This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are…
Abstract
This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.
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K.P. Anagnostopoulos, P.D. Chatzoglou and S. Katsavounis
The purpose of this paper is to present a procedure for finding the efficient frontier, i.e. a non‐decreasing curve representing the set of Pareto‐optimal or non‐dominated…
Abstract
Purpose
The purpose of this paper is to present a procedure for finding the efficient frontier, i.e. a non‐decreasing curve representing the set of Pareto‐optimal or non‐dominated portfolios, when the standard Markowitz' classical mean‐variance model is enriched with additional constraints.
Design/methodology/approach
The mean‐variance portfolio optimization model is extended to include integer constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset. Optimization‐based procedures run into difficulties in this framework and this motivates the investigation of heuristic algorithms to find acceptable solutions.
Findings
The problem is solved by a greedy randomized adaptive search procedure (GRASP), enhanced by a learning mechanism and a bias function for determining the next element to be introduced in the solution.
Originality/value
This is believed to be the first time, a GRASP for finding the efficient frontier for this class of portfolio selection problems is used.
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Ernest Effah Ameyaw and Albert P. C. Chan
Allocating risk in public–private partnership (PPP) projects based on public–private parties’ risk management (RM) capabilities is a condition for success of these projects. In…
Abstract
Allocating risk in public–private partnership (PPP) projects based on public–private parties’ risk management (RM) capabilities is a condition for success of these projects. In practice, however, risks are allocated to these parties beyond their respective RM capabilities. Too much risk is often assigned to the private or public party, resulting in poor RM and costly contract renegotiations and terminations. This chapter proposes a methodology based on fuzzy set theory (FST) in which decision makers (DMs) use linguistic variables to assess and calculate RM capability values of public–private parties for risk events and to arrive at risk allocation (RA) decisions. The proposed methodology is based on integrating RA decision criteria, the Delphi method and the fuzzy synthetic evaluation (FSE) technique. The application of FSE allows for the introduction of linguistic variables that express DMs’ evaluations of RM capabilities. This provides a means to deal with the problems of qualitative, multi-criteria analysis, subjectivity and uncertainty that characterise decision-making in the construction domain. The methodology is outlined and demonstrated based on empirical data collected through a three-round Delphi survey. The public–private parties’ RM capability values for land acquisition risk are calculated using the proposed methodology. The methodology is helpful for performing fuzzy-based analysis in PPP projects, even in the event of limited or no data. This chapter makes the contribution of presenting a RA decision-making methodology that is easy to understand and use in PPP contracting and that enables DMs to track calculations of RM capability values.
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Hessa Almatroushi, Moncer Hariga, Rami As'ad and AbdulRahman Al-Bar
This paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.
Abstract
Purpose
This paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.
Design/methodology/approach
A mixed integer linear programming model is devised, which utilizes the splitting of noncritical activities as a mean toward leveling the renewable resources. The developed model minimizes renewable resources leveling costs along with consumable resources related costs, and it is solved using IBM ILOG CPLEX optimization package. A hybrid metaheuristic procedure is also proposed to efficiently solve the model for larger projects with complex networks structure.
Findings
The results confirmed the significance of the integrated approach as both the project schedule and the material ordering policy turned out to be different once compared to the sequential approach under same parameter settings. Furthermore, the integrated approach resulted in substantial total costs reduction for low values of the acquiring and releasing costs of the renewable resources. Computational experiments conducted over 240 test instances of various sizes, and complexities illustrate the efficiency of the proposed metaheuristic approach as it yields solutions that are on average 1.14% away from the optimal ones.
Practical implications
This work highlights the necessity of having project managers address project scheduling and materials lot sizing decisions concurrently, rather than sequentially, to better level resources and minimize materials related costs. Significant cost savings were generated through the developed model despite the use of a small-scale example which illustrates the great potential that the integrated approach has in real life projects. For real life projects with complex network topology, practitioners are advised to make use of the developed metaheuristic procedure due to its superior time efficiency as compared to exact solution methods.
Originality/value
The sequential approach, wherein a project schedule is established first followed by allocating the needed resources, is proven to yield a nonoptimized project schedule and materials ordering policy, leading to an increase in the project's total cost. The integrated approach proposed hereafter optimizes both decisions at once ensuring the timely completion of the project at the least possible cost. The proposed metaheuristic approach provides a viable alternative to exact solution methods especially for larger projects.
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Ehsan Goudarzi, Hamid Esmaeeli, Kia Parsa and Shervin Asadzadeh
The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled…
Abstract
Purpose
The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.
Design/methodology/approach
The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.
Findings
Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.
Practical implications
The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.
Originality/value
Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.
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Marimuthu Kannimuthu, Benny Raphael, Palaneeswaran Ekambaram and Ananthanarayanan Kuppuswamy
Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an…
Abstract
Purpose
Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).
Design/methodology/approach
A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.
Findings
Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.
Research limitations/implications
It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.
Originality/value
An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.
