A fuzzy strategic alliance selection framework for supply chain partnering under limited evaluation resources

Puente

et al. 2008

Concurrent Engineering

The considerable amount of uncertainty involved in defining the factors that affect reverse logistics (RL) decision-making and the complex interrelationships between those factors make it rather difficult to decide what recovery policy a business should pursue. This article proposes a fuzzy system that helps in such decision-making and thereby mitigates these difficulties. The knowledge related to the decision is incorporated into the system by means of conditional rules, which serve to provide the ideal recovery policy for each particular case. The model proposed is applied to the analysis of a number of examples and proves to be a versatile tool that provides coherent results. These characteristics could be of critical importance especially in the point of entry into the RL pipeline and in the centralized return centres.

Section: Justification Of the Fuzzy Methodology In Rl Contextmentioning

confidence: 99%

A Decision-Making Support System on a Products Recovery Management Framework. A Fuzzy Approach

Puente

et al. 2008

Concurrent Engineering

Production Planning & Control

“…Approaches that have tackled this issue (e.g. Lin and Chen, 2004;Wu and Barnes, 2010) can be shown to have shortcomings. This is a significant gap in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To date, only limited attention has been given to partner selection criteria construction which is an essential pre-requisite in any selection process (Wu and Barnes, 2011). Lin and Chen (2004) applied Dempster-Shafer theory when constructing a partner selection criteria hierarchy within their strategic alliance selection model. However, their main focus is on the strategic alliance selection approach rather than the partner selection criteria construction, which makes their model hard to apply by supply chain managers in practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Partner selection in green supply chains using PSO – a practical approach

Barnes

2016

Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected prerequisite in the partner selection process. This paper proposes a three-stage model that combines Dempster-Shafer belief acceptability theory and particle swarm optimization technique for the first time in this application. This enables optimization of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer.

Production Planning & Control

“…Lin and Chen (2004) propose a method for developing industry specific criteria based on a set of general criteria, whilst Wu and Barnes (2010) use Dempster-Shafer theory and optimisation to develop a model for formulating criteria in ASCs.…”

Section: Decision Models For Formulation Of Criteriamentioning

confidence: 99%

Partner selection in agile supply chains: a fuzzy intelligent approach

Barnes

2013

-2 - Partner selection in agile supply chains: a fuzzy intelligent approachPartner selection is a fundamental issue in supply chain management as it contributes significantly to overall supply chain performance. However, such decision making is problematic due to the need to consider both tangible and intangible factors, which cause vagueness, ambiguity and complexity. This paper proposes a new fuzzy intelligent approach for partner selection in agile supply chains (ASC) by using fuzzy set theory in combination with radial basis function artificial neural network. Using these two approaches in combination enables the model to classify potential partners in the qualification phase of partner selection efficiently and effectively using very large amounts of both qualitative and quantitative data. The paper includes a worked empirical application of the model with data from eighty-four representative companies within the Chinese electrical components and equipment industry, to demonstrate its suitability for helping organizational decision makers in partner selection.