Motor Carrier Service Network Design

This chapter introduces service network design (SND) operations research models and solution methodologies specifically focused on problems that arise in the planning of operations in the trucking, or motor freight, industry. Consolidation carriers such as less-than-truckload and package trucking companies face flow planning problems to decide how to route freight between transfer terminals, and load planning problems to decide how to consolidate shipments into trailerloads and containerloads for dispatch. Integer programming models are introduced for these network design decision problems as well as exact and heuristic solution methods

An Integrated Optimization Framework for Multi-Component Predictive Analytics in Wind Farm Operations & Maintenance

Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. Maintenance decisions identify optimal times to repair every component, which in turn, determine the failure risk of the turbines. More specifically, optimization models that characterize a turbine's failure time as the first time that one of its constituent components fail - a systems reliability concept called competing risk is developed. The resulting turbine failures impact the optimization of wind farm operations and revenue. Extensive experiments conducted for multiple wind farms with 300 wind turbines - 1200 components - showcases the performance of the proposed framework over conventional methods

Sampling Scenario Set Partition Dual Bounds for Multistage Stochastic Programs

We consider multistage stochastic programming problems in which the random parameters have finite support, leading to optimization over a finite scenario set. There has been recent interest in dual bounds for such problems, of two types. One, known as expected group subproblem objective (EGSO) bounds, require solution of a group subproblem, which optimizes over a subset of the scenarios, for all subsets of the scenario set that have a given cardinality. Increasing the subset cardinality in the group subproblem improves bound quality, (EGSO bounds form a hierarchy), but the number of group subproblems required to compute the bound increases very rapidly. Another is based on partitions of the scenario set into subsets. Combining the values of the group subproblems for all subsets in a partition yields a partition bound. In this paper, we consider partitions into subsets of (nearly) equal cardinality. We show that the expected value of the partition bound over all such partitions also forms a hierarchy. To make use of these bounds in practice, we propose random sampling of partitions and suggest two enhancements to the approach: Sampling partitions that align with the multistage scenario tree structure and use of an auxiliary optimization problem to discover new best bounds based on the values of group subproblems already computed. We establish the effectiveness of these ideas with computational experiments on benchmark problems. Finally, we give a heuristic to save computational effort by ceasing computation of a partition partway through if it appears unpromising.

Supply chain and logistics in digital transformation

The aspirations to reduce environmental impact, the ongoing labor shortages, and the expanding possibilities of digital technologies urge logistics companies to reinvent their network designs, their methods of operating, and even their business models. For the logistics sector, we provide an overview of contextual perspectives, mechanisms of change, and outcomes in digitization, digitalization, and digital transformation. Specifically, we evaluate developments and opportunities in the logistics sector from the viewpoint of food distribution in cities. We find that both customers and suppliers in the food supply chain are willing to initiate or join novel logistics concepts that require collaboration with other stakeholders in the supply chain. Notably, we investigate attitudes towards the concept of bundling, where goods of different suppliers are jointly delivered to customers by the same vehicle. Furthermore, we quantitatively demonstrate the added value of such collaborative efforts. Collaborations between companies require an increased effort in digital exchange of data and other digital technologies to reap the potential benefits we determined. We identify an impending transformation in the business model of innovative food distributors that may change the landscape of food distribution in cities

Digital transformation in logistics from the perspective of a food distributor

The aspirations to reduce environmental impact, the ongoing labor shortages, and the expanding possibilities of digital technologies urge logistics companies to reinvent their network designs, their methods of operating, and even their business models. For the logistics sector, we provide an overview of contextual perspectives, mechanisms of change, and outcomes in digitization, digitalization, and digital transformation. Specifically, we evaluate developments and opportunities in the logistics sector from the viewpoint of food distribution in cities. We find that both customers and suppliers in the food supply chain are willing to initiate or join novel logistics concepts that require collaboration with other stakeholders in the supply chain. Notably, we investigate attitudes towards the concept of bundling, where goods of different suppliers are jointly delivered to customers by the same vehicle. Furthermore, we quantitatively demonstrate the added value of such collaborative efforts. Collaborations between companies require an increased effort in digital exchange of data and other digital technologies to reap the potential benefits we determined. We identify an impending transformation in the business model of innovative food distributors that may change the landscape of food distribution in cities

A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Personalized learning is emerging in schools as an alternative to one-size-fits-all education. This study introduces and explores a weekly demand-driven flexible learning activity planning problem of own-pace own-method personalized learning. The introduced problem is a computationally intractable optimization problem involving many decision dimensions and also many soft constraints. We propose batch and decomposition methods to generate good-quality initial solutions and a dynamic Thompson sampling based hyper-heuristic framework, as a local search mechanism, which explores the large solution space of this problem in an integrative way. The characteristics of our test instances comply with average secondary schools in the Netherlands and are based on expert opinions and surveys. The experiments, which benchmark the proposed heuristics against Gurobi MIP solver on small instances, illustrate the computational challenge of this problem numerically. According to our experiments, the batch method seems quicker and also can provide better quality solutions for the instances in which resource levels are not scarce, while the decomposition method seems more suitable in resource scarcity situations. The dynamic Thompson sampling based online learning heuristic selection mechanism is shown to provide significant value to the performance of our hyper-heuristic local search. We also provide some practical insights; our experiments numerically demonstrate the alleviating effects of large school sizes on the challenge of satisfying high-spread learning demands

Dynamic service of geographically dispersed time-sensitive demands

This paper presents a new framework that models the novel dynamic vehicle dispatch problem with holding costs (DVDPHC), which focuses on serving stochastic demands at geographically dispersed locations in a timely manner. This framework is applicable, among others, to the post-disaster ambulance bus routing problem, where an ambulance bus must pick up (urgent) patients at geographically dispersed locations and bring them to a centrally-located hospital as quickly as possible. Solving the DVDPHC requires a dynamic decision-making rule at each decision moment for which demands to serve at the current location, and where to direct the vehicle next. We propose a heuristic based on approximate dynamic programming combined with a neural network (ADP-NN) for effectively solving the DVDPHC. Numerical experiments demonstrate that our proposed method is fast, scalable and robust. Furthermore, it keeps up with computationally heavy direct lookahead (DLA) benchmarks on 120 large representative instances, achieving on average 12.77% total cost improvement. Numerical analysis also reveals that our proposed method exhibits complex self-learned flexible behavior, such as waiting near locations in anticipation of new demand