Algorithms for part and machine selection in flexible manufacturing systems

Deciding whether FMS technology is viable for a given application, and if so, what machines should comprise the FMS and what parts should be produced on it, can be a difficult task. Manual methods suffice only for situations where a small number of FMS-type machines are to be considered and less than a few dozen candidate parts are to be chosen from. When both machines and parts are to be selected from a larger number of candidates, manual methods become cumbersome and time consuming, and computer-based decision aids become a necessity. This paper gives an overview of the required decision process, and then focuses on those stages for which computer-based decision aids can be used effectively. Particular decision aids are described, and case studies are cited to illustrate their motivation and use.     

Subjective Information Modelling

Operational Research is viewed as an aid to decision making and it is believed that at present an increasing part of the decision maker's task is being shut off from the operational researcher because of the latter's inability to make a useful contribution. Particular problems are those in which the decision maker has to use his subjective judgement, intuition and foresight to enable him to make choices between alternatives. There has been a great deal of work over the last ten years in the field of technological forecasting in which attempts are made to qualify, quantify and aggregate such subjective judgements, for example, Delphi and Cross Impact Analysis.The object of this paper is to present a framework within which these new procedures and the traditional O.R. approaches may be brought together. We feel that this may open the way for an improvement in decision making and an expanded role for O.R.     

The k-means range algorithm for personalized data clustering in e-commerce

This paper describes the k-means range algorithm, a combination of the partitional k-means clustering algorithm with a well known spatial data structure, namely the range tree, which allows fast range searches. It offers a real-time solution for the development of distributed interactive decision aids in e-commerce since it allows the consumer to model his preferences along multiple dimensions, search for product information, and then produce the data clusters of the products retrieved to enhance his purchase decisions. This paper also discusses the implications and advantages of this approach in the development of on-line shopping environments and consumer decision aids in traditional and mobile e-commerce applications.     

Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture

This paper outlines a distributed GDSS suitable to be used over the Internet, based on the VIP Analysis methodology and software. VIP Analysis incorporates complementary approaches to deal with the aggregation of multicriteria performances by means of an additive value function under imprecise information. This proposed GDSS intends to support a decision panel forming a democratic decision unit, whose members wish to reach a final decision in a choice problem, based on consensus or on some majority rule. Its purpose is not to impose an aggregated model from the individual ones. Rather, the GDSS is designed to reflect to each member the consequences of his/her inputs, confronting them with analogous reflections of the group members' inputs. We propose aggregation procedures to provide a reflection of the group's inputs to each of its members, and an architecture for a GDSS implementing these procedures.     

The problem of highly constrained tasks in group decision support systems

Most experimental uses of group decision support systems (GDSS) are associated with relatively unrestricted domains, for example, idea generation and preference specification, where few restrictions on potential solutions exist. However, an important GDSS task is that of resource allocation across functional areas of the organization, including supply chain applications. These types of tasks, such as budget planning and production planning, are typically highly constrained and difficult to solve optimally, necessitating the use of decision aids, such as those found in GDSS.We use a model based on adaptive search of a genetic algorithm as the analogy for the group decision making process. We apply this model to experimental data gathered from GDSS groups solving a production planning task. The results indicate very low estimated crossover rates in the experimental data. We also run computational experiments based on adaptive search to mimic the GDSS data and find that the low estimated crossover rate might be due to the highly constrained search space explored by the decision making groups. The results suggest further investigation into the presumed beneficial effects of group interaction in such highly constrained task domains, as it appears very little true information exchange occurs between group members in such an environment. Furthermore, the simulation technique can be used to help predict certain GDSS behaviors, thus improving the entire GDSS process.     

Interactive group decision-making procedure using weak strength of preference

In the paper, we present an interactive decision procedure for aggregating group members’ preferences which are specified in incomplete ways. A group consensus under incomplete information is not usually reached at a single step since less-specific preferences on attribute weights and performance scores make a clear selection of best alternative more difficult. To circumvent these difficulties, a measure, indicating the strength of preference between alternatives, is derived to help each of decision makers change his/her preference structure. To make preference changes based on the measure effective, we present a solution method to convert an intractable nonlinear programme into a linear one.     

