Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

House of Quality

Nonlinear Programming: A Unified Approach

Are Investors Reluctant to Realize Their Losses

This paper provides a survey of credibility theory that is a new branch of mathematics for studying the behavior of fuzzy phenomena. Some basic concepts and fundamental theorems are introduced, including credibility measure, fuzzy variable, membership function, credibility distribution, expected value, variance, critical value, entropy, distance, credibility subadditivity theorem, credibility extension theorem, credibility semicontinuity law, product credibility theorem, and credibility inversion theorem. Recent developments and applications of credibility theory are summarized. A new idea on chance space and hybrid variable is also documented.

A survey of credibility theory

Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD

Green logistics is an emerging area in supply chain management, which has been shown to have tremendous impacts in recent years to face the serious climate changes risks. In this paper, the fuel consumption and fuzzy travel time have been delineated in developing and solving the green-fuzzy vehicle routing problem as an extension of the celebrated VRP in which routes are performed to reduce the total expenditure. Different from the existing solution manners, we transform the original fuzzy chance constrained programming model into an equivalent deterministic model, and then revise the original hybrid intelligent algorithm by replacing the embedded fuzzy simulation with analytical function calculation. Finally, a comparative study with the corresponding literature is performed, which shows that the revised algorithm can not only improve the solution accuracy but also shorten the runtime greatly.

Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm

Optimizing h value for fuzzy linear regression with asymmetric triangular fuzzy coefficients

Good segmentation contributes towards a better understanding of the market and customer demands. This study aims to develop a new methodological approach, integrating “Recency, Frequency and Monetary” with the sparse K-means clustering algorithm of Witten and Tibshirani (2010). The proposed approach is suitable for handling large, high-dimensional and sparse consumer data. Drawing on the proposed methodology, alongside data collection from the Chinese mobile telecommunications market, and considering specific services, our treatment is further assessed empirically and appears to provide robust results when compared to the Dolnicar, Kaiser, Lazarevski and Leisch (2012) biclustering of customers method. Following the attainment of a clear and robust market segmentation structure, our theoretical treatment and its empirical analysis provide a useful tool and valid methodology for marketers, and decision makers in general, to accurately determine the most profitable market segments.

Jian Zhou

Papers

Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD

Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm

Optimizing h value for fuzzy linear regression with asymmetric triangular fuzzy coefficients

Market segmentation using high-dimensional sparse consumers data

Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach