Eleftherios Iakovou

Texas A&M University

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Dimitrios Vlachos

Kantonsspital St. Gallen

A. Xanthopoulos

Aristotle University of Thessaloniki

Dimitrios Aidonis

International Hellenic University

Ioannis Mallidis

University of Western Macedonia

Christos Douligeris

University of Piraeus

Efstratios N. Pistikopoulos

Mitchell Institute

Christos Keramydas

International Hellenic University

Natasha J. Chrisandina

Texas A&M University

ΑΤ

Α. Τόκα

Aristotle University of Thessaloniki

Shivam Vedant

Texas A&M University

Cooperative Institutions

Aristotle University of Thessaloniki

University of Miami

Centre for Research and Technology Hellas

International Hellenic University

Texas A&M University

University of Western Macedonia

Technological Educational Institute of Central Macedonia

Mitchell Institute

National and Kapodistrian University of Athens

Georgia Institute of Technology

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Biomass Logistics for Bio-energy Production: A Methodological Framework

Eleftherios Iakovou Dionisios G. Vlachos Zissis Samaras

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Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in Scenario Analysis & Optimization

Funda Iseri Halil Iseri Harsh Shah Eleftherios Iakovou Efstratios N. Pistikopoulos

The global energy market faces significant challenges due to increasing demand, growing competition, and the ongoing energy transition toward renewable sources. Addressing these complexities requires advanced methodologies that can effectively navigate uncertainty and optimize investment and operational decisions. This study presents a flexible scenario-based framework for capacity-related decision making and investment planning in energy systems comprising solar, wind, and natural gas facilities. The proposed framework integrates Bayesian Neural Networks (BNNs) into an optimization problem to address uncertainties in energy generation and demand forecasts. It leverages probabilistic posterior distributions to generate realistic, data-driven scenarios with associated probabilities. The generated scenarios are incorporated into a two-stage stochastic multi-period mixed integer linear optimization model. The first stage optimizes investment decisions for new facilities prior to the realization of uncertainty, while the second stage incorporates operational costs, capacity expansions, and penalties for unmet demand across multiple future scenarios and time periods. The model accounts for real-world considerations such as construction and expansion lag times, capacity constraints, and scenario-dependent demands. This methodology enhances the flexibility of energy systems, enabling planners to make cost-effective future investments and operational decisions through the complexities of the modern energy landscape. The proposed framework offers significant advantages over traditional methods by capturing nuanced uncertainty distributions and enabling flexible and risk-aware decision-making.

Investment Decisions

Scenario analysis

Investment

10.2139/ssrn.5088471

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A strategic supply chain management model for waste biomass networks

Dimitrios Vlachos Eleftherios Iakovou Α. Τόκα A. Karagiannidis

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A hierarchical taxonomy of decision-making models for container terminal operations

International Journal of Logistics Economics and Globalisation (2008)

D. Tsitsamis Dimitrios Vlachos Eleftherios Iakovou Christos Douligeris

Managing container terminal operations in seaports is a cumbersome, complex and challenging problem. Given the importance of container traffic for international trade, it is surprising that this issue has been mostly tackled myopically. The problem involves a number of different criteria, such as faster discharge and loading of containers (vessel turnaround time), increased yard and equipment utilisation, and faster container turnaround time. In this article, we present a novel hierarchical framework that captures systematically the complexity in the decision-making process recognising its natural hierarchy (strategic, tactical and operational), the interdependencies among specific operations and the various forms of constraints including technological, spatial, temporal, monetary, and organisational as well as constraints imposed by the interests of various partners and customers of a port. We further emphasise and document the role of information technologies and management information systems solutions in implementing comprehensive policies.

Port (circuit theory)

10.1504/ijleg.2008.023168

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Development of a multi-objective model for the design of sustainable supply chains: the case of perishable food products

Annals of Operations Research (2019)

Volha Yakavenka Ioannis Mallidis Dimitrios Vlachos Eleftherios Iakovou Eleni Zafeiriou

Consumption

Supply network

Minification

10.1007/s10479-019-03434-5

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A Methodological Framework for Integrating Waste Biomass into a Portfolio of Thermal Energy Production Systems

Green energy and technology (2012)

Eleftherios Iakovou Dimitrios Vlachos Α. Τόκα

10.1007/978-1-4471-2306-4_3

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“Nearshoring and offshoring for global supply chain networks: a total landed cost perspective”

Eleftherios Iakovou Dimitrios Vlachos Maria Chatzipanagioti

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A Continuous Simulation-based Methodological Framework for Port Container Logistics

International Journal of Logistics Economics and Globalisation (2008)

Eleftherios Iakovou Dimitrios Vlachos Patroklos Georgiadis

Port (circuit theory)

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An information management system for the emergency management of hurricane disasters

International Journal of Risk Assessment and Management (2001)

Eleftherios Iakovou Christos Douligeris

Access to accurate and comprehensive information for a wide spectrum of topics to support hurricane disaster planning and response is of critical importance to emergency managers. This paper presents the development of IMASH, an Information Management System for Hurricane disasters. IMASH is an intelligent integrated dynamic information management tool, capable of providing comprehensive data pertaining to emergency planning and response for hurricane disasters. The architecture of IMASH along with all its modules is presented in this paper. IMASH is based on an object-oriented database containing geographically referenced environmental and related data. It further employs the intelligence of a decision support system, while providing worldwide access through the internet and the World Wide Web. The developed system can offer support for a wide range of hurricane disaster related activities hurricane preparedness activities, activities during or just before a hurricane strikes and post-disaster response and restoration activities; it can further serve as a platform that could dynamically be augmented to include new data and models.

Preparedness

Disaster Response

10.1504/ijram.2001.001508

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A planning optimisation framework for construction and demolition waste management

International Journal of Industrial and Systems Engineering (2012)

A. Xanthopoulos Dimitrios Aidonis Dimitrios Vlachos Eleftherios Iakovou

The goal of this paper is to present a new planning optimisation framework for the disassembly processes of an end-of-life (EOL) building, by explicitly taking into account, as an input, its bill of materials. When disassembling an EOL building, there is the option either to selectively deconstruct specific components and materials for reuse and recycling, respectively, while demolishing the remainder of the building, or to further continue the deconstruction process and postpone the demolition of the building for a later stage. In this context, we present a novel, integrated, decision-making model for the entire construction and demolition supply chain, starting from the optimisation of the on-site deconstruction and demolition decisions till the delivery of the collected components and materials to potential recyclers/customers and landfills. An application of the developed methodology is demonstrated via a specific case study, while the relative obtained managerial insights, regarding the behaviour of the optimal solution, are presented.

Demolition waste

10.1504/ijise.2012.045675

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Citations (19)