TY  - JOUR
T1  - Locating Nearby Delivery Zones in an Urban Logistics Context
AU - Ahmed, Miloudi AU - Btissam, Dkhissi AU - Fellahi Abdelilah, El 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 22
SP  - 8243
EP  - 8253
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8243.8253
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8243.8253
KW  - Genetic algorithm
KW  -AP
KW  -optimization
KW  -operations research
KW  -metaheuristics
KW  -Logistics
AB  - In logistics engineering, the client satisfaction is one of the most important stakes that a provider has
to overcome. Thus, many research focused on developing the tools required to guarantee the client satisfaction
taking into consideration the optimization of the costs. In this research and in addition to that two concerns
(client satisfaction and costs optimization), we will examine establishing new nearby delivery zones close to
major retail and commercial precincts in a socio-environmental context. Two alternatives is then available,
modeling the real life problem as multiple depot vehicle routing problem or using the uncapacited sing hub
location problem. In this study, we showed that the second alternative stands good than the first. Then, nearby
delivery zones would be implemented in cities. These delivery zones will occupy sections of curbsides space
and alleys to provide space for carriers to park their delivery vehicles in a pre-booked space where they can
load/unload products for the delivery/pickup to neighboring businesses walking or using rolling carts. Hence,
carriers will avoid time windows restriction by making a single long stop in the nearby delivery zone instead
of driving around the city center from one destination to another. In this study, we provide a mathematical
formulation to this end based on the uncapacitated single allocation p-hub location problem, also, a Genetic
algorithm approach is presented. The performance of the model and the metaheuristic was tested using the
Australian Post (AP) data set.
ER  -