Inspired by the retail industry, we introduce a fundamentally new approach towards stochastic inventory routing by replenishing retailers from a central warehouse using a time-based shipment consolidation policy. Such a time-based dispatching policy, where retailers facing stochastic demand are repetitively replenished at fixed times, is essential in practice. It allows for easy incorporation with dependent … Read more
We introduce a learning based algorithm to solve the drone routing problem with recharging stops that arises in many applications such as precision agriculture, search and rescue and military surveillance. The heuristic algorithm, namely Learn and Fly (L\&F), learns from the features of high quality solutions to optimize recharging visits, starting from a given Hamiltonian … Read more
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 7.0 of the SCIP Optimization Suite. The new version features the parallel presolving library PaPILO as a new addition to the suite. PaPILO 1.0 simplifies … Read more
Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiveness of randomization in interdiction games with an interdictor that is both risk and ambiguity averse, we introduce a distributionally robust network interdiction game where the interdictor randomizes over the … Read more
This paper proposes an optimization framework for retailers that are involved in demand response (DR) programs. In a first phase responsive users optimize their own household consumption, characterizing not only their appliances and equipment but also their comfort preferences. Then, the retailer exploits in a second phase this preliminary non-coordinated solution to implement a strategy … Read more
The establishment of kidney exchange programs has dramatically improved rates for kidney transplants by matching donors to compatible patients who would otherwise fail to receive a kidney for transplant. Rather than simply swapping kidneys between two patient-donor pairs, having multiple patient-donors pairs simultaneously donate kidneys in a cyclic manner enables all participants to receive a … Read more
There are currently two broad strategies for optimal Multi-agent Pathfinding (MAPF): (1) search-based methods, which model and solve MAPF directly, and (2) compilation-based solvers, which reduce MAPF to instances of well-known combinatorial problems, and thus, can benefit from advances in solver techniques. In this work, we present an optimal algorithm, BCP, that hybridizes both approaches … Read more
In this paper, we study a class of two-stage robust binary optimization problems with objective uncertainty where recourse decisions are restricted to be mixed-binary. For these problems, we present a deterministic equivalent formulation through the convexification of the recourse feasible region. We then explore this formulation under the lens of a relaxation, showing that the … Read more
This paper proposes an optimization framework for retailers that are involved in demand response (DR) programs. In a first phase responsive users optimize their own household consumption, characterizing not only their smart home components but also their comfort preferences. Then, the retailer exploits in a second phase this preliminary non-coordinated solution to implement a peak … Read more
We consider the problem of routing a fleet of feeders for civil air-to-air refueling operations. In the air-to-air refueling problem, a fixed set of cruisers requires refueling by a fleet of feeders at fixed locations and fixed points in time. A typical objective function is to minimize the fuel consumption or the total number of … Read more