A multi-criteria decision support model for evaluating the performance of partnerships

Partnership is one of the strategies that could help companies increase their competiveness in a global market. Previous studies reported that a high percentage of partnerships fail to achieve their drivers of entering into partnership. The lack of a comprehensive partnership evaluation has been identified as one of the main reasons for partnership failure. In this paper, a multi-criteria decision support model is developed to evaluate the performance of an ongoing partnership in different periods based on the measures associated with the drivers for entering into the partnership. Interpretive Structural Modeling (ISM), Analytical Network Process (ANP) and Fuzzy Logic (FL) are used in order to address the interdependency, the importance of, and the uncertainty in performance measures, respectively. The outputs of the model are the importance of each performance measure and a single number for the overall partnership performance in each period, named as Partnership Performance Index (PPI) here. PPI is different from either mere financial or operational performance measures. PPI is a multi-dimensional measure which includes multiple performance measures associated with the partnership drivers and accounts for their importance and interdependencies. The model is applied to a partnership between a logging company and a sawmill in British Columbia, Canada. PPI is used to evaluate this partnership in three different periods. PPI values are compared to conventional measures for partnership evaluation and the managers confirmed that PPI values better represent the performance of their partnership. The sensitivity of the PPIs is investigated based on the changes in the importance as well as the value of the measures. The rankings from the model are compared to the ones estimated by the managers, and the results showed that the rankings are compatible. This model contributes to the literature by developing an index for partnership performance which captures partnership drivers and performance measures as well as their importance and interdependencies.     

一种新型的作战指挥流程效能评估模型

为建立作战指挥流程效能的评估模型,定义在某一种指挥流程下,指挥信息传递过程中信息流通迅速程度的度量称为指挥流的“时效”,以“时效”的大小作为衡量流程效能的高低。“反馈”是作战指挥流程中常见的情况,根据实际情况确立了“反馈”流程的非线性反馈函数。此方法为优化指挥流程提供了一种科学有效的定量分析途径。     

一个新的信息系统敏捷性度量模型

针对信息系统的敏捷性难以客观量化的问题,基于模糊综合评价法建立了信息系统敏捷性度量模型。此模型面向企业敏捷性,指标的提炼比较深入和全面,具有实用性和可操作性;由于各敏捷性度量指标的边界不清晰,给评分带来一定难度,采用了Vague集评分法,增加了评分的客观性。经实例验证,所建立的模型比较有效。     

A multi-criteria model for the evaluation of business benefits in horizontal collaborative networks

Despite many advantages that could be gained through belonging to enterprise networks, only a few networks continue running businesses after the governments stop their funding. One of the reasons is the lack of a model that evaluates the benefits from the firm’s point of view. The objective of this work is to develop a model that estimates the benefits in horizontal collaborative networks, for a considered business opportunity, and determines the optimal network configuration in terms of the selected enterprises. We propose a method for evaluating the profits for a collaborative network based on a combination of product realisation graph and core competences identification. Through the case study of a Swiss horizontal collaborative network, the proposed approach proves its efficiency in selecting the optimal network of partners and evaluating their corresponding turnover and profits.     

A new model for the hazardous waste location-routing problem

Hazardous waste management involves the collection, transportation, treatment and disposal of hazardous wastes. In this paper a new multiobjective location-routing model is proposed. Our model also includes some constraints, which were observed in the literature but were not incorporated into previous models. The aim of the proposed model is to answer the following questions: where to open treatment centers and with which technologies, where to open disposal centers, how to route different types of hazardous waste to which of the compatible treatment technologies, and how to route waste residues to disposal centers. The model has the objective of minimizing the total cost and the transportation risk. A large-scale implementation of the model in the Central Anatolian region of Turkey is presented.     

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast.The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.     

