Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …

The distributed assembly mixed no-idle permutation flowshop scheduling problem
(DAMNIPFSP), a common occurrence in modern industries like integrated circuit production …

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in
practice, it is difficult to choose one appropriately for several reasons. First and …

The growth of artificial intelligence leads to a computational burden in solving non-
deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve …

Efficient truck dispatching is crucial for optimizing container terminal operations within
dynamic and complex scenarios. Despite good progress being made recently with more …

Grouping problems are a special type of combinatorial optimization problems that have
gained great relevance because of their numerous real-world applications. The solution …

The flexible job-shop scheduling problem (FJSP) is common in high-mix industries such as
semiconductor manufacturing. An FJSP is initiated when an operation can be executed on a …

Flexible job shop is prevalent in high-mix, low-volume (HMLV) production. Real-world HMLV
production environments are subjected to fluctuating demands that cause jobs to arrive …

Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization
problems. There are different kinds of HHs. One of them deals with how low-level heuristics …

Recent years have witnessed a growing interest in automatic learning mechanisms and
applications. The concept of hyper-heuristics, algorithms that either select among existing …