The topic of crowdsourcing has gained popularity in the last decade. Therefore, there is a large amount of literature examining the issue. Crowdlogistics and especially concrete solutions regarding designing of a system that can be used for simulation and testing are, on the other hand far less covered by research and literature. One reason for that
is that the use of crowd for delivery of items has been in place for a short time only which can be perceivable from the fact that literature reviewed in this section hails from the time span of the last few years.
In order for crowdsourcing to be a successful enterprise, a critically large number of participants is needed (Agatz, Erera, Savelsbergh, & Wang, 2011). It takes substantial time to be accepted by wider population and create a critical mass of customers. Even then, crowdsourced last-mile delivery needs to be supported by backup delivery vehicles in order to guarantee a certain level of service and cover all requests. In the crowddelivery concept, arrived delivery request is to be assigned or matched to a suitable crowd carrier from a pool of available crowdsources, with regard to predefined requirements, like, e.g., planned route, weight of the order, distance length. For the matching process an algorithm is usually constructed, in which case process is done automatically by the platform. If there is no interested individual for a delivery request or a successful match between an order request and crowd carrier cannot be made, then the task must be carried out by a backup vehicle. Therefore, construction of a crowddelivery solution usually consists of two stages:
i. matching of orders to in-store customers and
ii. routes generation for in-house vehicles and eventually for crowd carriers, in case of a single crowdsource delivering multiple orders per route.
The application of such two-stage method where a matching is performed first followed by route generation can be seen in Arslan, Agatz, Kroon, & Zuidwijk (2016); Dayarian &
Savelsbergh (2017). For assigning of orders to customers Rougès and Montreuil, (2014) recommend to use matching algorithms, whereas for routing part different extensions of VRP, TSP and PDP models are applied depending on the problem setting.
This study considers delivery from the local shop, located in an urban area, and assumes that customers visiting the giving store are willing to serve as crowd carriers in their available time and with respect to a specified time window for delivery. To solve our problem a static and deterministic model is constructed, where all information about crowdsource arrivals and transportation requests are known in advance. As a reference
for construction of the model we used the static variant designed by Dayarian &
Savelsbergh (2017) and the static variant from Arslan et al. (2016), which they used to get reference values for comparison with the dynamic variant. Real crowd concepts are highly dynamic, hence requests and crowdsources are arriving randomly throughout the day. However, we believe that a static solution is a good method for a simulation and examination, since Arslan et a.l (2016); Dayarian & Savelsbergh (2017); Kafle et al.
(2017); all used a static version as a benchmark to validate constructed dynamic algorithms for large-scale problems. Whereas, Archetti et al. (2016) in their research considered solely a static problem, where all information about customers is known in advance.
In the first stage of our model, delivery requests are assigned to crowd carriers and for that a matching algorithm is constructed. Our problem is only local, limited to a 4𝑘𝑚2 city area with a store in the center and pedestrians as crowd carriers. Kafle et al. (2017) also used walking, besides cycling as a mode of transportation, whereas P. Chen &
Chankov (2017) opted for all three transportation modes, walking, cycling and driving.
Walking as a mean of transportation has certain limitations: the distance and the weight of a package pedestrian is willing to accept. We also decided that a crowd carrier can only perform one task per delivery route, which is also implemented by P. Chen &
Chankov (2017); Dayarian & Savelsbergh (2017); and Setzke et al. (2017). Furthermore, carrier is available at certain point in time and every delivery request has its time window for delivery. Constructed matching algorithm takes all mentioned limitations in consideration, distance, weight, time windows and one task restriction; and conducts matching with a goal to assign as many orders as possible, whereby delivery requests with a maximum distance from the store are prioritized. The most similar matching solution is the one presented by Dayarian & Savelsbergh (2017) with not only the longest distances being prioritized, but also tasks which are most urgent.
After all possible delivery requests are assigned to crowd carriers, unmatched requests need to be delivered with backup vehicles and for this issue a model for the routing problem is created. According to Wang and Kopfer (2015) the type of mathematical model that can be used to solve a routing problem is determined by the following facts:
• if a single or more vehicles are considered,
• if vehicle capacities are limited,
• by the type of customer requests (pickup, delivery or both),
• if are pickup or delivery regarding each request are directly connected to the depot.
A single, uncapacitated vehicle is used in TSP, where all customers are visited in one route. The case where pickup and delivery requests are allowed in between customers is PDP. Whereas VRP can only have set of deliveries which originates from a depot or set of pickups that need to be delivered to a depot. A special case of VRP is Vehicle Routing Problem with Backhauls (VRPB) with both pickup and delivery requests, however first delivery requests are served following with pickups. Keeping in mind that crowdsources are not included in regard to the route generation in the second stage, our problem belongs to classic Capacitated Vehicle Routing Problem (CVRP) (Archetti et al., 2016). More than that, time windows are also employed and thus the problem is an extension of the CVRP called Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). Most similar to our model is the one proposed by Dayarian and Savelsbergh (2017) using calculations only for in-house vehicles in the second stage. They constructed a Multi-Trip Vehicle Routing Problem with Release and Due time (MTVRP-RD). Ready and due times serve almost the same as time windows, where ready times represent release times, hence earliest time when vehicle can start a route, whereas due times have the same function as delivery end time.
Theoretically, our proposed two-stage solution is not mathematically optimal, since two options, crowd carriers and in-house delivery are not considered simultaneously. The ideal model would calculate routes for crowd carriers parallel with vehicle routes to find an optimum. However, in solving our problem we were led by the assumption that using crowd as carriers is always a cheaper option than using an in-house employee and own vehicle. For that reason, our solution prioritizes the use of a crowd over in-house delivery and utilize as much as possible crowdsources with respect to given limitations mentioned in the previous paragraph. The same principle is applied in the study done by Dayarian & Savelsbergh (2017). For the rest of orders, that are unassigned and need to be delivered by the in-house vehicles, the model is constructed and solves a VRPTW to the optimum.
Lastly, a simulation is conducted to test what kind of an effect, in terms of distances travelled by carrier, a delivery incorporating crowd has in comparison to a pure in-house delivery. Similar as in P. Chen & Chankov (2017) the following Key Performance Indicators (KPI) have been applied: Crowddelivery Service Level that indicates portion of delivery requests conducted by crowdsources; and Crowd Utilization or portion of crowdsources successfully matched with a delivery request. Furthermore, carry-weight and willingness to travel a certain distance of a crowdsource as parameters have been adjusted in order to measure the grade of their influence exercised on the overall problem.