A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements

Multi-criteria decision making methods (MCDM) have been widely used throughout the last years to assist project contractors in selection processes related to the construction field. Sustainable urban drainage systems (SUDS) are an especially suitable discipline to implement these techniques, since they involve important impacts on each branch of sustainability: economy, environment and society. Considering that pervious pavements constitute an efficient solution to manage urban stormwater runoff as a source control system, this paper presents a multi-criteria approach based on the Integrated Value Model for Sustainable Assessments (MIVES) method to facilitate their proper selection. Given the lack of accurate information to shape the behavior of the alternatives regarding some of the criteria defining the decision-making environment, a series of variables are modeled by executing stochastic simulations based on the Monte Carlo methods. Additionally, a group of ten experts from various sectors related to water management was requested to provide their opinions about the importance of the set of selected criteria, according to the comparison levels of the Analytic Hierarchy Process (AHP). These judgments are converted into triangular fuzzy numbers, in order to capture the vagueness that human attitude entails when making judgments. A case of study in which the three major types of pervious pavements (porous asphalt, porous concrete and interlocking concrete pavers) are evaluated is presented to demonstrate the potential of the model.     

A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems

This study described a hybrid model for supporting the personnel selection process in manufacturing systems. Personnel selection is a very important issue for an effective manufacturing system, since the improper personnel might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, a hybrid model which employs analytic network process (ANP) and modified TOPSIS (Technique for Order Performance by Similarity to Idea Solution) together, is proposed for the personnel selection problem. The ANP is used to analyze the structure of the personnel selection problem and to determine weights of the criteria, and modified TOPSIS method is used to obtain final ranking. To illustrate how the approach is used for the personnel selection problem, an application of a real case in a company is conducted. The application has demonstrated the effectiveness and feasibility of the proposed model. Company management found the application and results satisfactory and implementable in their personnel selection process.     

聚类有效性评价新指标

聚类有效性评价指标分为外部评价指标和内部评价指标两大类。现有外部评价指标没有考虑聚类结果类偏斜现象;现有内部评价指标的聚类有效性检验效果难以得到最佳类簇数。针对现有内外部聚类评价指标的缺陷,提出同时考虑正负类信息的分别基于相依表和样本对的外部评价指标,用于评价任意分布数据集的聚类结果;提出采用方差度量类内紧密度和类间分离度,以类间分离度与类内紧密度之比作为度量指标的内部评价指标。UCI数据集和人工模拟数据集实验测试表明,提出的新内部评价指标能有效发现数据集的真实类簇数;提出的基于相依表和样本对的外部评价指标,可有效评价存在类偏斜与噪音数据的聚类结果。     

A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines

Assembly Lines (ALs) are used for mass production as they offer lots of advantages over other production systems in terms of lead time and cost. The advent of mass customization has forced the manufacturing industries to update to Mixed-Model Assembly Lines (MMALs) but at the cost of increased complexity. In the real world, industries need to determine the sequence of models based on various conflicting performance measures/criteria. This paper investigates the Multi-Criteria Model Sequencing Problem (MC-MSP) using a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm. To address the multiple criteria, a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm was developed by integrating a priori approach with NEH algorithm. Discrete Event Simulation (DES) was used to evaluate each solution. A mathematical model was developed for three criteria: flow time, makespan and idle time. Further, to validate the effectiveness of the proposed SMC-NEH a case study and Taillard's benchmark instances were solved and a Multi-Criteria Decision-Making (MCDM) analysis was performed to compare the performance of the proposed SMC-NEH algorithm with the traditional NEH algorithm and its variants. The results showed that the proposed SMC-NEH algorithm outperformed the others in optimizing the conflicting multi-criteria problem.     

Selecting an optimal model for forecasting the volumes of railway goods transportation

Consideration was given to selection of an optimal model of short-term forecasting of the volumes of railway transport from the historical and exogenous time series. The historical data carry information about the transportation volumes of various goods between pairs of stations. It was assumed that the result of selecting an optimal model depends on the level of aggregation in the types of goods, departure and destination points, and time. Considered were the models of vector autoregression, integrated model of the autoregressive moving average, and a nonparametric model of histogram forecasting. Criteria for comparison of the forecasts on the basis of distances between the errors of model forecasts were proposed. They are used to analyze the models with the aim of determining the admissible requests for forecast, the actual forecast depth included.     