Verbal ordinal classification with multicriteria decision aiding

Professionals in neuropsychology usually perform diagnoses of patients’ behaviour in a verbal rather than in a numerical form. This fact generates interest in decision support systems that process verbal data. It also motivates us to develop methods for the classification of such data. In this paper, we describe ways of aiding classification of a discrete set of objects, evaluated on set of criteria that may have verbal estimations, into ordered decision classes. In some situations, there is no explicit additional information available, while in others it is possible to order the criteria lexicographically. We consider both of these cases. The proposed Dichotomic Classification (DC) method is based on the principles of Verbal Decision Analysis (VDA). Verbal Decision Analysis methods are especially helpful when verbal data, in criteria values, are to be handled. When compared to the previously developed Verbal Decision Analysis classification methods, Dichotomic Classification method performs better on the same data sets and is able to cope with larger sizes of the object sets to be classified. We present an interactive classification procedure, estimate the effectiveness and computational complexity of the new method and compare it to one of the previously developed Verbal Decision Analysis methods. The developed and studied methods are implemented in the framework of a decision support system, and the results of testing on artificial sets of data are reported.     

Involving Users in the Development of Decision-Analytic Aids: The Principal Factor in Successful Implementation

Decision-analytic techniques and aids have been used successfully in a wide range of settings to help decision makers sytematically evaluate alternative means of resolving a problem. On the basis of this success. it is anticipated that organizations will increasingly desire decision-analytic aids with stand-alone capabilities for routine use without outside consultation. The paper's thesis is that decision-analytic aids will seldom achieve stand-alone capabilities unless eventual users are involved throughout the development process. User involvement develops the understanding and commitment necessary for implementing a different decision-making approach and tailors aid characteristics to the users' needs within their organizational context. Although the need for user involvement appears obvious, it has often been neglected in the development of analytical decision aids, with the result being unsuccessful implementation.     

A vague set based decision support approach for evaluating research funding programs

Scientific Research Assessment (SRA) is receiving increasing attention in both academic and industry. More and more organizations are recognizing the importance of SRA for the optimal use of scarce resources. In this paper, a vague set theory based decision support approach is proposed for SRA. Specifically, a family of parameterized S-OWA operator is developed for the aggregation of vague assessments. The proposed approach is introduced to evaluate the research funding programs of the National Natural Science Foundation of China (NSFC). It provides a soft and expansive way to help the decision maker in NSFC to make his decisions. The proposed approach can also be used for some other agencies to make similar assessment.     

Modeling uncertainty in multi-criteria decision analysis

his paper provides a review of multiple criteria decision analysis (MCDA) for cases where attribute evaluations are uncertain. The main aim is to identify different tools which can be used to represent uncertain evaluations, and to broadly survey the available decision models that can be used to support uncertain decision making. The review includes models using probabilities or probability-like quantities; explicit risk measures such as quantiles and variances; fuzzy numbers, and scenarios. The practical assessment of uncertain outcomes and preferences associated with these outcomes is also discussed.     

Vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method

With collaborative purchasing programmes where one of the aims is to develop suppliers, vendor rating is important not only in supplier selection and in deciding how to allocate business but also to determine where scarce development effort is best applied. This paper describes a case study into vendor rating for a government sponsored Entrepreneur Development programme in Malaysia. The paper reviews current methods for vendor rating and finds them wanting. It illustrates a new approach based on the use of Saaty's Analytic Hierarchy process method, which was developed to assist in multi-criteria decision problems. The new method overcomes the difficulties associated with the categorical and simple linear weighted average criteria ranking methods. It provides a more systematic way of deriving the weights to be used and for scoring the performance of vendors.     

Uncertain agency models with multi-dimensional incomplete information based on confidence level

This paper discusses a principal–agent problem with multi-dimensional incomplete information between a principal and an agent. Firstly, how to describe the incomplete information in such agency problem is a challenging issue. This paper characterizes the incomplete information by uncertain variable, because it has been an appropriate tool to depict subjective assessment and model human uncertainty. Secondly, the relevant literature often used expected-utility-maximization to measure the two participators’ goals. However, Ellsberg paradox indicates that expected utility criterion is not always appropriate to be regarded as decision rule. For this reason, this paper presents another decision rule based on confidence level. Instead of expected-utility-maximization, the principal’s aim is to maximize his potential income under the acceptable confidence level, and the agent’s aim depends on whether he has private information about his efforts. According to the agent’s different decision rules, three classes of uncertain agency (UA) models and their respective optimal contracts are presented. Finally, a portfolio selection problem is studied to demonstrate the modeling idea and the viability of the proposed UA models.     

Exploration of a hybrid feature selection algorithm

In the Knowledge Discovery Process, classification algorithms are often used to help create models with training data that can be used to predict the classes of untested data instances. While there are several factors involved with classification algorithms that can influence classification results, such as the node splitting measures used in making decision trees, feature selection is often used as a pre-classification step when using large data sets to help eliminate irrelevant or redundant attributes in order to increase computational efficiency and possibly to increase classification accuracy. One important factor common to both feature selection as well as to classification using decision trees is attribute discretization, which is the process of dividing attribute values into a smaller number of discrete values. In this paper, we will present and explore a new hybrid approach, ChiBlur, which involves the use of concepts from both the blurring and χ²-based approaches to feature selection, as well as concepts from multi-objective optimization. We will compare this new algorithm with algorithms based on the blurring and χ²-based approaches.     

