The delivery business is growing rapidly and consistently in the whole world. This is leading small and medium enterprises (SMEs) to join the business of the last mile delivery service. SMEs are different from the big players in services and products offered; thus, also the optimisation objectives of the SMEs are different from the big partners: often, SMEs do not possess vehicles and outsource to external partners the last mile deliveries, thus they aim to optimise the assignment of the deliveries for minimising the costs. Hence, the development of methods to find feasible solutions for assignation problems related to last mile delivery becomes fundamental for SMEs. This paper aims to address the problem of cost minimisation when outsourcing the last mile delivery service by mathematically representing the assignment decision problem and by proposing the genetic algorithm (GA) as metaheuristic to solve the problem. The tests revealed the effectiveness of the GA.

Practical optimisation model for SMEs of last mile delivery service

Fera, Marcello;Caterino, Mario
2025

Abstract

The delivery business is growing rapidly and consistently in the whole world. This is leading small and medium enterprises (SMEs) to join the business of the last mile delivery service. SMEs are different from the big players in services and products offered; thus, also the optimisation objectives of the SMEs are different from the big partners: often, SMEs do not possess vehicles and outsource to external partners the last mile deliveries, thus they aim to optimise the assignment of the deliveries for minimising the costs. Hence, the development of methods to find feasible solutions for assignation problems related to last mile delivery becomes fundamental for SMEs. This paper aims to address the problem of cost minimisation when outsourcing the last mile delivery service by mathematically representing the assignment decision problem and by proposing the genetic algorithm (GA) as metaheuristic to solve the problem. The tests revealed the effectiveness of the GA.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/562389
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