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The purpose of this paper is to examine the impact of the target and bidder reference points on the method of payment in mergers. When considering initial and final results for target and bidder, the target appears to have more negotiating power than the bidder in achieving the financial mix preference that was initially articulated.

The authors examine the impact of target and bidder reference points on the consideration sought by the target and the consideration offered by the bidder. The authors test whether the target reference points has an impact on the final method of payment agreed upon by the target and the bidder.

The authors find that targets with a longer distance to their respective target reference points prefer to receive cash financing in the consideration sought, while bidders with a longer distance to their respective reference points prefer stock financing in consideration offered. The authors also find evidence that target’s longer distance to its reference point is associated with the use of cash over stock, while the bidder reference point has no impact on the final method of payment used in the merger.

These insights may be used by the management to formulate the optimal mix of financing in M&A transactions.

This is an original paper exploring the effect of behavioral finance on corporate decision making.

The activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.

Considering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.

The empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.

The buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.

This paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.

A capacity constraint buffer monitoring method suitable for a multi-project environment is produced.

Profile measurement with boundary information plays a vital role in the detection of quality in the assembly of aviation parts. The purpose of this paper is to improve the evaluation accuracy of the aerodynamic shapes of airplanes, the profiles of large-sized parts need to be measured accurately.

In this paper, an accurate profile measurement method based on boundary reference points is proposed for the industrial stereo-vision system. Based on the boundary-reference points, the authors established a priori constraint for extracting the boundary of the measured part. Combining with the image features of background and the measured part, an image-edge compensation model is established to extract the boundary of the measured part. The critical point of a laser stripe on the edge of the measured part is extracted corresponding to the boundary constraint. Finally, as per the principle of binocular vision, the profile of the measured part is reconstructed.

Laboratory experiments validate the measurement accuracy of the proposed method which is 0.33 mm. In the analysis of results between the measured data and the theoretical model, the measuring accuracy of the proposed method was found to be significantly higher than that of the other traditional methods.

An aviation part was measured in the part-assembly shop by the proposed method, which verified the feasibility and effectiveness of this method. The research can realize the measurement of smooth surface boundary which can solve existing profile reconstruction problems for aviation parts.

According to the two-dimensional contour constraint, critical points of the laser strip sequence at the edge of measured part are extracted and the accurate profile reconstruction with the boundary is realized.

The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary.

Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value.

The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model.

This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.

Straightness measurement of rail weld joint is of essential importance to railway maintenance. Due to the lack of efficient measurement equipment, there has been limited in-depth research on rail weld joint with a 5-m wavelength range, leaving a significant knowledge gap in this field.

In this study, the authors used the well-established inertial reference method (IR-method), and the state-of-the-art multi-point chord reference method (MCR-method). Two methods have been applied in different types of rail straightness measurement trollies, respectively. These instruments were tested in a high-speed rail section within a certain region of China. The test results were ultimately validated through using traditional straightedge and feeler gauge methods as reference data to evaluate the rail weld joint straightness within the 5-m wavelength range.

The research reveals that IR-method and MCR-method produce reasonably similar measurement results for wavelengths below 1 m. However, MCR-method outperforms IR-method in terms of accuracy for wavelengths exceeding 3 m. Furthermore, it was observed that IR-method, while operating at a slower speed, carries the risk of derailing and is incapable of detecting rail weld joints and low joints within the track.

The research compare two methods’ measurement effects in a longer wavelength range and demonstrate the superiority of MCR-method.

The purpose of this paper is to report an efficient decision‐support system for industrial robot selection. It seeks to analyze potential robot selection attributes with a relatively new MCDM approach which employs grey set theory coupled with MULTIMOORA method.

Use of interval‐valued grey numbers (IVGN) adapted from grey theory has been explored to tackle subjective evaluation information collected from an expert group; finally MULTIMOORA (multi‐objective optimization by ratio analysis) method has been applied in order to aggregate individual criterion/attribute scores into an equivalent evaluation index towards evaluating feasible ranking order of candidate alternative robots.

An empirical study has also been shown here for better understanding of the said selection‐module; effectively applicable to any other decision‐making scenarios.

This method is computationally very simple, easily comprehensible, and robust which can simultaneously consider numerous subjective attributes. Grey MULTIMOORA ranking is expected to provide a good guidance to the managers of an organization to select the feasible robot. It will also provide a good insight to the robot manufacturer so that it can improve its product or introduce a new product to satisfy customer needs.

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues.

The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains.

The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives.

Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

The primitive purpose of this manuscript is to develop an effective and efficient computer numerical control (CNC) machine tool evaluation index from the perspective of appraisal and benchmarking of preferred candidate machine tool in subjective information scenario. In this reporting, manager has been facilitated from the decision making tool and methodology in order to evaluate the best one and benchmarking the preferred candidate alternative machine tool under the subjective criterion circumstances.

A MULTI-MOORA (multi-objective optimization by ratio analysis) methodology conjunction with grey number has fruitfully applied in evaluated subjective information against criterion module plate form from the prospectus of handling the vagueness, inconsistency, impreciseness and in order to appraisal and benchmarking of the candidate CNC machine tool alternatives.

In today scenario, the subjective evaluation criterion has even ran over the CNC machine tool evaluation module (index) except other valuable area on account of an abatement of consistent data. The authors found out the subjective information is even necessary/mandatory to handling and tackle such an inconsistent, vagueness, impreciseness which associated uncertain criterion. So, the authors found out that the application of grey number conjunction with MULTI-MOORA decision methodology from the prospectus of appraisal and benchmarking of preferred candidate alternatives machine tool.

The major contribution of this manuscript to exploration of grey number set conjunction with MULTI-MOORA methodology toward appraisal and benchmarking of preferred CNC candidate machine tool alternative, handled and tackled the evaluated subjective information from expert panel against subjective criterion environment, facilitates the multi-criterion decision making (MCDM) module from the prospectus of best one and ranking order the candidate machine tool alternative under the similar subjective criterion circumstances.

The application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.

This paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.

The feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.

The research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.

A novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.

The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.