the combined AHP-TOPSIS model. The AHP method determines and calculates the relative weights of the decision criteria, while the TOPSIS method is used to obtain the final ranking of alternatives. The prioritization of the evaluation criteria was done based on brainstorming with experts in the field, which allowed us to provide a decision support tool for carriers to evaluate their drivers before assigning them to different routes. The results indicate that the use of medicinal products containing Gemfibrozil and Glibenclamide and the driver's affliction with diabetes are the main criteria in the driver selection process. Language: en
LA - en
SN - 1992-8645
UR - http://dx.doi.org/
ID - ref1
ER -