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The rapid deployment of robotics technologies requires dedicated optimization algorithms to manage large fleets of autonomous agents. This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. The model maximizes throughput, while managing human workload at the workstations and congestion in the facility. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. The algorithm relies on an offline machine learning procedure to predict objective improvements based on subproblem features, and an online optimization model to generate a new subproblem at each iteration. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches. In particular, our solution enhances the utilization of robotic fleets by coordinating robotic tasks for human operators to pick multiple items at once, and by coordinating robotic routes to avoid congestion in the facility.

Transit agencies have the opportunity to outsource certain services to established Mobility-on-Demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes fares and discounts across a multimodal network. We capture commuters' travel decisions with a discrete choice model, resulting in a large-scale, mixed-integer, non-convex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization of fare discounts and passengers' travel decisions in the second stage. To solve the decomposition, we develop a new solution approach combining tailored coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization and warm-start, significantly outperforms algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, while geographically-informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income and long-distance commuters. Our profit allocation mechanism improves outcomes for both types of operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities., Comment: v1 Submitted to Transportation Science 8 Jan 2023. v2 Submitted to Transportation Science 17 March 2024

The Container Relocation Problem (CRP) is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. However, the assumption of knowing the full retrieval order of containers is particularly unrealistic in real operations. This paper studies the stochastic CRP (SCRP), which relaxes this assumption. A new multi-stage stochastic model, called the batch model, is introduced, motivated, and compared with an existing model (the online model). The two main contributions are an optimal algorithm called Pruning-Best-First-Search (PBFS) and a randomized approximate algorithm called PBFS-Approximate with a bounded average error. Both algorithms, applicable in the batch and online models, are based on a new family of lower bounds for which we show some theoretical properties. Moreover, we introduce two new heuristics outperforming the best existing heuristics. Algorithms, bounds and heuristics are tested in an extensive computational section. Finally, based on strong computational evidence, we conjecture the optimality of the "Leveling" heuristic in a special "no information" case, where at any retrieval stage, any of the remaining containers is equally likely to be retrieved next.

The Container Relocation Problem (CRP) is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers respecting a given order of retrieval. While the problem is known to be NP-hard, certain algorithms such as the A* search and heuristics perform reasonably well on many instances of the problem. In this paper, we first focus on the A* search algorithm, and analyze lower and upper bounds that are easy to compute and can be used to prune nodes. Our analysis sheds light on which bounds result in fast computation within a given approximation gap. We present extensive simulation results that improve upon our theoretical analysis, and further show that our method finds the optimum solution on most instances of medium-size bays. On "hard" instances, our method finds an approximate solution with a small gap and within a time frame that is fast for practical applications. We also study the average-case asymptotic behavior of the CRP where the number of columns grows. We calculate the expected number of relocations in the limit, and show that the optimum number of relocations converges to a simple and intuitive lower-bound. We further study the CRP with incomplete information by relaxing the assumption that the order of retrieval of all containers are initially known. This assumption is particularly unrealistic in ports without an appointment system. We assume that the retrieval order of a subset of containers is known initially and the retrieval order of the remaining containers is observed later at a given specific time. Before this time, we assume a probabilistic distribution on the retrieval order of unknown containers. We combine the A* algorithm with sampling technique to solve this two-stage stochastic optimization problem. We show that our algorithm is fast and the error due to sampling and pruning is reasonably small.

We introduce a new model and mathematical formulation for planning crane moves in the storage yard of container terminals. Our objective is to develop a tool that captures customer centric elements, especially service time, and helps operators to manage costly relocation moves. Our model incorporates several practical details and provides port operators with expanded capabilities including planning repositioning moves in off-peak hours, controlling wait times of each customer as well as total service time, optimizing the number of relocations and wait time jointly, and optimizing simultaneously the container stacking and retrieval process. We also study a class of flexible service policies which allow for out-of-order retrieval. We show that under such flexible policies, we can decrease the number of relocations and retrieval delays without creating inequities.