SAMS Subject Classification: 25, 26

The last-mile of a retail supply chain in which customers can place online orders for commodities entails delivering parcels from a brick-and-mortar store to end-user customers. This portion of the supply chain poses challenging logistical problems related to the pursuit of acceptable trade-offs between operational cost minimisation (as a result of eliminating last-mile delivery inefficiencies) and minimising the environmental impact of traditional commodity delivery modes (as a result of CO2 emissions and the use of fossil fuels).

The aim in this poster is to showcase the design of a decision support framework for managing the multi-modal last-mile delivery logistics of a retailer. The working of the framework is based on an agent-based simulation-optimisation model which takes as input

• time-stamped commodity demand volumes for a number of customers pertaining to a single retail store,

• time windows during which the aforementioned demand has to be satisfied at customers,

• details about a set of delivery modes available to service customer demand,

• data describing the fleets of available delivery vehicles for each of the aforementioned modes (including fixed and variable costs, rates of \(\mbox{CO}_2\) emission, vehicle speeds, and vehicle capacities), and

• information related to the delivery transport infrastructure and expected travel times within this infrastructure for each of the aforementioned delivery modes.

The delivery modes include trucks, cars, motorcycles, bicycles, and walkers. The model produces as output an assignment of vehicles of the appropriate transportation modes to service the set of customers in pursuit of the objectives mentioned above. The model also suggests together delivery routes and schedules to be followed by each of the delivery vehicles. The poster includes a demonstration of the working of a computerised instantiation of the framework which is capable of analysing the solutions to the agent-based model formulated over a rolling planning horizon (discretised into multiple planning periods) during which both customers and delivery vehicles are modelled as stochastic agents exhibiting autonomous behaviour (in terms of subjective preferences and the perceived value of time). The instantiation includes a virtual, real-time bidding platform for registering and evaluating delivery alternatives and corresponding occasional driver incentives offered.