An integrated framework for the optimization of project risk response plan under resource constraints with genetic algorithm

This chapter illustrates a technique to shorten the time duration using structured method. This is done by considering multiple resource constraints apart from time for the software project. The resource constraints are due to limited availability of resources (hardware, software, people, etc.). The difficulty is to locate minimal duration schedule. This is done by assigning the start time for each activity with the clear representation of precedence among them and resources available. There are various optimization approaches available but authors have selected a genetic algorithm. This method emulates the concept of biological evolution that is based on natural selection. This chapter concludes that additional research is needed in this area to provide better outcomes.

Download Full-text

Applying social network analysis to genetic algorithm in optimizing project risk response decisions

Information Sciences ◽

10.1016/j.ins.2019.10.012 ◽

2020 ◽

Vol 512 ◽

pp. 1024-1042

Author(s):

Lei Wang ◽

Tao Sun ◽

Chen Qian ◽

Mark Goh ◽

Vikas Kumar Mishra

Keyword(s):

Genetic Algorithm ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Project Risk ◽

Risk Response ◽

Response Decisions

Download Full-text

Optimal Risk Response plan of project risk management

2011 IEEE International Conference on Industrial Engineering and Engineering Management ◽

10.1109/ieem.2011.6118060 ◽

2011 ◽

Cited By ~ 1

Author(s):

Amnon Gonen

Keyword(s):

Risk Management ◽

Project Risk Management ◽

Project Risk ◽

Risk Response ◽

Response Plan

Download Full-text

Pairwise Elicitation for a Decision Support Framework to Develop a Flood Risk Response Plan

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4040661 ◽

2018 ◽

Vol 5 (1) ◽

Author(s):

MiguelAndres Guerra ◽

Yekenalem Abebe

Keyword(s):

Decision Making ◽

Flood Risk ◽

Flood Hazard ◽

Addis Ababa ◽

Pairwise Comparison ◽

Multicriteria Decision Making ◽

Risk Response ◽

Hazard And Risk ◽

Response Plan ◽

Decision Making Framework

There are several ways of quantifying flood hazard. When the scale of the analysis is large, flood hazard simulation for an entire city becomes costly and complicated. The first part of this paper proposes utilizing experience and knowledge of local experts about flood characteristics in the area in order to come up with a first-level flood hazard and risk zoning maps, by implementing overlay operations in Arc GIS. In this step, the authors use the concept of pairwise comparison to eliminate the need for carrying out a complicated simulation to quantify flood hazard and risk. The process begins with identifying the main factors that contribute to flooding in a particular area. Pairwise comparison was used to elicit knowledge from local experts and assigned weights for each factor to reflect their relative importance toward flood hazard and risk. In the second part of this paper, the authors present a decision-making framework to support a flood risk response plan. Once the highest risk zones have been identified, a city can develop a risk response plan, for which this paper presents a decision-making framework to select an effective set of alternatives. The framework integrates tools from multicriteria decision-making, charrette design process to guide the pairwise elicitation, and a cost-effective analysis to include the limited budget constraint for any city. The theoretical framework uses the city of Addis Ababa for the first part of the paper. For the second part, the paper utilizes a hypothetical case of Addis Ababa and a mock city infrastructure department to illustrate the implementation of the framework.

Download Full-text

Budget allocation decisions for project risk response

Kybernetes ◽

10.1108/k-03-2020-0188 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yao Zhang ◽

Xin Guan

Keyword(s):

Optimization Model ◽

Fault Tree ◽

Critical Issue ◽

Inadequate Response ◽

Expected Loss ◽

Project Risk ◽

Content Type ◽

Risk Response ◽

Total Budget ◽

Risk Protection

Purpose The purpose of this paper is to propose a method integrating fault tree analysis and optimization model to allocate response budget from the preventive and protective perspectives. Design/methodology/approach The proposed method consists of two main steps. The first step is to analyze and calculate the probability and the loss of the risk. The second step is to build an optimization model for allocating response budget. Findings First, there exists an optimal response budget. Second, risk protection is preferred to risk prevention when the total budget is limited. Third, the protective budget should be first invested for the consequence event with greatest expected loss. Fourth, the preventive budget should be first allocated to the risk cause with highest occurrence probability that belongs to the OR set in the fault tree. Practical implications Managerially, our results indicate that project managers (PMs) should make a tradeoff between the budget invested for risk response and reduced expected loss of the risk. Then, in the case of inadequate response budget, PMs should pay more attention to risk protection and cope with the event that can cause severe loss. In addition, under this circumstance, PMs had to better allocate the risk preventive budget in proper order. Originality/value Project risk response is a critical issue in project risk management as PMs can take actions actively to cope with project risks in this phase. Effective risk response, in general, requires financial support in practice, and reasonable allocation of the total budget among risk response strategies can produce better response effects.

Download Full-text

An Improved Genetic Algorithm for Emergency Decision Making under Resource Constraints Based on Prospect Theory

Algorithms ◽

10.3390/a12020043 ◽

2019 ◽

Vol 12 (2) ◽

pp. 43 ◽

Cited By ~ 4

Author(s):

Leiwen Chen ◽

Yingming Wang ◽

Geng Guo

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Prospect Theory ◽

Resource Constraints ◽

Emergency Situation ◽

Practical Significance ◽

Improved Genetic Algorithm ◽

Emergency Decision Making ◽

Emergency Decision ◽

Value Measurement

The study of emergency decision making (EDM) is helpful to reduce the difficulty of decision making and improve the efficiency of decision makers (DMs). The purpose of this paper is to propose an innovative genetic algorithm for emergency decision making under resource constraints. Firstly, this paper analyzes the emergency situation under resource constraints, and then, according to the prospect theory (PT), we further propose an improved value measurement function and an emergency loss levels weighting algorithm. Secondly, we assign weights for all emergency locations using the best–worst method (BWM). Then, an improved genetic algorithm (GA) based on prospect theory (PT) is established to solve the problem of emergency resource allocation between multiple emergency locations under resource constraints. Finally, the analyses of example show that the algorithm can shorten the decision-making time and provide a better decision scheme, which has certain practical significance.

Download Full-text