Shanlin Yang

The group consensus based evidential reasoning approach for multiple attributive group decision analysis

Many multiple attribute decision analysis problems include both quantitative and qualitative attributes with various kinds of uncertainties such as ignorance, fuzziness, interval data, and interval belief degrees. An evidential reasoning (ER) approach developed in the 1990s and in recent years can be used to model these problems. In this paper, the ER approach is extended to group consensus (GC) situations for multiple attributive group decision analysis problems. In order to construct and check the GC, a compatibility measure between two belief structures is developed first. Considering two experts utilities, the compatibility between their assessments is naturally constructed using the compatibility measure. Based on the compatibility between two experts assessments, the GC at a specific level that may be the attribute level, the alternative level, or the global level, can be constructed and reached after the group analysis and discussion within specified times. Under the condition of GC, we conduct a study on the forming of group assessments for alternatives, the achievement of the aggregated utilities of assessment grades, and the properties and procedure of the extended ER approach. An engineering project management software selection problem is solved by the extended ER approach to demonstrate its detailed implementation process, and its validity and applicability.

An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements

With the aim of modeling multiple attribute group decision analysis problems with group consensus (GC) requirements, a GC based evidential reasoning approach and further an attribute weight based feedback model are sequentially developed based on an evidential reasoning (ER) approach. In real situations, however, giving precise (crisp) assessments for alternatives is often too restrictive and difficult for experts, due to incompleteness or lack of information. Experts may also find it difficult to give appropriate assessments on specific attributes, due to limitation or lack of knowledge, experience and provided data about the problem domain. In this paper, an ER based consensus model (ERCM) is proposed to deal with these situations, in which experts’ assessments are interval-valued rather than precise. Correspondingly, predefined interval-valued GC (IGC) requirements need to be reached after group analysis and discussion within specified times. Also, the process of reaching IGC is accelerated by a feedback mechanism including identification rules at three levels, consisting of the attribute, alternative and global levels, and a suggestion rule. Particularly, recommendations on assessments in the suggestion rule are constructed based on recommendations on their lower and upper bounds detected by the identification rule at a specific level. A preferentially developed industry selection problem is solved by the ERCM to demonstrate its detailed implementation process, validity, and applicability.

A group evidential reasoning approach based on expert reliability

The reliability of an expert is an important concept in multiple attribute group decision analysis (MAGDA). However, reliability is rarely considered in MAGDA, or it may be simply assumed that all experts are fully reliable and thus their reliabilities do not need to be considered explicitly. In fact, any experts can only be bounded rational and their various degrees of reliabilities may significantly influence MAGDA results. In this paper, we propose a new method based on the evidential reasoning rule to explicitly measure the reliability of each expert in a group and use expert weights and reliabilities to combine expert assessments. Two sets of assessments, i.e., original assessments and updated assessments provided after group analysis and discussion are taken into account to measure expert reliabilities. When the assessments of some experts are incomplete while global ignorance is incurred, pairs of optimization problems are constructed to decide interval-valued expert reliabilities. The resulting expert reliabilities are applied to combine the expert assessments of alternatives on each attribute and then to generate the aggregated assessments of alternatives. An industry evaluation problem in Wuhu, a city in Anhui Province of China is analyzed by using the proposed method as a real case study to demonstrate its detailed implementation process, validity, and applicability.

An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context

In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements. The predefined GC is reached through several rounds of group analysis and discussion (GAD) in the approach. However, the GAD with no guidance may not be the most appropriate way to reach the predefined GC because several rounds of GAD will spend a lot of time of all experts and yet cannot help them to effectively emphasize on the assessments which primarily damage the GC. In this paper, an attribute weight based feedback model is constructed to effectively identify the assessments primarily damaging the GC and accelerate the GC convergence. Considering important attributes with the weights more than or at least equal to the mean of the weights of all attributes, the feedback model constructs identification rules to identify the assessments damaging the GC for the experts to renew. In addition, a suggestion rule is introduced to generate appropriate recommendations for the experts to renew their identified assessments. The identification rules are constructed at three levels including the attribute, alternative and global levels. The feedback model is used to solve an engineering project management software selection problem to demonstrate its detailed implementation process, its validity and applicability, and its advantages compared with the GC based approach.

