ABSTRACT In response to the increasing volume of parcels, last‐mile delivery innovations are exploring the integration of multiple delivery modes. The most prominent examples are delivery vans that, next to being the base for the delivery tasks of their human drivers, also function as mobile launching platforms for drones or autonomous delivery robots. This paper investigates a novel approach involving the cooperation of a mobile parcel locker, which repositions continuously to facilitate self‐pickup by urban customers, alongside the parallel home‐delivery tasks of its human driver. For a predetermined set of customers, divided into home‐delivery and self‐service categories, we aim to identify a synchronized route for a locker‐driver tandem that minimizes the total delivery duration. Based on a comprehensive analysis of the problem's computational complexity, we develop efficient solution methods for the deterministic version of the problem. Additionally, we address the scenario with stochastic response times of self‐service customers. From a managerial perspective, we examine the service‐cost trade‐off: accommodating convenient pickup times for self‐service customers can disrupt route efficiency, and vice versa. Our findings indicate that effective synchronization of both delivery modes can provide a suitable balance between service quality and operational efficiency.

Operational planning at transshipment nodes is a wide and challenging field of research that covers a vast number of distinct relevant applications, spanning from seaport container terminals to rail terminals to cross-docks. In this work, we study the feasibility version of a fundamental synchronization problem that assigns incoming vehicles to docking resources subject to handover relations. We carry out a comprehensive analysis of computational complexity of various problem variants and establish structural connections to famous decision problems in graph theory. We further propose an exact solution algorithm for finding feasible dock assignments, if vehicles can visit the node only once and evaluate its performance in a comprehensive computational study.

We introduce and investigate the problem of scheduling a single lock with parallel chambers. Special cases of this problem are related to interval scheduling. We focus on the existence of no-wait schedules and characterize their feasibility for a lock consisting of two chambers using new graph-theoretical concepts. We obtain a linear time algorithm for this special case. We also provide an efficient algorithm for the case where all chambers of the lock are identical. Furthermore, we describe a dynamic programming algorithm for the general case with arbitrary chambers. Finally, we indicate how our methods for the no-wait case can be applied to practical settings where waiting time is unavoidable.

Last mile deliveries with unmanned aerial vehicles (also denoted as drones) are seen as one promising idea to reduce excessive road traffic. To overcome the difficulties caused by the comparatively short operating ranges of drones, an innovative concept suggests to apply trucks as mobile landing and take‐off platforms. In this context, the paper on hand schedules the delivery to customers by drones for given truck routes. Given a fixed sequence of stops constituting a truck route and a set of customers to be supplied, we aim at a drone schedule (i.e., a set of trips each defining a drone's take‐off and landing stop and the customer serviced), such that all customers are supplied and the total duration of the delivery tour is minimized. We differentiate whether multiple drones or just a single one are placed on a truck and whether or not take‐off and landing stops have to be identical. We provide an analysis of computational complexity for each resulting subproblem, introduce efficient mixed‐integer programs, and compare all cases with regard to their potential of reducing the delivery effort on the last mile.

We investigate the scheduling of series of consecutive locks. This setting occurs naturally along canals and waterways. We describe a problem that generalizes different models that have been studied in literature. Our contribution is to (i) provide two distinct mathematical programming formulations, and compare them empirically, (ii) show how these models allow for minimizing emission by having the speed of a ship as a decision variable, (iii) to compare, on realistic instances, the optimum solution found by solving the models with the outcome of a decentralized heuristic.

Abstract The paper “Multistage Methods for Freight Train Classification” by Jacob et al. [Networks 57 (2011) 87–105] provides great insight into the theory and practice of sorting procedures at shunting yards. In Jacob et al. [Networks 57 (2011) 87–105] many relevant shunting situations (e.g., single or multiple inbound trains, single or multiple outbound trains, (un)restricted number of tracks, (un)restricted track capacity) are formally specified as optimization problems. Then, for almost all of them either an exact polynomial‐time algorithm or an NP‐hardness proof is provided. However, the case of multiple inbound trains, which is of high practical relevance, is left open. We close this gap by providing a proof of NP‐hardness. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013