Cost Optimization in Production Line Using Genetic Algorithm

TL;DR

This paper introduces a genetic algorithm approach for cost-efficient task scheduling in production lines, effectively handling complex constraints and demonstrating superior convergence over traditional methods.

Contribution

It compares two GA encoding strategies and shows that task-based encoding offers better convergence and cost minimization in production scheduling.

Findings

Task-based encoding outperforms station-based encoding in convergence.

GA effectively handles complex precedence and capacity constraints.

Experimental results show GA's advantages over gradient-based methods.

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.