用遗传算法优化生产线任务调度，降低总成本。
Cost Optimization in Production Line Using Genetic Algorithm
- 采用任务编码的遗传算法直接分配任务到工位。
- 在三类依赖结构下，任务编码收敛更稳、成本更低。
- 适合有复杂约束的工业调度场景，优于传统优化方法。
本文提出一种基于遗传算法（GA）的生产线上任务调度成本优化方法。系统包含一系列串行处理任务，每个任务具有固定持续时间、单位执行成本及前序依赖关系，需分配至无限数量工位，且每工位总时长受上限约束。目标是最小化总生产成本，该成本为工位级函数，依赖任务成本与时间上限，同时严格满足所有前置与容量约束。研究对比了两种染色体编码策略：基于工位的表示（使用JGAP库并加入SuperGene有效性检查）和基于任务的表示（基因直接编码工位分配）。针对每种编码，适配标准遗传操作（交叉、变异、选择、替换），以保持可行性并引导种群向低代价方案收敛。在三类前序结构（紧密耦合、松散耦合、无耦合）上的实验表明，任务编码在有效解空间较大时表现出更平滑的收敛性和更可靠的降本效果。研究强调了遗传算法在处理复杂约束与非可导成本曲面的组合优化问题上，相比梯度法和解析法的优势。
原文摘要 · Abstract (English)
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.
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