Virtual Enterprise Partner Selection Research Articles

RETRACTED ARTICLE: Statement of Retraction: “A hybrid technique for partner selection in virtual enterprises”

This article refers to:RETRACTED ARTICLE: A hybrid technique for partner selection in virtual enterprises

Modelling and group decision-making method for virtual enterprise partner selection with fuzzy completion time and due date

Modelling and group decision-making method for virtual enterprise partner selection with fuzzy completion time and due date

Modelling and group decision-making method for virtual enterprise partner selection with fuzzy completion time and due date

Modelling and group decision-making method for virtual enterprise partner selection with fuzzy completion time and due date

Partner Selection of Virtual Enterprise by Improved PSO with Dynamic Inertia Weight for Iterations

PSO is easy to fall into local optimum, and has the defects of low convergence accuracy and difficult convergence. In order to solve this defect and improve the accuracy of the algorithm, the modified PSO algorithm by Geng is applied to solve virtual enterprise partner selection with normalization, by changing dynamically inertia weight for the number of iterations. Furthermore, for the particles with small fitness, a large inertia weight is set to accelerate the approach of particles like the optimal solution region. Finally, results of numerical example show that the search speed of improved PSO is faster than that of standard PSO, which indicates that adding dynamic inertia weight can greatly shorten the search time of the algorithm and accelerate the convergence.

A hybrid technique for partner selection in virtual enterprises

Globally, businesses are increasingly depending on other businesses’ competencies to meet the increasing demands from customers. Enterprises collaborate with a view of sharing their expertise, costs and risks. Many studies have examined this subject with only a few proposing techniques that can facilitate decision-makers’ abilities to filter out inefficient partners among the many that show interest to be part of the consortium, prior to the selection process. Also, few techniques are available that can aid the selection process when the selection criteria are voluminous and uncertain in nature. Research dealing with this subject focusing on the construction industry is hardly available. A Fuzzy Analytical Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution (FAHP-TOPSIS) technique, a hybrid of FAHP and F-TOPSIS, is designed to address this problem. This technique is embedded to Data Envelopment Analysis used to filter out inefficient partners at initial stages. FAHP determines decision criteria weights, while F-TOPSIS evaluates the unfiltered partners. A comparison analysis of FAHP-TOPSIS with FAHP and F-TOPSIS in the same case, show similar results confirming their robustness. However, FAHP-TOPSIS gives the most consistent outcomes. It is proposed that FAHP-TOPSIS can be used on a wide range of partner selection decision problems.

Interval neutrosophic preference relations and their application in virtual enterprise partner selection

How to select satisfactory partners is essential to virtual enterprise and has attracted lots of attention from practitioners and researchers. In many real situations, preference relation is an important structure in representing decision makers’ preference information during the partner selection process. As a special case of neutrosophic sets, interval neutrosophic set (INS) can be used to handle uncertain and inconsistent information in decision making. To show the application, this paper introduces the concept of interval neutrosophic preference relations (INPRs) using interval neutrosophic numbers to denote the true, indeterminacy and false judgments independently. Then, a multiplicative consistency concept for INPRs is proposed to guarantee the ranking accurately. After that, several multiplicative consistency-based nonlinear programming models to derive multiplicatively consistent INPRs and to determine missing values in incomplete INPRs are constructed, respectively. To broaden the application of INPRs, a consensus index based on the distance measure is defined. Meanwhile, an algorithm to group decision making based on INPRs is developed, which can be applied to address incomplete and inconsistent INPRs. Finally, the feasibility and practicability of the developed approach is manifested through an illustrative example, and comparison analysis is performed with several related previous methods about decision making with INSs.

Partner selection and performance evaluation framework for a construction-related virtual enterprise: a multi-agent systems approach

ABSTRACTTo exploit market opportunities, disparate enterprises can pool their core competencies to form a temporary organisation. This inter-organisational collaboration of enterprises is commonly referred to as a virtual enterprise (VE). However, the VEs’ partner selection problems influence their overall performance. Due to the limited construction-specific studies on the evaluation and selection of partners, the current research sought to address this knowledge gap by investigating the partner selection problem. The partners comprising 10 construction companies carrying out project tasks for a large building in Nairobi, Kenya, were evaluated. Data were collected using a sequential mixed-methods approach consisting of a focus group interview, questionnaires and algorithms. The study proposed the use of the partner selection and performance evaluation technique. This technique combines fuzzy approximate reasoning with the conventional analytic hierarchy process algorithm, designed to deal with imprecise evaluators’ judgments. A multi-agent systems approach was chosen to simulate VEs. Results show that the chosen techniques are both efficient and effective. In particular, the reduced group fuzzy analytic hierarchy process reduces the number of pairwise comparisons required when comparing a large number of attributes. Application to the construction management domain, in particular, is germane to the research.