A fuzzy multi-criteria model for the industrial cooperation program transaction strategies: A case in Taiwan

In international trade offset practices (in Taiwan known as industrial cooperation program, ICP) have received increased attention over the past 20 years. In the coming 10 years, the Taiwanese government may expend roughly US$16 billion for purchasing Patriot-III missiles, P-3 long-range anti-submarine planes, and diesel-engine submarines from the United States through foreign military sale, and can achieve US$8 billion ICP credit the largest in Taiwanese history. Offsets or ICP can be regarded as fuzzy multiple criteria decision-making (MCDM) problems, therefore, the fuzziness and uncertainty of subjective perception should be considered. This paper provides an alternative approach, the non-additive fuzzy integral, to deal with the fuzzy MCDM problems especially when there is dependence among considered criteria. The main purpose of this paper is to discuss with Taiwan’s ICP Optimal Offset Transaction Policy and propose a framework of drawing on ICP credit in future. This paper considers the four aspects of policy, ability, economy, and environment, to establish a set of fuzzy AHP multiple criteria decision mode to identify the evaluative criteria variables and project item’s order for ICP project. This decision mode was identified as a workable method.     

Multi-criteria mathematical model for designing the distribution network of a consumer goods company

This paper describes a multiple criteria mixed-integer linear program used for designing the best possible supply chain distribution network for a consumer goods company. The model determines the optimal configuration of the manufacturing plants, distributors and customers in the distribution network. The model is intended for making tactical decisions for designing distribution networks, or more specifically, for designing the flow of products from the manufacturing plants to the customers. The customers have four options for receiving products in this model. Products can be supplied from (1) the regional distribution center (DC), (2) the manufacturing plant, (3) an independent distributor who is supplied from the regional DC, or (4) an independent distributor who is supplied directly from a manufacturing plant. The model selects the best option for each customer/distributor based on several criteria: profit, lead time, power, credit performance, and distributors’ reputation. The model is validated with real data from a consumer goods company to show its functionality. To account for variability in demand, the model is run under multiple scenarios and the results are analyzed to obtain the best solution. The company uses two DC’s located in the same region, but the model assumes only one regional DC with infinite capacity. With the proposed network, we show that one of the regional DC’s could be eliminated and distribution costs would be reduced from 12% to 3% of the Net Sales (approximately a monthly reduction of $574,000 in distribution expenses).     

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

In this study, neural network models improved by genetic algorithm were employed to estimate peak ground acceleration (PGA) at seven metropolitan areas in the island of Taiwan, which is frequently subject to earthquakes. By considering a series of historical seismic records, and using the seismic design value in the current building code as the evaluation criteria, two metropolitan areas, Taichung and Chiayi, were identified by computational results as having higher estimated horizontal PGAs than the recommended design values. The approach implemented in this study provides a new and good basis for solving this type of seismic problems in the region studied.     

Integrating cognitive and collective aspects of work in evaluating technology

Ergonomic criteria are receiving increasing attention from designers but their applications do not ensure that technology matches the user's abilities, needs, and work constraints. In this paper, we used two series of studies conducted in the healthcare system to illustrate how important work analysis is in evaluating new technology in order to identify critical dimensions of work and anticipate the impact of the devices on these dimensions: one looking at a new automatic drug device for anesthetists and one at a new robotic surgery system. Given the results, we developed a methodology for evaluation of new technology in complex systems. The aim is to insist to assess the impacts of technology on all the dimensions of work: technical, performance, cognitive, organizational, and economic. Which dimensions and criteria will be developed more in detail depends on the results of the work analysis.     

A mixed-integer programming model for evaluating distribution configurations in the steel industry

In this paper, a mixed-integer programming model is developed to aid management in the analysis of a distribution problem in the steel industry. In this problem, the company has seven service centers located in the northeastern part of the country. In order to maintain reasonable inventory levels and fast customer service, there is a considerable amount of transshipment between the service centers. The model takes as inputs supply and demand levels, transportation costs, loading/unloading costs and potential routes. The output of the model is a minimum cost distribution schedule. The model allows management to quickly evaluate various distribution scenarios, including the establishment of a central transfer facility.     

