This paper investigates how to use prediction strategies to improve the performance of multiobjective evolutionary optimization algorithms in dealing with dynamic environments. Prediction-based methods have been applied to predict some isolated points in both dynamic single objective optimization and dynamic multiobjective optimization. We extend this idea to predict a whole population by considering the properties of continuous dynamic multiobjective optimization problems. In our approach, called population prediction strategy (PPS), a Pareto set is divided into two parts: a center point and a manifold. A sequence of center points is maintained to predict the next center, and the previous manifolds are used to estimate the next manifold. Thus, PPS could initialize a whole population by combining the predicted center and estimated manifold when a change is detected. We systematically compare PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables. The statistical results show that PPS is promising for dealing with dynamic environments.

A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization

The topic of dynamic pricing and learning has received a considerable amount of attention in recent years, from different scientific communities. We survey these literature streams: we provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning. Our focus is on the operations research and management science literature, but we also discuss relevant contributions from marketing, economics, econometrics, and computer science. We discuss relations with methodologically related research areas, and identify directions for future research.

Dynamic pricing and learning: Historical origins, current research, and new directions

https://ris.utwente.nl/ws/files/5090800/memo2025.pdf

Dynamic pricing and learning: historical origins, current research, and new directions

We demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multi-layer network induces hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.

Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.

/pdf/unsupervised-clustering-with-spiking-neurons-by-sparse-4hf573mpho.pdf

Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks

Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on patient admissions or operating room schedules. In this paper we present an agent-based model for the selection of an optimal mix for patient admissions. Admitting the right mix of patients is important in order to optimize the resource usage and patient throughput. Our model is based on an extensive case analysis, involving data analysis and interviews, conducted in a case study at a large hospital in the Netherlands. We focus on the coordination of different surgical patient types with probabilistic treatment processes involving multiple hospital units. We also consider the unplanned arrival of other patients requiring (partly) the same hospital resources. Simulation experiments show the applicability of our agent-based decision support tool. The simulation tool allows for the assessment of resource network usage as a function of different policies for decision making. Furthermore, the tool incorporates a first optimization module for the resource allocation of postoperative care beds.

Agent-based patient admission scheduling in hospitals

Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. Furthermore, the problem is dynamic as patient arrival and treatment processes are stochastic. In this paper we present a strategy optimization approach where the parameters of different strategies are optimized using a multiobjective EDA. The strategies were designed such that they enable dynamic resource allocation with an offline EDA. Also, the solutions are understandable to health care professionals. We show that these techniques can be applied to this real-world problem. The results are compared to allocation strategies used in hospital practice.

/pdf/evolutionary-multiobjective-optimization-for-dynamic-2cxwlmv87q.pdf

Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management

This paper presents a case study for the application of agent-mediated negotiation techniques in transportation logistics. More specifically, we consider the interaction between several logistics service providers negotiating over the allocation of transportation orders. In this context, we show that automated negotiation techniques (especially multi-issue or multi-item negotiation) can bring significant advantages, by allowing parties to discover jointly profitable bundles (allocations) of orders. The model, evaluations and results reported in this paper concern the business processes of Vos Logistics, one of the largest European transportation logistic providers.

Automating supply chain negotiations using autonomous agents: a case study in transportation logistics

Lower Bounds for the Union-Find and the Split-Find Problem on Pointer Machines

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.

Han La Poutré

Papers

Agent-based patient admission scheduling in hospitals

Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management

Automating supply chain negotiations using autonomous agents: a case study in transportation logistics

Lower Bounds for the Union-Find and the Split-Find Problem on Pointer Machines

Robust Evolutionary Algorithm Design for Socio-economic Simulation