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.

中文翻译：

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具有岛元启发式的多目标优化算法，可有效解决项目管理问题

摘要背景和目的：在每个组织中，项目管理都会引发许多不同的决策问题，其中很大一部分可以使用特定的决策支持系统有效地解决。然而，这类问题始终是一个挑战，因为由于其复杂性，没有时间高效或计算效率高的算法可以解决这些问题。在这项研究中，我们考虑了最佳金融投资的问题。在我们的解决方案中，我们考虑了以下组织资源和项目特征：利润，成本和风险。设计/方法/方法：决策问题简化为多准则0-1背包问题。这意味着我们需要找到一组非主导的替代解决方案，这是在使收入最大化和风险最小化之间进行权衡的方法。同时，替代方案必须满足约束条件。这导致布尔空间中的约束二准则优化问题。为了解决该问题的特殊性和高复杂性，将具有岛元启发式算法的基于进化的算法用作传统技术的替代方法。结果：手头的问题被简化为两准则无约束的极端问题，并通过不同的基于演化的多目标优化启发式算法得以解决。接下来，我们应用了一种提出的元启发式方法，结合了特定的算法，并以协作和协作的方式引起它们的交互。获得的结果表明，基于特定度量的值，该岛试探法优于原始方法，从而显示了帕累托前沿近似的代表性。有了更多具有代表性的近似值，决策者可以拥有更多与不同风险和利润估计相对应的替代项目组合。由于这些标准相互矛盾，因此在选择具有较高估计利润的替代方案时，决策者会采用具有较高估计风险的策略，反之亦然。结论：本文将项目组合决策问题简化为0-1背包约束的多目标优化问题。算法研究证实，孤岛元启发式算法的使用显着提高了遗传算法的性能，从而为财务责任中心管理提供了有效的工具。由于这些标准相互矛盾，因此在选择具有较高估计利润的替代方案时，决策者会采用具有较高估计风险的策略，反之亦然。结论：本文将项目组合决策问题简化为0-1背包约束的多目标优化问题。算法研究证实，孤岛元启发式算法的使用显着提高了遗传算法的性能，从而为财务责任中心管理提供了有效的工具。由于这些标准相互矛盾，因此在选择具有较高估计利润的替代方案时，决策者会采用具有较高估计风险的策略，反之亦然。结论：本文将项目组合决策问题简化为0-1背包约束的多目标优化问题。算法研究证实，孤岛元启发式算法的使用显着提高了遗传算法的性能，从而为财务责任中心管理提供了有效的工具。项目组合决策问题被简化为0-1背包约束的多目标优化问题。算法研究证实，孤岛元启发式算法的使用显着提高了遗传算法的性能，从而为财务责任中心管理提供了有效的工具。项目组合决策问题被简化为0-1背包约束的多目标优化问题。算法研究证实，孤岛元启发式算法的使用显着提高了遗传算法的性能，从而为财务责任中心管理提供了有效的工具。