Assignment-Routing Optimization: Solvers for Problems Under Constraints

• URL: http://arxiv.org/abs/2512.18618v1
• Date: Sun, 21 Dec 2025 06:32:31 GMT
• Title: Assignment-Routing Optimization: Solvers for Problems Under Constraints
• Authors: Yuan Qilong, Michal Pavelka,
• Abstract summary: We study the Joint Routing-Assignment (JRA) problem in which items must be assigned one-to-one to placeholders.<n>We develop a solver tailored for practical packaging-planning scenarios with richer constraints.<n>Results highlight the practical applicability of MIP-based JRA optimization for robotic packaging, motion planning, and complex logistics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the Joint Routing-Assignment (JRA) problem in which items must be assigned one-to-one to placeholders while simultaneously determining a Hamiltonian cycle visiting all nodes exactly once. Extending previous exact MIP solvers with Gurobi and cutting-plane subtour elimination, we develop a solver tailored for practical packaging-planning scenarios with richer constraints.These include multiple placeholder options, time-frame restrictions, and multi-class item packaging. Experiments on 46 mobile manipulation datasets demonstrate that the proposed MIP approach achieves global optima with stable and low computation times, significantly outperforming the shaking-based exact solver by up to an orders of magnitude. Compared to greedy baselines, the MIP solutions achieve consistent optimal distances with an average deviation of 14% for simple heuristics, confirming both efficiency and solution quality. The results highlight the practical applicability of MIP-based JRA optimization for robotic packaging, motion planning, and complex logistics .

Related papers

• Applying a Random-Key Optimizer on Mixed Integer Programs [0.36700088931938835]
  Mixed-Integer Programs (MIPs) are NP-hard optimization models that arise in a broad range of decision-making applications.<n>This paper explores the use of the Random-Key integer (RKO) framework as a flexible, metaheuristic alternative for computing high-quality solutions to MIPs.
  arXiv  Detail & Related papers  (2026-02-25T18:20:03Z)
• An Efficient and Almost Optimal Solver for the Joint Routing-Assignment Problem via Partial JRA and Large-α Optimization [0.0]
  The Joint Routing-Assignment (JRA) optimization problem determines the assignment of items to placeholders and a Hamiltonian cycle that visits each node pair exactly once.<n>Previous studies introduced an exact mixed-integer programming (MIP) solver, along with datasets and a Gurobi implementation.<n>This work presents a novel and more efficient approach that attains high-accuracy, near-optimal solutions for large-scale JRA problems.
  arXiv  Detail & Related papers  (2025-11-07T09:54:46Z)
• FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming [52.52020895303244]
  Mixed-Integer Linear Programming (MILP) is a foundational tool for complex decision-making problems.<n>We propose Joint Continuous-Integer Flow for Mixed-Integer Linear Programming (FMIP), which is the first generative framework that models joint distribution of both integer and continuous variables for MILP solutions.<n>FMIP is fully compatible with arbitrary backbone networks and various downstream solvers, making it well-suited for a broad range of real-world MILP applications.
  arXiv  Detail & Related papers  (2025-07-31T10:03:30Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem [13.381261742433367]
  Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important optimization problems.<n>We propose Balans, an adaptive meta-solver for MIPs with online learning capability that does not require any supervision or apriori training.<n>Balans offers significant performance gains over the default MIP solver, is better than committing to any single best neighborhood, and improves over the state-of-the-art large-neighborhood search for MIPs.
  arXiv  Detail & Related papers  (2024-12-18T22:32:13Z)
• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• Sample-Efficient Multi-Agent RL: An Optimization Perspective [103.35353196535544]
  We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. We introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. We show that our algorithm provides comparable sublinear regret to the existing works.
  arXiv  Detail & Related papers  (2023-10-10T01:39:04Z)
• Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes [2.538209532048867]
  The proposed learn-to-perturb (L2P) hyper-heuristic is a multi-neighborhood simulated annealing algorithm. L2P is tested on several real-world mining complexes, with an emphasis on efficiency, robustness, and generalization capacity. Results show a reduction in the number of iterations by 30-50% and in the computational time by 30-45%.
  arXiv  Detail & Related papers  (2022-02-25T18:20:14Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

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.