Paul Bouman

Optimization Approaches for the Traveling Salesman Problem with Drone

The fast and cost-effcient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of a drone to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we formulate this problem as an MIP model and develop several fast route first-cluster second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several artificial instances with different characteristics and sizes. Our numerical analysis shows that substantial savings are possible with this concept in comparison to truck-only delivery.

Recoverable robustness by column generation

Real-life planning problems are often complicated by the occurrence of disturbances, which imply that the original plan cannot be followed anymore and some recovery action must be taken to cope with the disturbance. In such a situation it is worthwhile to arm yourself against common disturbances. Well-known approaches to create plans that take possible, common disturbances into account are robust optimization and stochastic programming. Recently, a new approach has been developed that combines the best of these two: recoverable robustness. In this paper, we apply the technique of column generation to find solutions to recoverable robustness problems. We consider two types of solution approaches: separate recovery and combined recovery. We show our approach on two example problems: the size robust knapsack problem, in which the knapsack size may get reduced, and the demand robust shortest path problem, in which the sink is uncertain and the cost of edges may increase.

The Travelers Route Choice Problem Under Uncertainty: Dominance Relations Between Strategies

In this paper we describe the Traveler’s Route Choice Problem (TRCP). This is the problem of a traveler in a railway system who plans to take the fastest route to a destination but is faced with a disruption of unknown length on this route. In that case, he can wait until the disruption is over or take a detour route as an alternative. Since the duration of the disruption is not known in advance, he is left with a decision problem under uncertainty. In this paper we model the problem and describe the strategies that may be used in such a situation. Instead of finding optimal strategies for a specialized quality measure, we consider dominance relations between strategies and show that dominated strategies are nonoptimal for the common quality measures. We then analyze which strategies for the TRCP are dominated. In general, the set of nondominated strategies is strongly reduced. We also show that, under certain assumptions, only a small set of strategies is nondominated and conclude that in this case the T...

Passenger route choice in case of disruptions

The passengers of railway transport systems occasionally need to deal with disruptions. In such situations, perfect information, for example about the duration of the disruption, is usually not available. As a result, passengers need to make decisions on the continuation of their journey in a highly uncertain context. In this paper, we introduce the passenger waiting problem, which allows us to analyze whether a passenger should wait for a disruption to resolve or start traveling along a detour. We present an implementation of our approach that runs efficiently on modern smartphones and apply it to a number of illustrative examples inspired by realistic situations.

Dynamic programming approaches for the traveling salesman problem with drone

A promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combining a truck and a drone gives rise to a new planning problem that is known as the Traveling Salesman Problem with Drone (TSP-D). This paper presents exact solution approaches for the TSP-D based on dynamic programming and provides an experimental comparison of these approach. Our numerical experiments show that our approach can solve larger problems than the mathematical programming approaches that have been presented in the literature thus far. Moreover, we show that restrictions on the number of locations the truck can visit while the drone is away can help significantly reduce the solution times while having relatively little impact on the overall solution quality.