Urban Parcel Logistics Hub and Network Design: The Impact of Modularity and Hyperconnectivity

This paper examines how exploiting the hyperconnectivity and modularity concepts underpinning the Physical Internet enables the parcel logistics industry to meet the worldwide challenges to efficiently and sustainably offer faster and more precise deliveries across urban agglomerations, notably across the world’s megacities. It emphasizes disruptive transformations of package logistics hubs and networks, such as multi-tier world pixelization, multi-plane parcel logistics web, smart dynamic parcel routing and hub-based consolidation, and modular parcel containerization

Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks

The load planning problem is a critical challenge in service network design for parcel carriers: it decides how many trailers (or loads) to assign for dispatch over time between pairs of terminals. Another key challenge is to determine a flow plan, which specifies how parcel volumes are assigned to planned loads. This paper considers the Dynamic Load Planning Problem (DLPP) that considers both flow and load planning challenges jointly to adjust loads and flows as the demand forecast changes over time before the day of operations. The paper aims at developing a decision-support tool to inform planners making these decisions at terminals across the network. The paper formulates the DLPP as a MIP and shows that it admits a large number of symmetries in a network where each commodity can be routed through primary and alternate paths. As a result, an optimization solver may return fundamentally different solutions to closely related problems, confusing planners and reducing trust in optimization. To remedy this limitation, the paper proposes a Goal-Directed Optimization that eliminates those symmetries by generating optimal solutions staying close to a reference plan. The paper also proposes an optimization proxy to address the computational challenges of the optimization models. The proxy combines a machine learning model and a feasibility restoration model and finds solutions that satisfy real-time constraints imposed by planners-in-the-loop. An extensive computational study on industrial instances shows that the optimization proxy is around 10 times faster than the commercial solver in obtaining the same quality solutions and orders of magnitude faster for generating solutions that are consistent with each other. The proposed approach also demonstrates the benefits of the DLPP for load consolidation, and the significant savings obtained from combining machine learning and optimization

Sort Planning for Express Parcel Delivery Systems

Parcel logistics services play a vital and growing role in economies worldwide, with customers demanding faster delivery of nearly everything to their homes. To move larger volumes more cost effectively, express carriers use sort technologies to consolidate parcels that share similar geographic and service characteristics for reduced per-unit handling and transportation costs. This thesis focuses on an operational planning problem that arises in sort systems operating within parcel transportation networks. In Chapter 2, we develop optimization-based models to generate cost-effective and time-feasible sort plans for two-stage sort systems. A sort plan, in this setting, involves determining first a high-level grouping of parcels into piles which are dispatched over time to secondary sorters; there, each pile's parcels are segregated based on their loading destinations and service requirements for final sort and packing. We explicitly model time deadlines for sorting that enable the parcel carrier to meet on-time delivery service guarantees commonly offered in practice. For tractability, we propose an integer programming formulation for solving sort plan optimization problems that separates first-stage sort decisions from second-stage decisions but, importantly, ensures that the first-stage decision model preserves feasibility for the second-stage operations. This formulation allows a detailed time-space model to be replaced by a much simpler model that can be readily solved exactly for large-scale instances found in practice. We illustrate the proposed modeling approach and its effectiveness using real-world instances obtained from an international express service provider. In Chapter 3, we extend our modeling from Chapter 2 to explicitly incorporate various sources of demand uncertainty commonly faced by parcel carriers. Using practitioner insights and industry data, we propose different uncertainty models that take into account changes in arrival quantities and/or arrival times. We exploit certain problem structures to generate computationally-tractable robust counterparts that solve realistic-sized instances. We demonstrate the computational viability of the proposed models based on industry data and show that high-quality solutions can be obtained in relatively short computation times. We show the value of the proposed robust models in providing hub managers with sort plan alternatives that quantify trade-offs between operational costs and different levels of robustness. In Chapter 4, we study a flexible assignment balancing problem for minimizing workload imbalance across resources in sort systems. The idea is to enable the use of simple and practical recourse strategies that allow sort equipment to be reconfigured once information about the actual demand is revealed. We introduce the stochastic k-adaptable assignment balancing problem that generates k resource configurations apriori with the objective of minimizing the maximum workload allocated to any resource; a critical objective for improving utilization and reducing congestion to meet deadlines. The goal is to enable decision makers to tap into the availability of real-time data and adapt their operations to plans that work best under the realized demand while maintaining a good level of consistency and stability desired in practice. We compare exact and heuristic solution approaches and test them on real data obtained from a large parcel carrier. We show that by allowing up to six configurations, sort systems can achieve around 6% improvement on average over traditional fixed plans, which accounts for an average of 90% of the benefits obtained when using fully dynamic settings; illustrating the benefits of limited adaptability in the context of sort operations.Ph.D

Optimization models for a single-plant District Cooling System

A District Cooling System (DCS) is an interconnected system encompassing a centralized chiller plant, a Thermal Energy Storage (TES) unit, a piping network, and clusters of consumers' buildings. The main function of a DCS is to produce and deliver chilled water to satisfy the cooling demand of a scattered set of buildings. DCSs are recognized to be highly energy efficient, and therefore constitute an environment-friendly alternative to the traditional power-driven air conditioning systems being operated at individual buildings. In this paper, we investigate the optimal design and operation of a DCS so that the total investment and operational costs are minimized. This involves optimizing decisions related to chiller plant capacity, storage tank capacity, piping network size and layout, and quantities to be produced and stored during every period of time. To this end, mixed-integer programming (MIP) models, that explicitly capture the structural aspects as well as both pressure- and temperature-related requirements, are developed and tested. The results of computational experiments that demonstrate the practical effectiveness of the proposed models are also presented. 2015 Elsevier B.V. and Association of European Operational Research Societies(EURO)with in the International Federation of Operational Research Societies(IFORS).All rights reserved.Scopu

Optimal design of a district cooling grid: structure, technology integration, and operation

The concept of a district cooling grid that allows the integration of several cooling technologies with different availability and supply patterns is introduced. An integrated multi-period optimization model for a district cooling grid configuration is developed. An approximate decomposition strategy is proposed to aid in the efficient design of large grids. Results of extensive computational study are provided that demonstrate the solvability of the developed model to optimality and the effectiveness of the proposed decomposition strategy as a viable solution approach. 2018, 2018 Informa UK Limited, trading as Taylor & Francis Group.Scopu

Urban Parcel Logistics Hub and Network Design: The Impact of Modularity and Hyperconnectivity

Poster 2