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Valentina N. Parakhina, Galina V. Vorontsova, Oksana N. Momotova, Olga A. Boris and Rustam M. Ustaev
This chapter studies the importance of implementation of innovational projects of technological growth through public–private partnership (PPP). The authors determine the…
Abstract
This chapter studies the importance of implementation of innovational projects of technological growth through public–private partnership (PPP). The authors determine the probability of implementing a project of PPP depending on distribution of risks between its participants. Usage of the mechanism of PPP allows optimizing possible risks during implementation of innovational activities, attracting large business for creation and implementation of new technologies, and forming sustainable ties between R&D departments and business structures. The types of risks in the projects of PPP are given, as well as tendencies of their emergence depending on the stage of implementation of the innovational project, including the following: formation of policy on development of PPP; preparatory, implementary, commercialization of the results of joint activities; and monitoring and control over execution of the project. The algorithm of the system of risk management in innovational projects of technological growth on the platform of PPP is presented. The methods of overcoming the risks that appear during implementation of an innovational project of technological growth within PPP are given. A special attention should be paid to the fourth (distribution of risks) and fifth (reduction of risks) stages. During implementation of innovational projects with application of a business model of PPP, the risks are dealt with by the participant who can manage them better. Reduction of risks is achieved better if several strategies are used – for decreasing the influence of the risk on the innovational project (strategies of risk evasion, acceptance of the risk situation, compensation, transfer, and reduction).
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Gabriella Marcarelli and Andrea Nappi
This paper aims to show how the proposed approach (two analytic hierarchy process [AHP] models) may allow dealing with the best tender selection process in an organic and simple…
Abstract
Purpose
This paper aims to show how the proposed approach (two analytic hierarchy process [AHP] models) may allow dealing with the best tender selection process in an organic and simple way and ensure the consistency check of the judgements, the necessary step for having reliable results. At first, this paper highlights some critical issues regarding the weighted sum model (WSM) and the algorithms frequently used to evaluate the most economic advantageous tender. Then, it proposes to extend the AHP approach to the evaluation of both the qualitative and quantitative components of a public procurement award. Finally, the WSM and the AHP are applied to the same case study to show, step by step, some criticisms of the former and some advantages of the latter.
Design/methodology/approach
This paper proposes to apply two AHP models to evaluate both qualitative and quantitative components of a public tender. The quality and cost models allow to identify and select the tender associated with the highest quality/cost ratio.
Findings
The assessment of the WSM and the AHP models, and some differences between them, build upon their application as an example of public procurement. A case study is used as a teaching device (Yin, 2003) to highlight why the AHP may provide different results. In particular, an important issue concerning the evaluation of qualitative requirements is explored: the consistency of judgements expressed by the committee members.
Social implications
This approach provides analytical tools for public management that allow appropriate implementation of their management function and allow a realisation of the strategic objectives of European Union law and Italian legislation on public procurement. It would help managers to prioritise their goals and criteria and evaluate them in a scientific way. The model integrates multiple qualitative and quantitative criteria, simplifies the selection process, achieves optimal use of funds and leads to cost savings. It allows to reduce the discretional power of both the contracting issuer, in the choice of the formula to adopt for calculating the coefficients, and the committee members, allowing tender evaluation to have more trust and ensure the fairness of public procurement matters and quality of the object purchased.
Originality/value
This paper proposes the use of two hierarchical models to evaluate qualitative and quantitative requirements and provide the ranking among several tenders.
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Manuel Blanco Abello and Zbigniew Michalewicz
This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary…
Abstract
Purpose
This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary Algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Some of the methods are useful for investigating the performance (solution-search abilities) of techniques (comprised of McBAR and other selected EA-based techniques) for solving some multi-objective dynamic resource-constrained project scheduling problems with time-varying number of tasks.
Design/methodology/approach
The RSM is applied to: determine some EA parameters of the techniques, develop models of the performance of each technique, legitimize some algorithmic components of McBAR, manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment.
Findings
The results of applying the methods are explored in the second part of this work.
Originality/value
The models are composite and characterize an EA memory-based technique. Further, the resiliency of techniques is determined by applying Lagrange optimization that involves the models.
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M. Ilangkumaran, V. Sasirekha, L. Anojkumar, G. Sakthivel, M. Boopathi Raja, T. Ruban Sundara Raj, CNS. Siddhartha, P. Nizamuddin and S. Praveen Kumar
This paper aims to describe an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of wastewater treatment (WWT) technology for treating…
Abstract
Purpose
This paper aims to describe an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of wastewater treatment (WWT) technology for treating wastewater.
Design/methodology/approach
The proposed approach is based on Analytical Hierarchy Process (AHP) under fuzzy environment, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) and hierarchy Grey Relation Analysis (GRA) techniques. Two models are proposed to evaluate the best WWT. The first model, Fuzzy Analytical Hierarchy Process (FAHP) is integrated with Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) technique. The second model, FAHP is integrated with hierarchy Grey Relation Analysis (GRA) technique. The Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of criteria and then ranking of the WWT technology is determined by PROMETHEE and GRA.
Findings
An efficient pair‐wise comparison process and ranking of alternatives can be achieved for WWT technology selection through the integration of FAHP and PROMETHEE, FAHP and GRA.
Originality/value
The paper highlights a new insight into MCDM techniques to select an optimum WWT technology selection for the paper manufacturing industry.