1.1 Sustainable Last Mile Delivery
Even though passenger mobility has received considerable attention in the literature
and in practice in the recent years (Gentile and Noekel 2016; Alonso-Mora et al.
2017), other contributions to new research and technology are found for the modeling of last mile delivery within urban areas. Applications can be found in different
industries to tackle the issues around last mile delivery in urban areas. To name a
few, we can mention DPD (https://www.dpd.com/), Green Link (http://green-link.
co.uk) or self-service parcel stations from DHL Packstation, LaPoste Pickup
Station, etc.
The most commonly used vehicles for deliveries in the last mile delivery (including the request made via online shopping) are vans or trucks. The increase in
e-commerce and related deliveries in cities is contributing to the increase in van
traffic resulting in more pollution. For example, in the UK, these vehicles are
responsible for 15% of total kilometers traveled on roads in 2015 compared to
10% in 1993 (Bates et al. 2018). In addition, these vehicles have contributed in
13.3 million tonnes of CO 2 equivalent to emissions in 2014 (Zanni and Bristow
2010). In this research, we focus on two aspects of the urban logistics systems in
order to reduce the number of necessary vehicles and kilometers traveled by them in
the network. In addition, we aim at shed light on how the available space inside the
vehicles can be used more efficiently to avoid circulating empty vehicles on roads.
Both topics are defined within the framework of Physical Internet.
1.2 Last Mile Delivery and Bin Packing Problem
The last mile delivery problem has been recognized as one of the most expensive,
least efficient and one of the main responsible to polluting inside the supply chain
networks. In urban areas, traffic infrastructure is used for the purpose of delivering
goods that results in traffic jams (Ehmke 2012). Not having a good planning system
for the last mile delivery causes heavier traffic that affects service quality and the
final cost (Eglese 2006). The body of literature is quite rich when it comes to the last
mile delivery. Here, we briefly mention the most relevant papers to our work.
In Gendreau et al. (2006), the authors propose a Tabu search in order to solve the
vehicle routing problem with capacity and route length restrictions. The Tabu search
consists of examining successive neighbors of a solution and selects the best. The
authors use a generalized insertion procedure that repeatedly removes a vertex
(which represents a customer) from its current route and reinsert it into another
route. This is the neighborhood of a solution. In order to avoid cycling, solutions that
were recently examined are forbidden and inserted in a constantly updated Tabu list.
In Bortfeldt (2012), the author presents a hybrid algorithm for the threedimensional loading capacitated vehicle routing problem. It includes a Tabu search
algorithm for the routing part and a tree search algorithm for packing boxes into
3 The Impact of Collaborative Scheduling and Routing for Interconnected. . .
37
Even though passenger mobility has received considerable attention in the literature
and in practice in the recent years (Gentile and Noekel 2016; Alonso-Mora et al.
2017), other contributions to new research and technology are found for the modeling of last mile delivery within urban areas. Applications can be found in different
industries to tackle the issues around last mile delivery in urban areas. To name a
few, we can mention DPD (https://www.dpd.com/), Green Link (http://green-link.
co.uk) or self-service parcel stations from DHL Packstation, LaPoste Pickup
Station, etc.
The most commonly used vehicles for deliveries in the last mile delivery (including the request made via online shopping) are vans or trucks. The increase in
e-commerce and related deliveries in cities is contributing to the increase in van
traffic resulting in more pollution. For example, in the UK, these vehicles are
responsible for 15% of total kilometers traveled on roads in 2015 compared to
10% in 1993 (Bates et al. 2018). In addition, these vehicles have contributed in
13.3 million tonnes of CO 2 equivalent to emissions in 2014 (Zanni and Bristow
2010). In this research, we focus on two aspects of the urban logistics systems in
order to reduce the number of necessary vehicles and kilometers traveled by them in
the network. In addition, we aim at shed light on how the available space inside the
vehicles can be used more efficiently to avoid circulating empty vehicles on roads.
Both topics are defined within the framework of Physical Internet.
1.2 Last Mile Delivery and Bin Packing Problem
The last mile delivery problem has been recognized as one of the most expensive,
least efficient and one of the main responsible to polluting inside the supply chain
networks. In urban areas, traffic infrastructure is used for the purpose of delivering
goods that results in traffic jams (Ehmke 2012). Not having a good planning system
for the last mile delivery causes heavier traffic that affects service quality and the
final cost (Eglese 2006). The body of literature is quite rich when it comes to the last
mile delivery. Here, we briefly mention the most relevant papers to our work.
In Gendreau et al. (2006), the authors propose a Tabu search in order to solve the
vehicle routing problem with capacity and route length restrictions. The Tabu search
consists of examining successive neighbors of a solution and selects the best. The
authors use a generalized insertion procedure that repeatedly removes a vertex
(which represents a customer) from its current route and reinsert it into another
route. This is the neighborhood of a solution. In order to avoid cycling, solutions that
were recently examined are forbidden and inserted in a constantly updated Tabu list.
In Bortfeldt (2012), the author presents a hybrid algorithm for the threedimensional loading capacitated vehicle routing problem. It includes a Tabu search
algorithm for the routing part and a tree search algorithm for packing boxes into
3 The Impact of Collaborative Scheduling and Routing for Interconnected. . .
37