arXiv subjects

Richard Hartl

Publications and source records attributed to Richard Hartl.

3 recordsLinked to original sources

arXiv2021

An efficient bundle-based approach for the share-a-ride problem

Some of today's most significant challenges in urban environments concern individual mobility and rapid parcel delivery. With the surge of e-commerce and the ever-increasing volume of goods to be handled, new logistic solutions are in high demand. The share-a-ride problem (SARP) was proposed as one such solution, combining people and parcel transportation in taxis. This is an NP-hard problem and thus obtaining optimal solutions can be computationally costly. In this paper, we work with a variation of SARP for ride-hailing systems, which can be formulated as a multi-depot open generalised vehicle routing problem with time windows. We present and solve a mixed-integer linear programming (MILP) formulation for this problem that bundles requests together, and we compare its results to a previously proposed two-stage method. The latter solves the so-called freight insertion problem (FIP) in the second stage, for which we consider two versions, and the problem consists of inserting parcels into predefined passenger routes obtained in the first stage. We tested the methods in three sets of instances. The developed bundle-based approach outperformed both FIP versions in solution quality and in the service of parcels. Our method also compares favourably when it comes to reducing the amount of deadheading distance.

cs.DS↗

arXiv2018

New Exact Algorithm and Solution Properties for the Vehicle Routing Problem with Stochastic Demands

This paper considers the vehicle routing problem with stochastic demands (VRPSD) under optimal restocking. We develop an exact algorithm that is effective for solving instances with many vehicles and few customers per route. In our experiments, we show that in these instances solving the stochastic problem is most relevant (i.e., the potential gains over the deterministic equivalent solution are highest). The proposed branch-price-and-cut algorithm relies on an efficient labeling procedure, exact and heuristic dominance rules, and completion bounds to price profitable columns. Instances with up to 76 nodes could be solved in less than 5 hours, and instances with up to 148 nodes could be solved in long-runs of the algorithm. The experiments also allowed new findings on the problem. Solving the stochastic problem leads to solutions up to 10% superior to the deterministic equivalent solution. When the number of routes is not fixed, the optimal solutions under detour-to-depot and optimal restocking are nearly equivalent. Opening new routes is a good strategy to reduce restocking costs, and in many cases results in solutions with less transportation costs. For the first time, scenarios where the expected demand in a route is allowed to exceed the capacity of the vehicle were also tested, and the results indicate that superior solutions with lower cost and fewer routes exist.

math.OC↗

arXiv2018

Large Neighborhood-Based Metaheuristic and Branch-and-Price for the Pickup and Delivery Problem with Split Loads

We consider the multi-vehicle one-to-one pickup and delivery problem with split loads, a NP-hard problem linked with a variety of applications for bulk product transportation, bike-sharing systems and inventory re-balancing. This problem is notoriously difficult due to the interaction of two challenging vehicle routing attributes, "pickups and deliveries" and "split deliveries". This possibly leads to optimal solutions of a size that grows exponentially with the instance size, containing multiple visits per customer pair, even in the same route. To solve this problem, we propose an iterated local search metaheuristic as well as a branch-and-price algorithm. The core of the metaheuristic consists of a new large neighborhood search, which reduces the problem of finding the best insertion combination of a pickup and delivery pair into a route (with possible splits) to a resource-constrained shortest path and knapsack problem. Similarly, the branch-and-price algorithm uses sophisticated labeling techniques, route relaxations, pre-processing and branching rules for an efficient resolution. Our computational experiments on classical single-vehicle instances demonstrate the excellent performance of the metaheuristic, which produces new best known solutions for 92 out of 93 test instances, and outperforms all previous algorithms. Experimental results on new multi-vehicle instances with distance constraints are also reported. The branch-and-price algorithm produces optimal solutions for instances with up to 20 pickup-and-delivery pairs, and very accurate solutions are found by the metaheuristic.

cs.AI↗