Freight consolidation has significant potential to reduce transportation costs and mitigate congestion and pollution. An effective load consolidation plan relies on carefully chosen consolidation points to ensure alignment with existing transportation management processes, such as driver scheduling, personnel planning, and terminal operations. This complexity represents a significant challenge when searching for optimal consolidation strategies. Traditional optimization-based methods provide exact solutions, but their computational complexity makes them impractical for large-scale instances and they fail to leverage historical data. Machine learning-based approaches address these issues but often ignore operational constraints, leading to infeasible consolidation plans. This work proposes SPOT, an end-to-end approach that integrates the benefits of machine learning (ML) and optimization for load consolidation. The ML component plays a key role in the planning phase by identifying the consolidation points through spatio-temporal clustering and constrained frequent itemset mining, while the optimization selects the most costeffective feasible consolidation routes for a given operational day. Extensive experiments conducted on industrial load data demonstrate that SPOT significantly reduces travel distance and transportation costs (by about 50%) on large terminals) compared to the existing industry-standard load planning strategy and a neighborhood-based heuristic. Moreover, the ML component provides valuable tactical-level insights by identifying frequently recurring consolidation opportunities that guide proactive planning. In addition, SPOT is computationally efficient and can be easily scaled to accommodate large transportation networks.

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.

The deliberate contamination of food is well recognized as a major public health threat. A food supply chain offers several possible targets for the intentional insertion of a biological or chemical toxin by a perpetrator, which can result in significant morbidity and mortality. We assume that both manager (defender) of the food production facility and perpetrator (attacker) select actions at each of a possibly countable number of decision epochs, based on possibly inaccurate real-time observations of the other agent. The defender's objectives are to maximize long-run expected total discounted system productivity and to minimize the long-run expected total discounted consequence of an attack. The attacker's objective is to maximize its reward, which combines the long-run expected total discounted consequence of an attack with a penalty if the attack is unsuccessful. We model this problem as a leader-follower, two-agent partially observed Markov game. We show that system risk is dynamic, determine the impact of observation accuracy on facility productivity and risk, thus providing a measure of the value of information, and perform a sensitivity analysis on key parameters. We present an illustrative example involving a liquid egg production system.

Abstract We propose a practical and flexible fixed routing system that preserves many of the benefits of traditional fixed routes but can be deployed in settings with medium to high variability and delivery time window constraints. The key ideas are the introduction of a new recourse strategy, in which customers are assigned to two planned routes, a primary and a backup, and recourse decisions can move customers to backup routes to regain feasibility or improve costs, and the use of sampling‐based techniques to handle the presence of delivery time windows during the construction of primary and backup routes. A computational study based on real‐life data demonstrates the efficacy of the proposed fixed routing system and the route construction techniques. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009