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- The probabilistic travelling salesman problem with crowdsourcingPublication . Santini, Alberto; Viana, Ana; Klimentova, Xenia; Pedroso, João PedroWe study a variant of the Probabilistic Travelling Salesman Problem arising when retailers crowdsource last-mile deliveries to their own customers, who can refuse or accept in exchange for a reward. A planner must identify which deliveries to offer, knowing that all deliveries need fulfilment, either via crowdsourcing or using the retailer’s own vehicle. We formalise the problem and position it in both the literature about crowdsourcing and among routing problems in which not all customers need a visit. We show that to evaluate the objective function of this stochastic problem for even one solution, one needs to solve an exponential number of Travelling Salesman Problems. To address this complexity, we propose Machine Learning and Monte Carlo simulation methods to approximate the objective function, and both a branch-and-bound algorithm and heuristics to reduce the number of evaluations. We show that these approaches work well on small size instances and derive managerial insights on the economic and environmental benefits of crowdsourcing to customers.
- 2-echelon lastmile delivery with lockers and occasional couriersPublication . Santos, André Gustavo dos; Viana, Ana; Pedroso, João PedroWe propose a new approach for the lastmile delivery problem where, besides serving as collecting points of orders for customers, parcel lockers are also used as transshipment nodes in a 2-echelon delivery system. Moreover, we consider that a customer (occasional courier) visiting a locker may accept a compensation to make a delivery to another customer on their regular traveling path. The proposed shared use of the locker facilities – by customers that prefer to self-pick up their orders, and also as a transfer deposit for customers that prefer home delivery – will contribute to better usage of an already available storage capacity. Furthermore, the use of occasional couriers (OCs) brings an extra layer of flexibility to the delivery process and may positively contribute to achieving some environmental goals: although non-consolidation of deliveries may, at first sight, seem negative, by only considering OCs that would go to the locker independently of making or not a delivery on their way home, and their selection being constrained by a maximum detour, the carbon footprint can be potentially reduced when compared to that of dedicated vehicles. We present a mixed-integer linear programming formulation for the problem that integrates three delivery options – depot to locker, depot to locker followed by final delivery by a professional fleet, and depot to locker followed by final delivery by an OC. Furthermore, to assess the impact of OCs’ no show on the delivery process, we extend the formulation to re-schedule the delivery of previous undelivered parcels, and analyze the impact of different no-show rates. Thorough computational experiments show that the use of OCs has a positive impact both on the delivery cost and on the total distance traveled by the dedicated fleets. Experiments also show that the negative impact of no-shows may be reduced by using lockers with higher capacities.
- Influence of Solution Coding on Solving the Unit Commitment Problem with MetaheuristicsPublication . Viana, Ana; Sousa, Jorge P.; Matos, Manuel A.[Introduction excerpt] In Power Systems Engineering, as well as in many other areas of Electrical Engineering, optimising the usage of resources, subject to several constraints, is extremely important and usually difficult, due to specific technical, economical, or other particular aspects of the problem. The unit commitment problem is an important scheduling problem in planning the operation of power generators, and it is a good example of the interest devoted by the Power Engineering sector, to this area of research. It is the on/off decision problem of selecting the power generating units to be in service within a given planning horizon (from 1 day to 2 weeks, generally split in periods of 1 hour) and for how long. The committed units must satisfy the forecasted system load and reserve requirements, at minimum operating cost, subject to a large set of other system, technological and environmental constraints. Due to important start-up and shut-down costs, the problem is in general very hard to solve, as it is not possible to perform a separate optimization for each time interval. Furthermore, given that the operating costs depend on the load assigned to each generator, the problem of committing units over the planning horizon is directly connected to the additional problem of (roughly) assigning the load demand to the on-line units (“pre-dispatch” problem). The final, actual assignment is done later, for a much shorter period (usually from 15 to 60 minutes).