Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Abstract

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.

Keywords:

• multi-objective optimisation
• particle swarm optimisation
• food quality
• perishable product
• intelligent food logistics operations
• integrated outlining

Acknowledgement

The authors also would like to thank The Hong Kong Polytechnic University Research Committee for financial and technical support.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Felix T.S. Chan http://orcid.org/0000-0001-7374-2396

M.K. Tiwari http://orcid.org/0000-0001-8564-1402

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71901052,71971143]; Research Committee of Hong Kong Polytechnic University: [Grant Number G-UAFS,G-UADM].