Virtual enterprise partner selection integrating LINMAP and TOPSIS

Virtual enterprise (VE) has become an ever-increasing trend in today’s highly competitive markets. A more scientific decision-making process for selecting partner is very important during the formation phase of VE. Partner selection is formulated as a type of fuzzy hybrid multi-criteria group decision-making problems with fuzzy truth degrees of alternatives’ comparisons represented as trapezoidal fuzzy numbers (TrFNs). Integrating Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), we use the relative closeness degrees to define fuzzy consistency and inconsistency indices. The decision makers’ weight vector is derived by using the relative entropy. Criteria weights are estimated through constructing a new fuzzy linear programming model with TrFNs, which is solved by the developed fast and efficient method. Collective ranking matrix of alternatives is generated through constructing multi-objective assignment model. Example analysis demonstrates the effectiveness of the proposed method.

A novel method for group decision making with interval-valued Atanassov intuitionistic fuzzy preference relations

This paper investigates the group decision making (GDM) problems with interval-valued Atanassov intuitionistic fuzzy preference relations (IV-AIFPRs) and develops a novel method for solving such problems. A new consistency index of an Atanassov intuitionistic fuzzy preference relation (AIFPR) is introduced to judge the consistency of an AIFPR and then a convergent iterative Algorithm I is designed to repair the consistency of an AIFPR with unacceptable consistency. Subsequently, the consistency and acceptable consistency of an IV-AIFPR are defined through separating an IV-AIFPR into two AIFPRs. Based on Algorithm I, a new iterative Algorithm II is devised to repair the consistency of an IV-AIFPR with unacceptable consistency. Afterwards, to determine decision makers’ (DMs’) weights objectively, an optimization model is established by minimizing the deviations between each individual IV-AIFPR and the collective one. This model is skillfully transformed into a linear goal program to resolve sufficiently considering different principles of decision making. A linear program is built to derive interval-valued Atanassov intuitionistic fuzzy (IVAIF) priority weights of alternatives. Then, a TOPSIS (technique for order performance by similarity to an ideal solution) based approach is proposed to rank such IVAIF priority weights. Thereby, a method for GDM with IV-AIFPRs is put forward. At length, a practical example of a virtual enterprise partner selection is provided to illustrate the feasibility and validity of the proposed method.

An improved gravitational search algorithm for green partner selection in virtual enterprises

With the increasing public consciousness in environmental protection, the green partner selection problem (G-PSP) is an important issue in virtual enterprises. In this paper, the green criterion is introduced to partner selection problem (PSP), and a green partner selection model based on six criteria is proposed. As PSP has been proven to be an NP problem, and G-PSP cannot be solved in reasonable time by traditional methods. In this paper, an improved algorithm I-GSA/PSO that combines gravitational search algorithm and particle swarm optimization is developed to solve G-PSP in virtual enterprises. Experimental results show that I-GSA/PSO is effective and outperforms other evolutionary algorithms in solving G-PSP.

Design of a Customer's Type Based Algorithm for Partner Selection Problem of Virtual Enterprise

Virtual Enterprise (VE) is a temporary platform for individual enterprises to collaborate with each other, sharing their core competencies to fulfill a customer demand. In order to improve the customer satisfaction, the most successful VEs select their consortium's members based on customer's preferences. There is quite extensive literature in the field of partner selection in VE, each proposing a new approach to evaluate and select the most appropriate partners among pool of enterprises. However, none of the studies in literature recommend which partner selection methodology should be used in each project with a particular customer attitude. In this study an algorithm is proposed which classifies the customers into three categories; passive, standard and assertive. Three different approaches; Fuzzy Logic- FAHP TOPSIS and Goal programming are used for each customer type respectively. This classification is beneficial since the problem's characteristics; such as vagueness of data, change as the customer's attitude varies. The results certify that, adopting this algorithm not only helps the VE to select the most appropriate partners based on customer preferences, but also the model adapts itself to each customer's attitude. As a result, the overall system flexibility is significantly improved.

