Simulation optimization (SO) refers to the optimization of an objective function subject to
constraints, both of which can be evaluated through a stochastic simulation. To address …

Simulation optimization refers to the optimization of an objective function subject to
constraints, both of which can be evaluated through a stochastic simulation. To address …

Swarm intelligence is a relatively new approach to problem solving that takes inspiration
from the social behaviors of insects and of other animals. In particular, ants have inspired a …

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve
complex optimization problems, and they are a growing research area since a few decades …

This book is written for students and researchers in the field of industrial engineering,
computer science, operations research, management science, electrical engineering, and …

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-
complete optimization problems. Over the last 10–15 years, tabu search (TS) has emerged …

Simulated annealing is a well-studied local search metaheuristic used to address discrete
and, to a lesser extent, continuous optimization problems. The key feature of simulated …

Metaheuristics have been established as one of the most practical approaches to simulation
optimization. However, these methods are generally designed for combinatorial …

Since the emergence of large language models, prompt learning has become a popular
method for optimizing and customizing these models. Special prompts, such as Chain-of …

While mathematically sophisticated methods can be used to better understand and improve
processes, the nonlinear nature of food processing models can make their dynamic …