Authors: Osmar Martín Salvador-Grijalva; Sonia Valeria Avilés-Sacoto; Galo Eduardo Mosquera-Recalde
Addresses: Universidad San Francisco de Quito (USFQ), Campus Cumbayá, Diego de Robles s/n, Quito, Pichincha, 170157, Ecuador ' Departamento de Ingeniería Industrial, Colegio de Ciencias e Ingenierías, Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interocenica, 170901, Quito, Pichincha, Ecuador ' Departamento de Ingeniería Industrial, Colegio de Ciencias e Ingenierías, Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interocenica, 170901, Quito, Pichincha, Ecuador
Abstract: In the last years, the growth of e-commerce has caused several changes in the way of how companies administrate their facilities and the transportation of their vehicles. Nowadays, companies must deal with small orders, short delivery schedules and a variable workload. This paper proposes a methodology, based the Ecuadorian company ZDelivery, to optimise the delivery of products with the use of mobile facilities. A series of tools are employed to solve the problem. In first place, a clustering technique is used to find strategic points within the city where the mobile facilities can be located. Then, an adaptation of a mobile facility routing problem is applied to determine the location and time where the mobile facilities should be located. Finally, to solve the model, the Monte Carlo method is applied to handle the uncertainty of the demand. This methodology applied to ZDelivery gives an optimal route for the fleet of the mobile facilities of the company between Friday's nights and Saturday's mornings where the strategic points in which the mobile facilities need to be located are specified for each mobile facility in intervals of 15 minutes.
Keywords: mobile facility; routing problem; Monte Carlo simulation; delivery; clustering; transportation; vehicles; demand; strategic points; time window; costs.
International Journal of Operational Research, 2022 Vol.44 No.4, pp.496 - 521
Received: 11 Jun 2019
Accepted: 01 Nov 2019
Published online: 31 Aug 2022 *