A5001699131- Scheduling and Optimization Algorithms
- Advanced Queuing Theory Analysis
- Advanced Multi-Objective Optimization Algorithms
- Simulation Techniques and Applications
- Supply Chain and Inventory Management
- Advanced Manufacturing and Logistics Optimization
- Healthcare Operations and Scheduling Optimization
- Emergency and Acute Care Studies
- Operations Management Techniques
- Metaheuristic Optimization Algorithms Research
- Optimal Experimental Design Methods
- Assembly Line Balancing Optimization
- Quality and Supply Management
- Optimization and Search Problems
- Scheduling and Timetabling Solutions
- Advanced Wireless Network Optimization
- Manufacturing Process and Optimization
- ERP Systems Implementation and Impact
- Design Education and Practice
- E-commerce and Technology Innovations
- Consumer Market Behavior and Pricing
- Heat Transfer and Optimization
- Food Supply Chain Traceability
- Transportation Planning and Optimization
- Sustainable Supply Chain Management
Hasselt University
2018-2024
Flanders Make (Belgium)
2022-2024
KU Leuven
2011-2023
Hubei University Of Economics
2019
Central South University
2019
Joint Research Center
2019
European University College
2005-2007
University of Antwerp
2002-2005
Abstract Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed perform HPO; most these are focused on optimizing one measure (usually an error-based measure), and literature such single-objective HPO problems vast. Recently, though, algorithms appeared that focus multiple conflicting objectives simultaneously. This article presents systematic survey published between 2014 2020...
This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this is used analyze the input/output data of model (computer code). and analysis adapt classic "expected improvement" (EI) in "efficient optimization" (EGO) through introduction an improved estimator predictor variance; parametric bootstrapping. Classic EI bootstrapped are compared various...
Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 2013, the present survey aims to unveil extent which simulation has been used solve practical inventory problems (as opposed small, theoretical “toy problem”), detect any trends that might have arisen (e.g., topics, effective methods, frequently studied system structures). We find metaheuristics (especially genetic...
Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial studying infectious disease dynamics. Compartmental models represent popular approach address problem, dividing population interest into discrete finite number states depending on, for example, individuals' age stage infection. We study corresponding linearised system whose...
In this article, we present a decision support system (DSS) for improving patient flow in emergency departments (EDs).The core of the is discrete-event simulation (DES) model that aims to capacity planning ED, view controlling patients' length stay (LOS).Conceptually, it regards LOS as result different queueing systems, behaviour which influenced by types capacities.Taking inputs from ED record data, DSS allows analyse impact changes on flow, and detect efficient combinations using data...
This article proposes a supporting framework for the implementation of material control system POLCA (paired-cell overlapping loops cards with authorization). The is particularly appropriate environments that involve highly variable demand and large product variety, which force small batch (or even one-of-a-kind) production. We propose load-based version (LB-POLCA), determines parameters (release authorizations, allowed workloads in loops) according to an advanced resources planning (ARP)...
This paper describes two experiments exploring the potential of Kriging methodology for constrained simulation optimization. Both study an (s, S) inventory system with objective finding optimal values s and S. The goal function constraints in these differ, as does approach to determine optimum combination predicted by model. results indicate that offers opportunities solving optimization problems stochastic simulation; future research will focus on further refining methodology.
This paper describes two experiments exploring the potential of Kriging methodology for constrained simulation optimization. Both study an (s, S) inventory system with objective finding optimal values s and S. The goal function constraints in these differ, as does approach to determine optimum combination predicted by model. results indicate that offers opportunities solving optimization problems stochastic simulation; future research will focus on further refining methodology.
This tutorial focuses on kriging-based simulation optimization, emphasizing the importance of data efficiency in optimization problems involving expensive models. It discusses how kriging models contribute to developing algorithms that minimize number required simulations, particularly presence noisy evaluations. The compares performance against traditional polynomial-based methods using an illustrative example. Additionally, it key extensions algorithms, including multi-objective and...