scholarly journals Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

Organizacija ◽  
2017 ◽  
Vol 50 (4) ◽  
pp. 364-373 ◽  
Author(s):  
Christina Brester ◽  
Ivan Ryzhikov ◽  
Eugene Semenkin
Keyword(s):  
Decision Making ◽  
Project Management ◽  
Optimization Problem ◽  
Decision Makers ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Financial Investment ◽  
Financial Responsibility ◽  
Multi Objective ◽  
Decision Making Problem

Abstract Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.

scholarly journals Multi Objective Multi Mode Project Management Problem in Triangular Fuzzy Environment

2020 ◽  
Vol 9 (2) ◽  
pp. 1772-1781
Keyword(s):  
Project Management ◽  
Fuzzy Numbers ◽  
Critical Path ◽  
Decision Makers ◽  
Programming Approach ◽  
Management Problem ◽  
Linguistic Variables ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Multi Mode

Execution of any project with optimum duration, cost, quality and risk is very significant for project administrators in recent very competitive commercial situation. Sometimes it is not possible to have detailed earlier statistics about project criteria. In such situations, estimation of different Decision makers are considered in linguistic variables and altered into triangular fuzzy numbers as fuzzy numbers have ability to deal with vagueness. In this paper, we frame a new multi-mode multi objective critical path problem and suggest a possibilistic methodology to find critical path for a project where three decision makers’ views are considered as three modes of execution in terms of linguistic variables. We have formulated model of multiple mode in project network problem and find its solution with fuzzy programming approach with exponential membership and linear membership function. The proposed approach is useful to solve multi-mode project management problem which calculates optimal critical path according to four criteria- time, cost, risk and quality with three activities modes of execution in fuzzy environment.

scholarly journals Solving the Asymmetry Multi-Objective Optimization Problem in PPPs under LPVR Mechanism by Bi-Level Programing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1667
Author(s):  
Feiran Liu ◽  
Jun Liu ◽  
Xuedong Yan
Keyword(s):  
Optimization Problem ◽  
Optimal Strategies ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Present Value ◽  
Public And Private ◽  
The Public ◽  
Multi Objective ◽  
The Cost

Optimizing the cost and benefit allocation among multiple players in a public-private partnership (PPP) project is recognized to be a multi-objective optimization problem (MOP). When the least present value of revenue (LPVR) mechanism is adopted in the competitive procurement of PPPs, the MOP presents asymmetry in objective levels, control variables and action orders. This paper characterizes this asymmetrical MOP in Stackelberg theory and builds a bi-level programing model to solve it in order to support the decision-making activities of both the public and private sectors in negotiation. An intuitive algorithm based on the non-dominated sorting genetic algorithm III (NSGA III) framework is designed to generate Pareto solutions that allow decision-makers to choose optimal strategies from their own criteria. The effectiveness of the model and algorithm is validated via a real case of a highway PPP project. The results reveal that the PPP project will be financially infeasible without the transfer of certain amounts of exterior benefits into supplementary income for the private sector. Besides, the strategy of transferring minimum exterior benefits is more beneficial to the public sector than to users.

MULTI-OBJECTIVE INITIAL PREVENTIVE MAINTENANCE SCHEDULING FOR LARGE ENGINEERING PLANTS

2007 ◽  
Vol 14 (03) ◽  
pp. 241-250 ◽  
Author(s):  
A. K. VERMA ◽  
P. G. RAMESH
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Preventive Maintenance ◽  
Domain Knowledge ◽  
Optimization Problem ◽  
Maintenance Scheduling ◽  
Start Time ◽  
Multi Objective ◽  
Decision Making Problem ◽  
Large Systems

Systems, sub systems and components of large engineering plants are grouped into Higher Modular Assemblies (HMA), all of which will need to be operational for the plant to be available. However, each HMA could have different intervals and durations for maintenance based on their different physical and functional characteristics resulting in narrow windows of plant operational periods. The problem of scheduling maintenance of large systems have been studied in literature in a limited way from this perspective and in practice, the management of the issue involves elements of subjectivity, sub-optimality and lack of adequate sensitivity analysis. The present paper views the initial scheduling of preventive maintenance as a constrained non linear multi-objective decision making problem. The objectives of reliability, cost and newly introduced criteria, non-concurrence of maintenance periods and maintenance start time factor are simultaneously optimized. The solution enables the decision maker to make a balanced choice of maintenance intervals which provide larger operational periods. The optimization problem is solved using an elitist GA and maintenance domain knowledge is effectively incorporated in its implementation.

