Konstantinos P. Anagnostopoulos

A portfolio optimization model with three objectives and discrete variables

We formulate the portfolio selection as a tri-objective optimization problem so as to find tradeoffs between risk, return and the number of securities in the portfolio. Furthermore, quantity and class constraints are introduced into the model in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings. Since the proposed portfolio selection model involves mixed integer decision variables and multiple objectives finding the exact efficient frontier may be very hard. Nevertheless, finding a good approximation of the efficient surface which provides the investor with a diverse set of portfolios capturing all possible tradeoffs between the objectives within limited computational time is usually acceptable. We experiment with the current state of the art evolutionary multiobjective optimization techniques, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), for solving the mixed-integer multiobjective optimization problem and provide a performance comparison among them using metrics proposed by the community.

Public-private partnership projects in Greece: risk ranking and preferred risk allocation

Project participants, through experience, have an initial perception and predisposition towards risk and the types of risks they are willing and able to undertake. This is equally true for parties interested in public–private partnership (PPP) projects. These initial positions have been registered for the major Greek PPP market stakeholders potentially involved in a PPP arrangement through a survey covering all candidate construction companies, interested financing institutes and a number of public sector entities to be involved in PPPs. Findings revealed that stakeholders were, for the majority of risks identified, in agreement as to preferred risk allocation. Risk allocation preferences for construction companies were compared with similar findings for the UK, a mature PPP market, indicating a possible learning/maturing process based on the particular country background. Conclusions add to other surveys carried out on the subject and should enable public sector clients to establish a more efficient framework for risk allocation, thus reducing negotiations prior to contract award and minimizing the risk of poor risk distribution.

The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.

A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem

Abstract In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic algorithm for solving the resource constrained project scheduling problem (RCPSP). To the best of our knowledge, this is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP. The hyper-heuristic works as an upper-level algorithm that controls several low-level heuristics which operate to the solution space. The solution representation is based on random keys. Active schedules are constructed by the serial scheduling generation scheme using the priorities of the activities which are modified by the low-level heuristics of the algorithm. Also, the double justification operator, i.e. a forward–backward improvement procedure, is applied to all solutions. The proposed approach was tested on a set of standard problem instances of the well-known library PSPLIB and compared with other approaches from the literature. The promising computational results validate the effectiveness of the proposed approach.

Multiobjective evolutionary algorithms for complex portfolio optimization problems

This paper investigates the ability of Multiobjective Evolutionary Algorithms (MOEAs), namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), for solving complex portfolio optimization problems. The portfolio optimization problem is a typical bi-objective optimization problem with objectives the reward that should be maximized and the risk that should be minimized. While reward is commonly measured by the portfolio’s expected return, various risk measures have been proposed that try to better reflect a portfolio’s riskiness or to simplify the problem to be solved with exact optimization techniques efficiently. However, some risk measures generate additional complexities, since they are non-convex, non-differentiable functions. In addition, constraints imposed by the practitioners introduce further difficulties since they transform the search space into a non-convex region. The results show that MOEAs, in general, are efficient and reliable strategies for this kind of problems, and their performance is independent of the risk function used.

A simulated annealing hyperheuristic for construction resource levelling

Resource levelling techniques aim to minimize the fluctuation from one time period to another in resource usage. Except for small‐sized problems, though, computational optimization procedures are inefficient when solving construction resource levelling problems. Consequently, heuristic and metaheuristic approaches are used to get an acceptable, but not necessarily optimal, solution. A simulated annealing hyperheuristic to generate better‐levelled resource profiles is proposed. Unlike traditional heuristic algorithms, a hyperheuristic operates in the ‘low level’ heuristics domain rather than in the solutions domain. A low level heuristic, on the other hand, works in the current solution neighbourhood. The algorithm has been programmed within a commercial project management software system to improve its performance. The low level heuristics operate on the priority levels that the software uses for resource levelling. An illustrative example and the computational analysis demonstrate the potential of the procedure in solving complex scheduling problems.

Prophylaxis with mesna prevents oxidative stress induced by ischemia reperfusion in the intestine via inhibition of nuclear factor‐κB activation

Background and Aim:  Mesna (2‐mercaptoethane‐sulfonate) has been shown to attenuate oxidative injury induced by ischemia reperfusion (I/R) in the kidneys, the liver, and the intestine; however, its mechanism of action has not been fully elucidated. We sought to determine a prophylactic admininstration schedule of mesna that would confer optimal antioxidant protection on the intestinal mucosa following I/R and to investigate whether mesnas action is mediated via inhibition of nuclear factor‐κB (NF‐κB) activity.

An AHP model for construction contractor prequalification

Given that contractor plays a critical role in any construction project, contractor selection constitutes key decision for public authorities. Prequalification, i.e. the elimination of incompetent contractors from the bidding process according to a predetermined set of criteria, is a frequently practiced procedure in many countries, including Greece. In order to enhance the performance levels of selected contractors and to minimize failures in meeting client’s objectives, several criteria must be taken into account and a consistent evaluation methodology must be applied. We propose a multicriteria decision making approach, based on the Analytic Hierarchy Process (AHP), for supporting public authorities in contractor prequalification. The decision problem is decomposed into qualitative criteria and sub-criteria that are further analyzed in quantitative indicators on which the candidate contractors are evaluated. Our advisory decision support system is an appropriate tool for at least three reasons: First, various criteria are included, in order to ensure the quality of the completed product. Second, it is easy to use, because, on the one hand, it requires no prior knowledge of multicriteria methods from the potential users; and, on the other hand, it minimizes subjective judgments. Finally, the model minimizes the required pairwise comparisons, which is considered to be a major default of AHP.

GIS-based multicriteria municipal solid waste landfill suitability analysis: A review of the methodologies performed and criteria implemented

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of the geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods. GIS-based MCDA allows the incorporation of conflicting objectives and decision maker (DM) preferences into spatial decision models. During recent decades, a variety of research articles have been published regarding the implementation of methods and/or tools in a variety of real-world case studies. The article discusses, in detail, the criteria and methods that are implemented in GIS-based landfill siting suitability analysis and especially the exclusionary and non-exclusionary criteria that can be considered when selecting sites for municipal solid waste (MSW) landfills. This paper reviews 36 seminal articles in which the evaluation of candidate landfill sites is conducted using MCDA methods. After a brief description of the main components of a MC-SDSS and the applied decision rules, the review focuses on the criteria incorporated into the decision models. The review provides a comprehensive guide to the landfill siting analysis criteria, providing details regarding the utilization methods, their decision or exclusionary nature and their monotonicity.

Construction Resource Allocation and Leveling Using a Threshold Accepting–Based Hyperheuristic Algorithm

AbstractIn this study we propose a threshold accepting based hyperheuristic for solving in a single run both the resource-constrained project scheduling problem or resource allocation, and the resource leveling problem. Having their roots in the field of artificial intelligence, hyperheuristics operate in the “low-level” heuristics domain rather than in the solutions domain. The hyperheuristic has been implemented within a commercial project management software package. Low-level heuristics operate on the solution domain defined by the priority values that the software uses for resource allocation. A case example from the literature and computational experiments on randomly generated projects demonstrate that the hyperheuristic achieves good performance in a timely manner, improving the results provided by the software.