Operations Research 2025
Abstract Submission

2402. Interdependence-aware Modeling, Analytics and Optimization for complex Systems’ Business

Invited abstract in session TA-12: AI for Optimization Modeling, stream Artificial Intelligence, Machine Learning and Optimization.

Thursday, 8:45-10:15
Room: H10

Authors (first author is the speaker)

1. Taieb Mellouli
Business Information Systems and Operations Research, Martin-Luther-University Halle-Wittenberg

Abstract

In production systems, hierarchical interdependencies of decisions, e.g. between tactical (resource-level) and operational (activity-level) decisions, arise and integrative optimization leads to considerable additional gain. However, for complex business environments, such as airlines and hospitals, planning problems and their interdependencies are intricate, since humans are integrated at customer service (demand) side - patients in hospitals - or at resource (supply) side - crew personal in airlines. The planning of their activities (medical treatments/flights) should comply with complex working rules in case of crew (duties and pairings) and with medical/clinical standards in case of patients (patient pathways). To this side of complexity related to composition of activities, one faces another complexity of multiplicity of these activity structures within (patient/crew) flow problem settings.

A new two-dimensional aggregation scheme w.r.t designed crossed dimensions for such complexities is presented which serves as modeling, analytics and optimization design tool for complex systems’ businesses. Assigning appropriate aggregation/granulation levels to each complexity dimension, one can easily characterize different decision problems at strategic, tactical and operational stages – some of those may have not been apparent in advance. Types of solution methods can also be entailed in a generic way: Most problems at aggregated levels require descriptive/predictive analytics as well as AI for learning complex structures. However, planning problems at granulated levels should be tackled by prescriptive analytics and OR. Further, interdependencies between aggregated and granulated levels of decision problems give rise to AI/OR synergies of methodologies.

Keywords

Status: accepted

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