Title: Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model

Authors: Jie Chen; Wei Shi; Xi Wang; Sanjeevi Pandian; V.E. Sathishkumar

Addresses: Institute of Education and Economy Research, University of International Business and Economics, Beijing 100029, China ' Institute of Education and Economy Research, University of International Business and Economics, Beijing 100029, China ' Institute of Economics, Jilin Academy of Social Sciences, Changchun 130033, China ' Jiangnan University, Wuxi 214122, China ' Sunchon National University, Suncheon, South Korea

Abstract: Workforce optimisation has always been a challenge both in conventional industries, including logistics, and in the majority of emerging shared economy systems. In a rapidly changing service environment, customer experiences are important and are influenced by the quality of service provided. This paper proposes a shift workforce allocation, using the heuristic mathematical model (SWA-HMM) for workforce optimisation to enhance the customer experience in urban transportation. Furthermore, urban city mobility is a key differentiator for competitiveness which continues to move to have a more dynamic economy and draw greater domestic investment. Subsequently, workforce optimisation for urban transportation using deep learning as an essential artificial intelligence branch provides knowledge for a computer system to identify, predict, and make decisions by analysing the data relevant to the application deployed. The experimental result shows that there is an overall positive customer experience and enhancement of the performance of urban mobility with better service quality.

Keywords: workforce optimisation; customer experience; urban transportation; artificial intelligence; heuristic mathematical model; maximum passenger flow volume.

DOI: 10.1504/IJSTL.2021.117278

International Journal of Shipping and Transport Logistics, 2021 Vol.13 No.5, pp.538 - 553

Received: 20 Mar 2020
Accepted: 31 Jul 2020
Published online: 31 Aug 2021 *

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