Application of Industry 3.5 approach for planning of more sustainable supply chain operations for tourism service providers

ABSTRACT

This paper addresses a tourist trip design problem and a tour route planning problem to improve the competitiveness of community tourism. The problem was investigated with the objective to maximise the total number of tourists interested in visiting multiple points of interest and several specific activities to receive a satisfactory service by matching their preferences. The problem was formulated as $(\mathrm{F}\mathrm{J}\mathrm{S}\mathrm{P}|\mathrm{T}\mathrm{W},\mathrm{M}{\mathrm{R}}_{ijk},\mathrm{S}{\mathrm{D}}_{ijk}|\sum _{k\in K}\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{\_}\mathrm{O}\mathrm{u}\mathrm{t})$. To solve this problem, a mixed-integer linear programming model was developed for small-size problems, while the modified differential evolution with K-variable moves and random variable neighbourhood search (MDE–RVNS) was developed to determine near-optimal solutions for real problems. To assist the tourism service provider to schedule and sequence the trip and route planning for the tourists, a tourism service provider scheduling and sequencing software based on the MDE-RVNS was designed and developed. A mobile application using the software has been planned for launching to assist tour route planning decision-makers. This can help local tourism businesses to manage demands and ensure that the tourism revenue is evenly distributed in a tourism supply chain to enhance the well-being of local communities.

KEYWORDS:

• Tourism industry
• Industry 3.5
• AI heuristics for optimisation
• sustainable supply chain management

Acknowledgments

We thank Assoc. Prof. Somnuk Theerakulpisut for the critical review and Mr. Ian Thomas for his grammatical review of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [Thumrongvut et al.], upon reasonable request.

Additional information

Funding

This work is supported by the Research Unit on System Modelling for Industry [Grant number SMI. KKU 64004], Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Thailand.