Cost Optimization in Production Line Using Genetic Algorithm

• URL: http://arxiv.org/abs/2601.00689v1
• Date: Fri, 02 Jan 2026 13:36:42 GMT
• Title: Cost Optimization in Production Line Using Genetic Algorithm
• Authors: Alireza Rezaee,
• Abstract summary: Genetic algorithm (GA) approach to cost-optimal task scheduling in a production line.<n>Experiments on three classes of precedence structures-tightly coupled, loosely coupled, and uncoupled.
• Score: 0.5482532589225553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a genetic algorithm (GA) approach to cost-optimal task scheduling in a production line. The system consists of a set of serial processing tasks, each with a given duration, unit execution cost, and precedence constraints, which must be assigned to an unlimited number of stations subject to a per-station duration bound. The objective is to minimize the total production cost, modeled as a station-wise function of task costs and the duration bound, while strictly satisfying all prerequisite and capacity constraints. Two chromosome encoding strategies are investigated: a station-based representation implemented using the JGAP library with SuperGene validity checks, and a task-based representation in which genes encode station assignments directly. For each encoding, standard GA operators (crossover, mutation, selection, and replacement) are adapted to preserve feasibility and drive the population toward lower-cost schedules. Experimental results on three classes of precedence structures-tightly coupled, loosely coupled, and uncoupled-demonstrate that the task-based encoding yields smoother convergence and more reliable cost minimization than the station-based encoding, particularly when the number of valid schedules is large. The study highlights the advantages of GA over gradient-based and analytical methods for combinatorial scheduling problems, especially in the presence of complex constraints and non-differentiable cost landscapes.

Related papers

• TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs [8.818252253980985]
  TempoNet is a reinforcement learning scheduler that pairs a permutation-invariant Transformer with a deep Q-approximation.<n>A latency-aware sparse attention stack with blockwise top-k selection and locality-sensitive chunking enables global reasoning over unordered task sets.
  arXiv  Detail & Related papers  (2026-02-20T09:56:23Z)
• Resource-Scalable Fully Quantum Metropolis-Hastings for Integer Linear Programming [0.0]
  We introduce a fully quantum Metropolis-Hastings algorithm for Metropolis linear programming.<n>Each walk step is a unitary update that prepares coherent candidate moves.<n>We show that the method provides a coherent, resource-characterized baseline for fully quantum constraint programming.
  arXiv  Detail & Related papers  (2026-02-11T19:01:45Z)
• Queueing-Aware Optimization of Reasoning Tokens for Accuracy-Latency Trade-offs in LLM Servers [4.3400407844814985]
  We consider a single large language model (LLM) server that serves a heterogeneous stream of queries belonging to $N$ distinct task types.<n>For each task type, the server allocates a fixed number of internal thinking tokens, which determines the computational effort devoted to that query.<n>We formulate a constrained optimization problem that maximizes a weighted average accuracy objective penalized by the mean system time.
  arXiv  Detail & Related papers  (2026-01-15T10:47:11Z)
• Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing [53.88774113545582]
  This paper presents a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework.<n>It aims to minimize the system cost, and thus realize an improved trade-off between task consumption and energy consumption.<n>We show that the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task consumption and energy consumption.
  arXiv  Detail & Related papers  (2025-10-23T02:55:40Z)
• Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution [51.79973519845773]
  Real-world image super-resolution (Real-SR) is a challenging problem due to the complex degradation patterns in low-resolution images.<n>We propose an improved paradigm that frames Real-SR as a data-heterogeneous multi-task learning problem.
  arXiv  Detail & Related papers  (2025-06-05T21:40:21Z)
• TRATSS: Transformer-Based Task Scheduling System for Autonomous Vehicles [8.073451025446454]
  We introduce a framework called Transformer-Based Task Scheduling System (TRATSS)<n>TRATSS outputs optimized task scheduling decisions while dynamically adapting to evolving task requirements and resource availability.
  arXiv  Detail & Related papers  (2025-04-07T18:23:13Z)
• PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving [89.60370366013142]
  We propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents.<n>Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms.
  arXiv  Detail & Related papers  (2025-02-22T06:21:56Z)
• Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency [52.60557300927007]
  We present a $textbfMA-OSMA$ algorithm to transfer the discrete submodular problem into a continuous optimization.<n>We also introduce a projection-free $textbfMA-OSEA$ algorithm, which effectively utilizes the KL divergence by mixing a uniform distribution.<n>Our algorithms significantly improve the $(frac11+c)$-approximation provided by the state-of-the-art OSG algorithm.
  arXiv  Detail & Related papers  (2025-02-07T15:57:56Z)
• Autoregressive Policy Optimization for Constrained Allocation Tasks [4.316765170255551]
  We propose a new method for constrained allocation tasks based on an autoregressive process to sequentially sample allocations for each entity. In addition, we introduce a novel de-biasing mechanism to counter the initial bias caused by sequential sampling.
  arXiv  Detail & Related papers  (2024-09-27T13:27:15Z)
• Differentiable Combinatorial Scheduling at Scale [18.09256072039255]
  We propose a differentiable scheduling framework, utilizing Gumbel-Softmax differentiable sampling technique. To encode inequality constraints for scheduling tasks, we introduce textitconstrained Gumbel Trick, which adeptly encodes arbitrary inequality constraints. Our method facilitates an efficient and scalable scheduling via gradient descent without the need for training data.
  arXiv  Detail & Related papers  (2024-06-06T02:09:39Z)
• Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection [4.89253144446913]
  We propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Our experiments show that SAGA can arrive at near-optimal solutions three times faster than a wrapper GA, on average.
  arXiv  Detail & Related papers  (2021-11-17T12:33:18Z)
• Evolutionary Optimization of High-Coverage Budgeted Classifiers [1.7767466724342065]
  Budgeted multi-feature classifiers (MSC) process inputs through a sequence of partial feature acquisition and evaluation steps. This paper proposes a problem-specific MSC that incorporates a terminal reject option for indecisive predictions. The algorithm's design emphasizes efficiency while respecting a notion of aggregated performance via a uniqueization.
  arXiv  Detail & Related papers  (2021-10-25T16:03:07Z)
• 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)
• Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Conditioned Options [54.40780660868349]
  We introduce a compositional framework called Linearly-Solvable Goal Kernel Dynamic Programming (LS-GKDP)<n>LS-GKDP combines the Linearly-Solvable Markov Decision Process (LMDP) formalism with the Options Framework of Reinforcement Learning.<n>We show how an LMDP with a goal kernel enables the efficient optimization of meta-policies in a lower-dimensional subspace defined by the task grounding.
  arXiv  Detail & Related papers  (2020-07-06T05:13:20Z)

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.