Two-level Tabu-predatory search for schedule risk control of IT outsourcing projects

Lu, Fuqiang; Zhu, Wenjing; Bi, Hualing; Huang, Min; Chen, Weidong; Zhao, Yong

doi:10.1016/j.ins.2019.02.061

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…Yildiz et al (2014) developed a knowledge-based cost risk mapping tool that considers the causality among interdependent cost overrun risk factors for the identification of risk paths and assessment of risk levels. For project schedule risk, Lu et al (2019) considered the cooperative relationships between the client and vendors to propose a two-level schedule risk control model consisting of minimizing the schedule risk and maximizing the benefit of vendors. Ballesteros-p erez et al (2019) stressed the importance of activity sensitivity to determine which activities could increase the risk of a project schedule delay and proposed a new method to evaluate the performance of activity sensitivity metrics.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of multiproject resource conflict risk via an artificial neural network

Bai

Wang

et al. 2020

ECAM

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PurposeInadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk.Design/methodology/approachA fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk.FindingsAccurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately.Originality/valueThe authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of multiproject resource conflict risk via an artificial neural network

Bai

Wang

et al. 2020

ECAM

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“…Outsourcing in information technology has increased dramatically. Therefore, risk control in this area has been considered more than other areas (Lu et al, 2019). Given that the studied organization is engaged in outsourcing other companies, therefore, studying outsourcing can control the risks in different parts of outsourcing projects.…”

Section: Introductionmentioning

confidence: 99%

A model for key success factors for outsourcing in project-based companies: integrating soft and dynamic approaches

Hoseini

Nikabadi

2021

JFM

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Purpose The purpose of this study is to achieve the dynamic model of outsourcing success factors in project-based companies. Design/methodology/approach This study is descriptive-survey in terms of method and practical in terms of purpose. To achieve the dynamic model of outsourcing success, 1,000 outsourcing articles published in high-status journals from 2017 to 2019 were first text-mining. Then, using the clustering technique, the factors affecting the success of outsourcing were obtained. To achieve the key variables, the variables obtained by interpretive structural modeling (ISM) were then leveled. Then, the strategic options development and analysis (SODA) technique has been used to achieve a consensus and coordination on factors relationships. Finally, the dynamic model of outsourcing success in GHODRAT CONTROL PARS Company has been modeled and implemented. Findings In total, five clusters and nine factors were extracted (strategy, management, performance, market, R&D, supplier, product, organizational data and outsourcing findings). In central and domain analysis, two factors, “Strategy” and “R and D,” were recognized as factors that have the most interaction and centrality. The result of the dynamic model indicate that the organization will significantly reduce the construction time of the power plant by improving the “R and D” factor. Originality/value In this study, various techniques have been combined. Therefore, one of the aspects of innovation in the present study is the combination of methods that have not been used earlier. The second aspect of this study’s innovation is using SODA technique to design the dynamic model of outsourcing success factors. Given that the scope of this study is the component affecting the success of outsourcing, so extensive research has been conducted in the field of articles worked in the field of outsourcing to get a comprehensive view of the components affecting the success of outsourcing, which has not been reviewed in other articles. In this study, in addition to identifying the effective factors, their identified and also how these variables affect the successful performance of outsourcing in the form of a dynamic model, and then analyzed.

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4PL routing problem using hybrid beetle swarm optimization

Chen

Feng

et al. 2023

Soft Comput

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Considering the uncertainty of transportation time and cost due to seasonality and human factors, A multi-objective chance constrained programming model with minimum transportation time and cost was established. According to the characteristics of the problem, the beetle search algorithm with one beetle is changed into multiple beetles and Dijkstra algorithm is embedded, a hybrid beetle swarm optimization algorithm (HBSO) is designed to solve the problem, and the case analysis and algorithm analysis are made for three different examples. By adjusting the model parameters, the minimum objective function value under the combination of parameters is obtained. Under the three examples, the influence of the customer on the time and cost is different, and the impact on the whole logistics and transportation scheme is obtained. The Taguchi test was used to determine the optimal parameter combination of the HBSO under different node scales. The GA and PSO are embedded with Dijkstra algorithm, and are compared with HBSO to verify the optimization ability of the HBSO.

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Two-level Tabu-predatory search for schedule risk control of IT outsourcing projects

Cited by 8 publications

References 35 publications

Prediction of multiproject resource conflict risk via an artificial neural network

Prediction of multiproject resource conflict risk via an artificial neural network

A model for key success factors for outsourcing in project-based companies: integrating soft and dynamic approaches

4PL routing problem using hybrid beetle swarm optimization

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