2013 Single-machine scheduling with learning and forgetting effects

Research Overview

This paper introduces a novel scheduling framework that simultaneously considers both learning and forgetting effects in single-machine environments, providing a more realistic model of human performance in manufacturing systems.

Key Contributions

Dual Effect Modeling:

• Develops mathematical models that capture the paradoxical nature of human learning: performance improves with practice (learning effect) but deteriorates during interruptions or breaks (forgetting effect)
• Establishes position-dependent and time-dependent functions to model how processing times change based on both job sequence and temporal gaps

Theoretical Framework:

• Provides rigorous mathematical analysis of the combined learning-forgetting phenomenon
• Derives optimal conditions for minimizing various scheduling objectives including makespan, total completion time, and weighted completion time
• Establishes polynomial-time algorithms for specific problem variants

Performance Analysis:

• Compares scheduling strategies that ignore learning/forgetting effects versus those that explicitly optimize for these phenomena
• Demonstrates significant performance improvements when both effects are properly modeled
• Analyzes the trade-offs between learning gains and forgetting losses in different scheduling scenarios

Practical Applications:

• Shows relevance to manufacturing environments where workers experience skill acquisition through repetition but suffer performance degradation during shift changes, breaks, or task interruptions
• Provides guidelines for optimal job sequencing and break scheduling in human-centric production systems

Impact and Significance

Published in Applied Mathematical Modelling, this research established a new paradigm in scheduling theory by recognizing that learning and forgetting are dual aspects of human performance that must be considered together. The work has influenced subsequent research in human factors engineering and production scheduling, providing mathematical foundations for optimizing work environments that account for realistic human performance patterns.

Recommended citation: Peng-Jen Lai and Wen-Chiung Lee. (2013). "Single-machine scheduling with learning and forgetting effects." Applied Mathematical Modelling. Volume 37, Issue 6, Pages 4509–4516.
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