The optimal quantity discount that a supplier should offer

Quantity discounts are a useful mechanism for coordination. Here we investigate such discounts from the supplier's perspective, both from a non-cooperative game-theoretical approach and a joint decision model. Taking into account the price elasticity of demand, this analysis aids a sole supplier in establishing an all-unit quantity discount policy in light of the buyer's best reaction. The Stackelberg equilibrium and Pareto optimal solution set are derived for the non-cooperative and joint-decision cases, respectively. Our research indicates that channel efficiency can be improved significantly if the quantity discount decision is made jointly rather than non-cooperatively. Moreover, we extend our model in three directions: (1) the product is transported by a private fleet; (2) the buyer may choose to offer her customers a different percentage discount than that she obtained from the supplier; and (3) the case of heterogeneous buyers. Numerical case studies are employed throughout the paper to illustrate the practical applications of the models presented and the sensitivity to model parameters.     

The MACBETH approach for multi-criteria evaluation of development projects on cross-cutting issues

The European Union’s efforts for poverty reduction are based on the principle of Sustainable Human Development. Meeting the terms of this principle entails the consideration of aspects of general interest, known as “cross-cutting issues”, at all levels of intervention. Cross-cutting issues comprise issues like human rights, gender equity, environmental concern, democracy as a social value, or the participation and empowerment of the beneficiaries of development. These issues concern social phenomena that are difficult to isolate in time or capture empirically, causing operational difficulties when projects are subjected to evaluation. Traditional methods of project evaluation, such as Cost Benefit Analysis or the Logic Framework, struggle to incorporate impacts that are difficult to measure or estimate in terms of indicators or monetary value. Therefore, in the field work at a rural development programme located in Bolivia, a need arose spontaneously to find and implement an approach that, complementary to traditional methods of project evaluation, could allow project managers to keep an eye on project performances in terms of cross-cutting issues. This article describes how the MACBETH multicriteria approach was implemented in practice, in order to help an important rural development programme build a project evaluation tool, taking into account cross-cutting issues through a series of interviews and decision conferences attended by specialists and the programme staff.     

Preference ranking of discrete multiattribute instances

This paper presents a procedure for preference ranking of discrete multiattribute instances based on the observations that (1) a difficult question could be answered in terms of answers to a sequence of easier ones, (2) in general, the decision maker's preferences can easily be expressed only relative to pairs of instances that differ over one attribute, and (3) the decision maker is able and willing to express his uncertainty regarding a quantity via a subjective probability distribution. The procedure is illustrated through a hypothetical situation.     

Formalizing and solving the problem of clustering in MCDA

The topic of clustering has been widely studied in the field of Data Analysis, where it is defined as an unsupervised process of grouping objects together based on notions of similarity. Clustering in the field of Multi-Criteria Decision Aid (MCDA) has seen a few adaptations of methods from Data Analysis, most of them however using concepts native to that field, such as the notions of similarity and distance measures. As in MCDA we model the preferences of a decision maker over a set of decision alternatives, we can find more diverse ways of comparing them than in Data Analysis. As a result, these alternatives may also be arranged into different potential structures. In this paper we wish to formally define the problem of clustering in MCDA using notions that are native to this field alone, and highlight the different structures which we may try to uncover through this process. Following this we propose a method for finding these structures. As in any clustering problem, finding the optimal result in an exact manner is impractical, and so we propose a stochastic heuristic approach, which we validate through tests on a large set of artificially generated benchmarks.     

Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems

As indicated by the most widely accepted classification, the Multi-Objective Mathematical Programming (MOMP) methods can be classified as a priori, interactive and a posteriori, according to the decision stage in which the decision maker expresses his/her preferences. Although the a priori methods are the most popular, the interactive and the a posteriori methods convey much more information to the decision maker. Especially, the a posteriori (or generation) methods give the whole picture (i.e. the Pareto set) to the decision maker, before his/her final choice, reinforcing thus, his/her confidence to the final decision. However, the generation methods are the less popular due to their computational effort and the lack of widely available software. The present work is an effort to effectively implement the ε-constraint method for producing the Pareto optimal solutions in a MOMP. We propose a novel version of the method (augmented ε-constraint method - AUGMECON) that avoids the production of weakly Pareto optimal solutions and accelerates the whole process by avoiding redundant iterations. The method AUGMECON has been implemented in GAMS, a widely used modelling language, and has already been used in some applications. Finally, an interactive approach that is based on AUGMECON and eventually results in the most preferred Pareto optimal solution is also proposed in the paper.