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In high-speed hyperconnected parcel logistics, intermediate hubs play a critical role in reaching economies of scale through consolidating disperse flows of goods. However, resorting small-size parcels at every hub is resource-intensive and can increase hubs' workload and parcels' total travel time. Such resorting can be reduced by smart containerized consolidation, encapsulating together parcels sharing service level and a subsequent destination. In this study, we introduce an optimization model enabling to assess the impact of such consolidation in reducing the expected total pickup-to-delivery times over a parcel logistic network and its consequences on urban logistic operations sustainability. We provide empirical results for a synthetic urban environment, contrasting consolidation performance over different operational and tactical capabilities, network configurations and demand patterns.

The Port of Newcastle features three coal export terminals, operating primarily in cargo assembly mode, that share a rail network on their inbound side, and a channel on their outbound side. Maximising throughput at a single coal terminal, taking into account its layout, its equipment, and its operating policies, is already challenging, but maximising throughput of the Hunter Valley coal export system as a whole requires that terminals and inbound and outbound shared resources be considered simultaneously. Existing approaches to do so either lack realism or are too computationally demanding to be useful as an everyday planning tool. We present a parallel genetic algorithm to optimise the integrated system. The algorithm models activities in continuous time, can handle practical planning horizons efficiently, and generates solutions that match or improve solutions obtained with the state-of-the-art solvers, whilst vastly outperforming them both in memory usage and running time.

Disease management programs have emerged as a cost-effective approach to treat chronic diseases. Appointment adherence is critical to the success of such programs; missed appointment are costly, resulting in reduced resource utilization and worsening of patients' health states. The time of an appointment is one of the factors that impacts adherence. We investigate the benefits, in terms of improved adherence, of incorporating patients' time-of-day preferences during appointment schedule creation and, thus, ultimately, on population health outcomes. Through an extensive computational study, we demonstrate, more generally, the usefulness of patient stratification in appointment scheduling in the environment that motivates our research, an asthma management program offered in Chicago. We find that capturing patient characteristics in appointment scheduling, especially their time preferences, leads to substantial improvements in community health outcomes. We also identify settings in which simple, easy-to-use policies can produce schedules that are comparable in quality to those obtained with an optimization-based approach.

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm., Comment: 26 pages

We study an incremental network design problem, where in each time period of the planning horizon an arc can be added to the network and a maximum flow problem is solved, and where the objective is to maximize the cumulative flow over the entire planning horizon. After presenting two mixed integer programming (MIP) formulations for this NP-complete problem, we describe several heuristics and prove performance bounds for some special cases. In a series of computational experiments, we compare the performance of the MIP formulations as well as the heuristics., Comment: 26 pages

Given an edge-weighted graph $G=(V,E)$ and a set $E_0\subset E$, the incremental network design problem with minimum spanning trees asks for a sequence of edges $e'_1,\ldots,e'_T\in E\setminus E_0$ minimizing $\sum_{t=1}^Tw(X_t)$ where $w(X_t)$ is the weight of a minimum spanning tree $X_t$ for the subgraph $(V,E_0\cup\{e'_1,\ldots,e'_t\})$ and $T=\lvert E\setminus E_0\rvert$. We prove that this problem can be solved by a greedy algorithm., Comment: 9 pages, minor revision based on reviewer comments