An exact approach for the grocery delivery problem in urban areas
- 334 Downloads
In this paper, we face the problem of delivering a given amount of goods in urban areas in a business-to-consumer (B2C) electronic commerce (EC) environment. This problem can be considered as a particular case of vehicle routing problem. As a novel issue, here we have to determine the fleet of no homogeneous vehicles to be used for satisfying the demands of clients coming from grocery e-channels, and their related itineraries, given the traveling limits imposed by the urban government; in fact, commercial vehicles are not allowed to go everywhere and can travel only in restricted daily time windows, according to their pollution emissions. We have to minimize the overall distribution costs, taking into account traveling components and setup ones, together with operative aspects and environmental issues; customer requirements, vehicle capacity and daily shift constraints have to be satisfied too. We outline the main characteristics of the problem in a B2C EC environment and propose a mixed integer linear programming model to solve this NP-hard problem. Computational results of test bed cases related to different sized transportation networks and delivery demands are presented and analyzed with respect to the fleet of vehicles chosen for satisfying the customer demand and the street traffic limitations. Then, a realistic case study derived from the e-distribution channel of a grocery company of Genoa, Italy, is reported. Considerations about CPU time and optimality gap are also given with the idea of making the proposed model effectively used and solved with any commercial software.
KeywordsDistribution network models E-channel grocery delivery Vehicle routing Mixed integer linear programming models
Compliance with ethical standards
Conflict of interest
The F. Carrabs declares that he has no conflict of interest. The R. Cerulli declares that he has no conflict of interest. The A. Sciomachen declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Carrabs F, Cerulli R, Sciomachen A (2014) Environmental sustainable fleet planning in B2C e-commerce Urban distribution networks”. In: Dameri R, Rosenthal-Sabroux C (eds) Smart city. How to create public and economic value with high technology in urban space. Springer, BerlinGoogle Scholar
- Cattaruzza D, Feillet D, González-Feliu J (2015) Vehicle routing problems for city logistics. EURO J Transp Log, pp 1–29. doi: 10.1007/s13676-014-0074-0
- Chao IM, Liou TS (2005) A new tabu search heuristic for the site dependent vehicle routing problem. In: Golden B, Raghavan S, Wasil E (eds) The next wave in computing, optimization, and decision technologies, vol 29. Springer, New York, pp 107–119Google Scholar
- Chao IM, Golden BL, Wasil EA (1999) A computational study of a new heuristic for the site-dependet vehicle routing problem. INFOR 37:319–336Google Scholar
- Confessore G, Galiano G, Stecca G (2008) An evolutionary algorithm for vehicle routing problem with real life constraints. In: The 41st CIRP conference on manufacturing systems, TokyoGoogle Scholar
- Cordeau J-F, Laporte G (2001) A tabu search algorithm for the site dependent vehicle routing problem with time windows. INFOR 39:292–298Google Scholar
- Cordeau J-F, Desaulniers G, Desrosiers J, Solomon MM, Soumis F (2002) The vehicle routing problem. In: Toth P, Vigo D (eds) Volume 9 of SIAM monographs on discrete mathematics and applications 2002, Chap. 7, 157193. SIAM, PhilidelphiaGoogle Scholar
- Gosman C, Cornea T, Dobre C, Pop F, Castiglione A (2016) Putting the user in control of the intelligent transportation system. In: Lecture notes in computer science vol 9722, pp 231–246Google Scholar
- Govindam K, Sarkis J, Chiappetta Jabbour CJ, Zhu K, Geng Y (2014) Eco-efficiency based green supply chain management: current status and opportunities. Eur J Oper Res 2014(233):293–298Google Scholar
- Negru C, Mocanu M, Chiru C, Draghia A, Drobot R (2015) Cost efficient cloud-based service oriented architecture for water pollution prediction. In: Intelligent computer communication and processing (ICCP), pp 417-423Google Scholar
- Sniezek J, Bodin L, Levy L, Ball M (2001) Capacitated Arc routing problem with vehicle-site dependencies: the Philadelphia experience. In: Toth P, Vigo D (eds) The vehicle routing problem, pp 287-308Google Scholar
- Simchi-Levi D, Wu DS, Shen ZJ (eds) (2004) Handbook of quantitative supply chain analysis: modeling in the E-business Era. Kluwer, BostonGoogle Scholar
- Toth P, Vigo D (eds) (2015) Vehicle routing: problems, methods, and applications. Society for Industrial and Applied Mathematics, PennsylvaniaGoogle Scholar
- Yusuf I (2014) Solving multi-depot, heterogeneous, site dependent and asymmetric VRP using three steps heuristic. J Algorithms Optim 2:28–42Google Scholar