An erosion model for evaluating regional land-use scenarios

The conversion to pasture of indigenous forest on New Zealand hill country has led to increased mass-movement erosion and consequently increased sedimentation of waterways. Effective soil conservation requires a model that can evaluate erosion and sedimentation for different land-use scenarios. In this paper, we develop a model of mean sediment discharge related to mean erosion rates through a sediment delivery ratio. Mean erosion rate in a particular terrain (“erosion terrain”) is the product of (i) the square of mean annual rainfall with (ii) a cover factor and (iii) an erosion coefficient that depends on erosion terrain. Measurements of mean sediment discharge are used to estimate erosion coefficients for each erosion terrain. The model can be used to predict mean sediment discharge in response to land-cover/land-use scenarios. It is easy to execute and uses input data readily available in GIS layers in New Zealand. This makes it suitable for widespread management application, in contrast to physically based models which are presently only suitable for research catchments. We demonstrate the utility of the model for three different applications: evaluating land-use scenarios in the Motueka catchment; setting priorities for soil conservation in the Manawatu catchment; and determining national trends in agricultural erosion over a 30-year period. The general methodology is applicable to countries dominated by mountains and steep hills with high erosion rates.     

New methods for evaluating the validity of the results of mathematical computations

From a mathematical standpoint, numerical methods can be divided in two classes: (i) Direct methods, which give an exact result after some finite number of computations, and (ii) approximate methods, which only give an approximate result after any finite number of computations.

When these numerical methods are carried out on a computer, they are projected into discrete space and performed with a limited precision arithmetic. Consequently they yield approximate results. A result given by a direct method contains only a computing error while a result given by an approximate method contains both a computing error and a method error. It is absolutely necessary to evaluate these errors. In this paper, we present new methods for estimating these errors and for evaluating the exact number of significant decimal digits appearing in computed results. Also presented here is a general methodology for improving the precision of computed results.     

An integrated model for evaluating the amount of data required forreliable recognition

Many recognition procedures rely on the consistency of a subset of data features with a hypothesis as the sufficient evidence to the presence of the corresponding object. We analyze here the performance of such procedures, using a probabilistic model, and provide expressions for the sufficient size of such data subsets, that, if consistent, guarantee the validity of the hypotheses with arbitrary confidence. We focus on 2D objects and the affine transformation class, and provide, for the first time, an integrated model which takes into account the shape of the objects involved, the accuracy of the data collected, the clutter present in the scene, the class of the transformations involved, the accuracy of the localization, and the confidence we would like to have in our hypotheses. Increasingly, it turns out that most of these factors can be quantified cumulatively by one parameter, denoted “effective similarity”, which largely determines the sufficient subset size. The analysis is based on representing the class of instances corresponding to a model object and a group of transformations, as members of a metric space, and quantifying the variation of the instances by a metric cover     

New approach for evaluating form errors of engineering surfaces

Manufactured features generally deviate in size, form and relations with respect to other features from the features desired by the designer. Designers specify certain limits for these deviations that depend on functional requirements. The specification of different form errors is dealt with by the International Organization for Standardization (ISO). ISO also specifies that an ideal geometric feature must be established from the actual measurements such that the maximum deviation between it and the actual feature concerned is the least possible value. In practice, the least squares (LS) method is sometimes used for establishing the ideal feature, as this method is based on sound mathematical principles. However, the least squares procedure does not yield a minimum value. Therefore some attempts have been made to arrive at a form error based on the minimum deviation (MD) principles. A stray peak or valley on the actual feature introduces considerable variation in the results obtained by the minimum deviation method. This paper suggests a new approach based on the minimum average deviation (MAD). In this method, the ideal feature is established by using a search technique for different geometric features such as straight lines, circles, planes, cylinders and spheres. Using simple numerical examples, the values obtained by this approach are compared with the values obtained by the least squares and minimum deviation methods.