Partner selection model for design chain collaboration

Chen, Jiaying; Chiang, David Ming-Huang; Guo, Ruey‐Shan

doi:10.1080/00207543.2012.678404

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…The incentive mechanism of the owner also plays a key role in promoting cooperation with the contractor [29]. For mega projects, multi-party cooperation is even more significant; because of the greater uncertainty, technical complexity, strict requirements on quality, and time limits in the construction process, it is necessary to cooperate with various functional units, such as exploration, design, consulting, construction, and supervision [30]. Because the objectives of project participants are, to some extent, consistent, the parties involved in the construction process can realize benefits and achieve risk sharing through cooperation, thereby contributing to the realization of project objectives.…”

Section: Literature Reviewmentioning

confidence: 99%

Contractor Cooperation Mechanism and Evolution of the Green Supply Chain in Mega Projects

et al. 2018

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The large scale of construction in mega projects leads to significant environmental and socioeconomic impacts; thus, the projects should also exhibit greater social responsibility. Adopting green supply chain management in the construction process is an important way to realize the goal of sustainable development of mega projects. Because the green supply chain behavior during construction is mainly demonstrated by contractors, it is especially important to study the evolutionary trend of their behavior. Thus, to explore the cooperative relationship among contractors, this paper considers a lengthy construction period, multi-agent participation dynamics, and opportunistic behavior—all are key features of mega projects—and establishes an evolutionary game model. Specifically, a replicator dynamic equation is used to describe the long-term effects of the contractor’s decisions. Equilibrium determinants are then analyzed and simulated. The results show that the initial probabilities of the two types of contractors (main contractor and subcontractor) demonstrating opportunistic behaviors have a significant effect on the direction of evolution of the mega project. The main contractor, who dominates the subsidy allocation by the government and project owner, should pay attention to maintaining a balance in the income distribution between him or her and the subcontractor. Additionally, there is an optimal distribution coefficient for the subsidy that minimizes the overall probability of opportunistic behavior. This paper provides a point of reference for the decisions of the main contractor and the subcontractor in the green supply chain of mega projects.

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Section: Literature Reviewmentioning

confidence: 99%

Contractor Cooperation Mechanism and Evolution of the Green Supply Chain in Mega Projects

et al. 2018

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“…Generally, the methods can be grouped into four categories (Niu, Ong, and Nee 2012): exact algorithm (Ip, Yung, and Wang 2004;Zeng, Li, and Zhu 2006), mathematical modelling and programming (Chan et al 2008;Chen, Chiang, and Guo 2013;Jarimo and Salo 2009), fuzzy decision-making and multi-attributive decision-making algorithms (Crispim and De Sousa 2009;Ye and Li 2009), and heuristic and meta-heuristic algorithms (Crispim and De Sousa 2010;Wang, Xu, and Zhan 2009). The methods grouped in first two categories can be used to efficiently solve the small or mediumsized problems but cannot provide satisfactory solutions in a reasonable computational time for large problems.…”

Section: Introductionmentioning

confidence: 99%

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

Xiao

Liu

Huang

et al. 2013

International Journal of Production Research

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The selection of cooperative partners is one of the most important factors when creating a virtual enterprise, and has been extensively researched in recent years. It is difficult to solve the selection problem using the traditional optimisation methods because the simultaneous consideration of many aspects of the problem is required, such as the running cost, the reaction time and the running risk. In this paper, an adaptive quantum swarm evolutionary algorithm with time-varying acceleration coefficients is proposed for the partner selection optimisation problem. Simulation and experimental results demonstrate that the improved algorithm is valid and outperforms other evolutionary algorithms.

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“…A strategic alliance may also refer to “collaborative efforts between two or more firms in which they pool their resources to achieve mutually compatible goals that they could not achieve easily alone” [3]. A strategic alliance, has various benefits including access to valuable resources [2], capability to rapidly develop high quality products, reduced time to market [4], learning new technologies and trends [1]. In strategic alliance, partners pool their strengths and resources together to achieve their respective goals.…”

Section: Introductionmentioning

confidence: 99%

A bi-objective game-theoretic model for collaboration formation between software development firms

2019

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Requirement for formation of collaborations has been on increase for the software development industry, especially for smaller to medium sized firms, due to rapid technological advancements, requirements for diversified skills, ever enhancing demands for innovation and fierce competition. Collaborative product development in an alliance enables the firms to benefit from each other’s diversified skills and the experience as a result of which they can develop products more rapidly and of better quality as well resulting in a higher payoff. Also, the development costs decrease. However, to avoid undesired results, selection of an appropriate partner firm for collaboration is of utmost importance keeping in view the objectives of alliance formation of both the strategic partners. One-way partner selection techniques available in the literature are impractical as they enable a firm to rank potential partners only from its own perspective while ignoring their objectives. This problem is addressed by the two-way partner selection techniques, however, they either ignore the payoff distribution criteria or the proposed criteria is unfair. More importantly, existing techniques consider that firm collaborate only with the objective to enhance their financial payoff which might not always be the case. The fact that collaborating firms may have one but different objectives for collaboration, or, each may have multiple objectives is largely neglected. To address the scenarios in which firms may collaborate due to multiple and possibly different objectives, this work proposes a bi-objective game-theoretic model that enables a firm to select an appropriate partner based on the individual preferences of both on the following two objectives: 1) learning and 2) financial revenue. Moreover, this model calculates the pay-off that each firm should get whether only monetary, only in the form of learning or both. The calculation of payoff share is based on the following parameters: 1) individual goals of collaboration of partner selecting firms on the said two objectives, 2) their level of cost contribution, 3) cooperation ratio and 4) knowledge investment difference. Comprehensive analysis of various scenarios is done for the proposed Nash Bargaining payoff distribution model to find the optimum strategy of collaborating firms for each scenario.

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Partner selection model for design chain collaboration

Cited by 12 publications

References 32 publications

Contractor Cooperation Mechanism and Evolution of the Green Supply Chain in Mega Projects

Contractor Cooperation Mechanism and Evolution of the Green Supply Chain in Mega Projects

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

A bi-objective game-theoretic model for collaboration formation between software development firms

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