A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling

Automated Design of Multipass Heuristics for Resource-Constrained Job Scheduling With Self-Competitive Genetic Programming

Resource constraint job scheduling is an important combinatorial optimization problem with many practical applications. This problem aims at determining a schedule for executing jobs on machines satisfying several constraints (e.g., precedence and resource constraints) given a shared central resource while minimizing the tardiness of the jobs. Due to the complexity of the problem, several exact, heuristic, and hybrid methods have been attempted. Despite their success, scalability is still a major issue of the existing methods. In this study, we develop a new genetic programming algorithm for resource constraint job scheduling to overcome or alleviate the scalability issue. The goal of the proposed algorithm is to evolve effective and efficient multipass heuristics by a surrogate-assisted learning mechanism and self-competitive genetic operations. The experiments show that the evolved multipass heuristics are very effective when tested with a large dataset. Moreover, the algorithm scales very well as excellent solutions are found for even the largest problem instances, outperforming existing metaheuristic and hybrid methods.

A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated in isolation. When hyper-heuristics are selected among existing algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features that accurately describe the problem state—and thus allow for a proper mapping—requires plenty of domain-specific knowledge, which is not always available. This work proposes a simple yet effective hyper-heuristic model that does not rely on problem features to produce such a mapping. The model defines a fixed sequence of heuristics that improves the solving process of knapsack problems. This research comprises an analysis of feature-independent hyper-heuristic performance under different learning conditions and different problem sets.

People-Centric Evolutionary System for Dynamic Production Scheduling

Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming (GP) methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This article develops a novel people-centric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of GP. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution toward the desired solutions.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.