Optimizing Agricultural Order Fulfillment Systems: A Hybrid Tree Search Approach

• URL: http://arxiv.org/abs/2407.13968v1
• Date: Fri, 19 Jul 2024 01:25:39 GMT
• Title: Optimizing Agricultural Order Fulfillment Systems: A Hybrid Tree Search Approach
• Authors: Pranay Thangeda, Hoda Helmi, Melkior Ornik,
• Abstract summary: Efficient order fulfillment is vital in the agricultural industry, particularly due to the seasonal nature of seed supply chains. This paper addresses the challenge of optimizing seed orders fulfillment in a centralized warehouse where orders are processed in waves. We propose an adaptive hybrid tree search algorithm that combines Monte Carlo tree search with domain-specific knowledge.
• Score: 1.1470070927586018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient order fulfillment is vital in the agricultural industry, particularly due to the seasonal nature of seed supply chains. This paper addresses the challenge of optimizing seed orders fulfillment in a centralized warehouse where orders are processed in waves, taking into account the unpredictable arrival of seed stocks and strict order deadlines. We model the wave scheduling problem as a Markov decision process and propose an adaptive hybrid tree search algorithm that combines Monte Carlo tree search with domain-specific knowledge to efficiently navigate the complex, dynamic environment of seed distribution. By leveraging historical data and stochastic modeling, our method enables forecast-informed scheduling decisions that balance immediate requirements with long-term operational efficiency. The key idea is that we can augment Monte Carlo tree search algorithm with problem-specific side information that dynamically reduces the number of candidate actions at each decision step to handle the large state and action spaces that render traditional solution methods computationally intractable. Extensive simulations with realistic parameters-including a diverse range of products, a high volume of orders, and authentic seasonal durations-demonstrate that the proposed approach significantly outperforms existing industry standard methods.

Related papers

• LiteSearch: Efficacious Tree Search for LLM [70.29796112457662]
  This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
  arXiv  Detail & Related papers  (2024-06-29T05:14:04Z)
• Online Learning of Decision Trees with Thompson Sampling [12.403737756721467]
  Decision Trees are prominent prediction models for interpretable Machine Learning. We devise a new Monte Carlo Tree Search algorithm, able to produce optimal Decision Trees in an online setting.
  arXiv  Detail & Related papers  (2024-04-09T15:53:02Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling [0.0]
  This article presents a memetic algorithm with applying deep reinforcement learning (DRL) to flexible job shop scheduling problems (DRC-FJSSP) From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration.
  arXiv  Detail & Related papers  (2022-12-21T11:24:32Z)
• Monte Carlo Tree Descent for Black-Box Optimization [10.698553177585973]
  We study how to further integrate sample-based descent for faster optimization. We design novel ways of expanding Monte Carlo search trees, with new descent methods at vertices. We show empirically that the proposed algorithms can outperform state-of-the-art methods on many challenging benchmark problems.
  arXiv  Detail & Related papers  (2022-11-01T22:45:10Z)
• Continuous Monte Carlo Graph Search [61.11769232283621]
  Continuous Monte Carlo Graph Search ( CMCGS) is an extension of Monte Carlo Tree Search (MCTS) to online planning. CMCGS takes advantage of the insight that, during planning, sharing the same action policy between several states can yield high performance. It can be scaled up through parallelization, and it outperforms the Cross-Entropy Method (CEM) in continuous control with learned dynamics models.
  arXiv  Detail & Related papers  (2022-10-04T07:34:06Z)
• Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces [54.58348769621782]
  Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
  arXiv  Detail & Related papers  (2022-07-02T16:59:37Z)
• Bayesian Optimized Monte Carlo Planning [34.8909579244631]
  Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. We present a general method for efficient action sampling based on Bayesian optimization. We implement the proposed approach in a new online tree search algorithm called Bayesian Optimized Monte Carlo Planning.
  arXiv  Detail & Related papers  (2020-10-07T18:29:27Z)
• MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
  We present a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
  arXiv  Detail & Related papers  (2020-07-24T17:06:55Z)
• Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning [2.4565068569913384]
  We present a Deep Reinforcement Learning (DRL) approach for deciding how and when orders should be batched and picked in a warehouse to minimize the number of tardy orders. In particular, the technique facilitates making decisions on whether an order should be picked individually (pick-by-order) or picked in a batch with other orders (pick-by-batch) and if so with which other orders.
  arXiv  Detail & Related papers  (2020-06-16T20:40:41Z)
• Planning in Markov Decision Processes with Gap-Dependent Sample Complexity [48.98199700043158]
  We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process. We prove an upper bound on the number of calls to the generative models needed for MDP-GapE to identify a near-optimal action with high probability.
  arXiv  Detail & Related papers  (2020-06-10T15:05:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.