A new variable production scheduling strategy for deteriorating items with time-varying demand and partial lost sale

Recently, Wee and Wang (Comput. Oper. Res. 26 (1999) 237) modeled a production-inventory system for deteriorating items with time-varying demands and completely backlogged shortages; we now present an extended cost model that relaxes the assumption of completely backlogged shortages by permitting part of the backlogged shortages to turn into lost sales--which is assumed to be a function of currently backlogged amount. Wee and Wang (Comput. Oper. Res. 26 (1999) 237) constructed production schedules for this system using the traditional scheduling strategy in which each cycle starts with replenishment and ends with shortages. We now consider an alternative scheduling strategy in which each cycle of a schedule starts with a period of shortages, then followed by continuous replenishment. By examining the profit performance of the two scheduling strategies (i.e., start with replenishment versus start with shortages), we show that our alternative strategy produces schedules with superior cost and profit values.

Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation.

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment.

Exploring the uniform effect of FCM clustering

Fuzzy c-means (FCM) is a well-known and widely used fuzzy clustering method. Though there have been considerable studies that focused on the improvement of FCM algorithm or its applications, it is still necessary to understand the effect of data distributions on the performance of FCM. In this paper, we present an organized study of FCM clustering from the perspective of data distribution. We first analyze the structure of the objective function of FCM and find that FCM has the same uniform effect as K-means. Namely, FCM also tends to produce clusters of relatively uniform sizes. The coefficient of variation (CV) is introduced to measure the variation of cluster sizes in a given data set. Then based on the change of CV values between the original true cluster sizes and the cluster sizes partitioned by FCM clustering, a necessary but not sufficient criterion for the validation of FCM clustering is proposed from the data distribution perspective. Finally, our experiments on six synthetic data sets and ten real-world data sets further demonstrate the uniform effect of FCM. It tends to reduce the variation in cluster sizes when the CV value of the original data distribution is larger than 0.88, and increase the variation when the variation of original true cluster sizes is low.

An optimal replenishment policy for items with inventory-level-dependent demand and fixed lifetime under the LIFO policy

In a recent paper, Hwang and Hahn considered inventory replenishment problems for an item with an inventory-level-dependent demand rate and a fixed lifetime. They developed an EQQ model under the situation of considering the first-in–first-out (FIFO) issuing policy. First, this paper reconsiders Hwang and Hanns problem by employing the last-in–first-out (LIFO) issuing policy, which is more practical in the retail industry. An inventory model is developed. Secondly, the concavity of the objective function is proved. Thirdly, this paper presents conditions where the present model has a unique optimal solution and a method for finding the global optimal solution. A simple solution procedure and sensitivity analyses of parameters are also provided.

Assessment of Strategic R&D Projects for Car Manufacturers Based on the Evidential Reasoning Approach

Assessment of strategic R&D projects is in essence a multiple-attribute decision analysis (MADA) problem. In such problems, qualitative information with subjective judgments of ambiguity is often provided by people together with quantitative data that may be imprecise or incomplete. A few approaches can be used to deal with such quantitative and qualitative MADA problems under uncertainty, such as the evidential reasoning (ER) approach that has its own unique features. In this paper, the ER approach is applied to the assessment of strategic R&D projects for a car manufacturer, which is characterized by many qualitative factors that may be imprecise or fuzzy. The ER approach is well-suited for dealing with such problems and can generate comprehensive distributed assessments for different projects. The group analytic hierarchy process (GAHP) method is applied to calculate the weights of attributes in the E-R assessment process, where a group of people from the company were involved. We also provide a new al...

On the inference and approximation properties of belief rule based systems

Belief rule based (BRB) system provides a generic inference framework for approximating complicated nonlinear causal relationships between antecedent inputs and output. It has been successfully applied to a wide range of areas, such as fault diagnosis, system identification and decision analysis. In this paper, we provide analytical and theoretical analyses on the inference and approximation properties of BRB systems. We first investigate the unified multi-model decomposition structure of BRB systems, under which the input space is partitioned into different local regions. Then we analyse the distributed approximation process of BRB systems. These analysis results unveil the underlying inference mechanisms that enable BRB systems to have superior approximation performances. Furthermore, by using the Stone-Weierstrass theorem, we constructively prove that BRB systems can approximate any continuous function on a compact set with arbitrary accuracy. This result provides a theoretical foundation for using and training BRB systems in practical applications. Finally, a numerical simulation study on the well-known benchmark nonlinear system identification problem of Box-Jenkins gas furnace is conducted to illustrate the validity of a BRB system and show its inference and approximation capability.