Bottleneck Identification in Resource-Constrained Project Scheduling via Constraint Relaxation
- URL: http://arxiv.org/abs/2504.07495v1
- Date: Thu, 10 Apr 2025 06:53:10 GMT
- Title: Bottleneck Identification in Resource-Constrained Project Scheduling via Constraint Relaxation
- Abstract summary: We aim to automatically identify bottlenecks in the given schedule while linking them to the particular constraints to be relaxed.<n>The first method adapts existing approaches from the job shop literature and utilizes them for so-called untargeted relaxations.<n>The second method identifies potential improvements in relaxed versions of the problem and proposes targeted relaxations.
- Score: 0.4143603294943439
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In realistic production scenarios, Advanced Planning and Scheduling (APS) tools often require manual intervention by production planners, as the system works with incomplete information, resulting in suboptimal schedules. Often, the preferable solution is not found just because of the too-restrictive constraints specifying the optimization problem, representing bottlenecks in the schedule. To provide computer-assisted support for decision-making, we aim to automatically identify bottlenecks in the given schedule while linking them to the particular constraints to be relaxed. In this work, we address the problem of reducing the tardiness of a particular project in an obtained schedule in the resource-constrained project scheduling problem by relaxing constraints related to identified bottlenecks. We develop two methods for this purpose. The first method adapts existing approaches from the job shop literature and utilizes them for so-called untargeted relaxations. The second method identifies potential improvements in relaxed versions of the problem and proposes targeted relaxations. Surprisingly, the untargeted relaxations result in improvements comparable to the targeted relaxations.
Related papers
- A Temporal Planning Approach for Intelligent Flood Response [0.0]
This work presents an intelligent flood-response framework that exploits temporal planning and models the complete operational life cycle of flood response.<n>The framework incorporates priority-driven triage, route accessibility and travel costs, resource allocation, and supply management.
arXiv Detail & Related papers (2026-08-20T19:01:57Z) - PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models [52.48858778580074]
Planning is a fundamental capability for large language models (LLMs)<n>PlanningBench is a framework for generating scalable, diverse, verifiable planning data for both evaluation and training.
arXiv Detail & Related papers (2026-05-20T08:10:15Z) - TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals [23.424632543315795]
Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics.<n>Traditionalf task planning approaches often transform the temporal planning problem into a classical planning problem with reachability goals.<n>We introduce TIDE, a novel approach that addresses this limitation by decomposing a temporal problem into a sequence of smaller, manageable reach sub-problems.
arXiv Detail & Related papers (2026-01-17T19:07:03Z) - Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning [52.03884701766989]
offline reinforcement learning (RL) algorithms typically impose constraints on action selection.<n>We propose a new neighborhood constraint that restricts action selection in the Bellman target to the union of neighborhoods of dataset actions.<n>We develop a simple yet effective algorithm, Adaptive Neighborhood-constrained Q learning (ANQ), to perform Q learning with target actions satisfying this constraint.
arXiv Detail & Related papers (2025-11-04T13:42:05Z) - Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration [11.232441969983672]
This work is to perform capacity planning, estimate resource requirements, and job scheduling for on-prem grid computing environments.<n>A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs.<n>We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming.
arXiv Detail & Related papers (2025-07-01T22:56:08Z) - No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! [56.80767500991973]
We focus on two canonical settings: $(i)$ online resource allocation where rewards and costs are observed before action selection, and $(ii)$ online learning with resource constraints where they are observed after action selection, under full feedback or bandit feedback.<n>It is well known that achieving sublinear regret in these settings is impossible when reward and cost distributions may change arbitrarily over time.<n>We design general (primal-)dual methods that achieve sublinear regret with respect to baselines that follow the spending plan. Crucially, the performance of our algorithms improves when the spending plan ensures a well-balanced distribution of the budget
arXiv Detail & Related papers (2025-06-16T08:42:31Z) - Semantic Scheduling for LLM Inference [48.19648297172146]
We introduce the concept of semantic scheduling in scheduling of requests from large language models (LLM)<n>We present a novel scheduling algorithm with optimal time complexity, designed to minimize the overall waiting time in LLM-based prompt scheduling.
arXiv Detail & Related papers (2025-06-13T20:15:58Z) - Machine Learning and Constraint Programming for Efficient Healthcare Scheduling [0.8287206589886879]
We tackle the Nurse Scheduling Problem (NSP)
In the implicit solving approach, we rely on Machine Learning methods using historical data to learn and generate new solutions through the constraints and objectives that may be embedded in the learned patterns.
To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an explicit approach where we first model the NSP using the Constraint Satisfaction Problem framework.
Since our implicit approach may not guarantee the feasibility or optimality of the generated solution, we propose a data-driven approach to passively learn the NSP as a constraint
arXiv Detail & Related papers (2024-09-11T18:09:25Z) - The Road Less Scheduled [45.01813613035411]
Existing learning rate schedules that do not require specification of the optimization stopping step T are greatly out-performed by learning rate schedules that depend on T.
We propose an approach that avoids the need for this stopping time by eschewing the use of schedules entirely.
Our Schedule-Free approach introduces no additional hyper- parameters over standard schedules with momentum.
arXiv Detail & Related papers (2024-05-24T16:20:46Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - On efficient computation in active inference [1.1470070927586016]
We present a novel planning algorithm for finite temporal horizons with drastically lower computational complexity.
We also simplify the process of setting an appropriate target distribution for new and existing active inference planning schemes.
arXiv Detail & Related papers (2023-07-02T07:38:56Z) - Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints.
Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
arXiv Detail & Related papers (2023-05-02T07:41:16Z) - Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking
Job Shop Problem Using Graph Neural Network and Reinforcement Learning [21.021840570685264]
The interrupting swap-allowed blocking job shop problem (ISBJSSP) is able to model many manufacturing planning and logistics applications realistically.
We introduce a dynamic disjunctive graph formulation characterized by nodes and edges subjected to continuous deletions and additions.
A simulator is developed to simulate interruption, swapping, and blocking in the ISBJSSP setting.
arXiv Detail & Related papers (2023-02-05T23:35:21Z) - A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist
Scheduling Problems [11.66506213335498]
Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices.
We formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL.
We provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem.
arXiv Detail & Related papers (2022-12-11T05:30:44Z) - Continual Test-Time Domain Adaptation [94.51284735268597]
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data.
CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models.
arXiv Detail & Related papers (2022-03-25T11:42:02Z) - Efficient Temporal Piecewise-Linear Numeric Planning with Lazy
Consistency Checking [4.834203844100679]
We propose a set of techniques that allow the planner to compute LP consistency checks lazily where possible.
We also propose an algorithm to perform duration-dependent goal checking more selectively.
The resultant planner is not only more efficient, but outperforms most state-of-the-art temporal-numeric and hybrid planners.
arXiv Detail & Related papers (2021-05-21T07:36:54Z) - CoreDiag: Eliminating Redundancy in Constraint Sets [68.8204255655161]
We present a new algorithm which can be exploited for the determination of minimal cores (minimal non-redundant constraint sets)
The algorithm is especially useful for distributed knowledge engineering scenarios where the degree of redundancy can become high.
In order to show the applicability of our approach, we present an empirical study conducted with commercial configuration knowledge bases.
arXiv Detail & Related papers (2021-02-24T09:16:10Z) - 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.