The Selection and Evaluation of Manufacturing Partner Based on AHP-FUZZY and Genetic Algorithm

The essence of virtual enterprise partner selection in agile manufacturing is optimization problem. Aiming at the optimization selection feature of virtual enterprise manufacturing partner, a qualitative and quantitative combination method is proposed to solve the problem of partner selection by combination of AHP-FUZZY and genetic algorithm. At last, a practical example will be used to demonstrate.

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.

Partner selection in a manufacturing virtual enterprise based on intuitionistic fuzzy sets

In this paper, a new method based on intuitionistic fuzzy sets is proposed to solve the partner selection problem in a manufacturing virtual enterprise. Based on the concept of average delivery time satisfaction degree, the formulated partner selection problem is interpreted so as to maximise the score of average delivery time satisfaction degree. The model takes into account the factors of average delivery time satisfaction degree, due date, cost and the precedence of tasks. To solve the problem, an improved particle swarm optimisation (PSO) algorithm is proposed. Finally, the simulation of a numerical example and comparisons with the standard PSO algorithm demonstrate that the improved PSO algorithm can effectively improve the searching quality, and the method is effective.

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

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.

Research on Partner Selection in Virtual Enterprise Based on AHP and DEA

Research on Partner Selection in Virtual Enterprise Based on AHP and DEA

Green partner selection in virtual enterprise based on Pareto genetic algorithms

The partner selection problem (PSP) in virtual enterprise has been comprehensively investigated from the aspects of research fields, contents, attributes or criteria been considered, and algorithms. With the consideration of environmental protection, the importance of “green criteria” in PSP is introduced, and two new green criteria, i.e., carbon emission and lead content in manufacturing production, are firstly brought into PSP. A formulation of PSP with green criteria is established which includes four objectives and six constraints. A new improved algorithm, named Pareto genetic algorithm for PSP (Pareto-PSGA), is designed for addressing the specific PSP. With Pareto solution ideas, vector encoding, random selection, two-point crossover, and single-point mutation for Pareto solutions are designed in the Pareto-PSGA. Experimental results demonstrate that compared with other typical intelligent algorithms such as simulated annealing and particle swarm optimization, Pareto-PSGA shows high performance in solving the specific PSP with more suitable Pareto solutions in shorter time.

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

Increasing global competition drives enterprises, especially small and medium-sized enterprises, to collaborate in order to respond faster to customers’ needs, reduce operating costs, increase capacity, and produce customised products to reach the market quicker. A virtual enterprise (VE) is an important manufacturing paradigm to address this trend in the dynamic global economy. Partner selection is a key issue tightly coupled to the success of a VE coalition, and because of its complexity, it is considered a multi-attribute optimisation problem. In this paper, an enhanced ant colony optimiser (ACO) is proposed to address the partner selection problem. Five attributes (namely, cost, time, quality, reputation, and risk) considering both qualitative and quantitative aspects have been investigated to evaluate the candidate partners. Experiments have been conducted to validate the performance of the enhanced ACO algorithm, and the results show that the enhanced ACO algorithm can produce better results in terms of search accuracy and computing time.

An approach to partner selection in virtual enterprises based on grey relational analysis

Virtual enterprise (VE) is a new type of cooperation mode and has attracted considerable attention in the global manufacturing environment. Success of the VE is highly dependent on participators, and thus selection of partners becomes a crucial issue. Partner selection can be considered as a multi-attribute decision-making problem involving variable criteria and a large number of alternatives. However, decision makers are usually uncertain of their preferences owing to changeable types of criteria and incomplete information on candidates. Therefore, an integrated approach is presented in this paper. First, a decision matrix is flexibly established according to the requirements, and then attribute weights of the decision matrix are calculated using the combination weighting method. Subsequently, the preference-order of candidates is provided to enable decision makers to choose suitable partners using grey relational analysis. Finally, an illustrative example is used to explain an application of the proposed approach.

A novel swarm optimisation approach for risk-based partner selection in virtual enterprises

Selection of right partners is vital for virtual enterprise (VE) to take the advantage of market opportunity. Partner selection problem belongs to combinatorial optimisation category and known as NP-hard. A modified discrete particle swarm optimisation (PSO) algorithm is proposed, in this work, to select alliances in a VE. The velocity terms, which is commonly used in the standard PSO algorithm, is avoided; rather, the concept of proportional likelihood with modifications, a method used in data mining techniques, is adopted to improve solution quality. A set of experiments have been conducted and results indicate PSO algorithm is an effective and efficient way to solve the partner selection problems with precedence, risk of failure and due date constraints. A design of experiment (DoE) approach is adopted to study statistical significance of various model parameters on solution quality.