scholarly journals A Simple Proposal for Including Designer Preferences in Multi-Objective Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 991
Author(s):  
Xavier Blasco ◽  
Gilberto Reynoso-Meza ◽  
Enrique A. Sánchez-Pérez ◽  
Juan Vicente Sánchez-Pérez ◽  
Natalia Jonard-Pérez
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Underlying Structure ◽  
Multi Objective Optimization ◽  
Number Of Solutions ◽  
Multi Objective ◽  
Objective Space ◽  
Definition Of

Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we call a preference basis that shows the preferred optimization directions in the objective space. Associated to this preference basis a new basis in the objective space—dominance basis—is computed. With this new basis the meaning of dominance is reinterpreted to include the designer’s preferences. In this paper, we show the effect of changing the geometric properties of the underlying structure of the Euclidean objective space by including preferences. This way of incorporating preferences is very simple and can be used in two ways: by redefining the optimization problem and/or in the decision-making phase. The approach can be used with any multi-objective optimization algorithm. An advantage of including preferences in the optimization process is that the solutions obtained are focused on the region of interest to the designer and the number of solutions is reduced, which facilitates the interpretation and analysis of the results. The article shows an example of the use of the preference basis and its associated dominance basis in the reformulation of the optimization problem, as well as in the decision-making phase.

scholarly journals Fuzzy Multiobjective Reliability Optimization Problem of Industrial Systems Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Harish Garg
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Decision Makers ◽  
Reliability Optimization ◽  
Imprecise Data ◽  
Design Problems ◽  
Industrial Systems ◽  
Conflicting Objectives ◽  
Decision Making Problem ◽  
Practical Constraints

The present work investigates the reliability optimization problem of the repairable industrial systems by utilizing uncertain, limited, and imprecise data. In many practical situations where reliability enhancement is involved, the decision making is complicated because of the presence of several mutually conflicting objectives. Moreover, data collected or available for the systems are vague, ambiguous, qualitative, and imprecise in nature due to various practical constraints and hence create some difficulties in optimizing the design problems. To handle these problems, this work presents an interactive method for solving the fuzzy multiobjective optimization decision-making problem, which can be used for the optimization decision making of the reliability with two or more objectives. Based on the preference of the decision makers toward the objectives, fuzzy multi-objective optimization problem is converted into crisp optimization problem and then solved with evolutionary algorithm. The proposed approach has been applied to the decomposition unit of a urea fertilizer plant situated in the northern part of India producing 1500–2000 metric tons per day.

scholarly journals A Multi-Criteria Decision Support Concept for Selecting the Optimal Contractor

2021 ◽  
Vol 11 (4) ◽  
pp. 1660 ◽  
Author(s):  
Ivan Marović ◽  
Monika Perić ◽  
Tomaš Hanak
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Project Performance ◽  
Decision Makers ◽  
Construction Project Management ◽  
Analytic Hierarchy ◽  
Conflicting Demands ◽  
Decision Making Problem ◽  
Scientific Approach ◽  
Hierarchy Process

A way to minimize uncertainty and achieve the best possible project performance in construction project management can be achieved during the procurement process, which involves selecting an optimal contractor according to “the most economically advantageous tender.” As resources are limited, decision-makers are often pulled apart by conflicting demands coming from various stakeholders. The challenge of addressing them at the same time can be modelled as a multi-criteria decision-making problem. The aim of this paper is to show that the analytic hierarchy process (AHP) together with PROMETHEE could cope with such a problem. As a result of their synergy, a decision support concept for selecting the optimal contractor (DSC-CONT) is proposed that: (a) allows the incorporation of opposing stakeholders’ demands; (b) increases the transparency of decision-making and the consistency of the decision-making process; (c) enhances the legitimacy of the final outcome; and (d) is a scientific approach with great potential for application to similar decision-making problems where sustainable decisions are needed.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

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