Ching-Ter Chang

Fuzzy linearization strategy for multiple objective linear fractional programming with binary utility functions

Abstract This paper integrates fuzzy linearization strategy, goal programming, a membership function and conditional control mechanisms to produce a novel method to deal with the binary behavior of multiple objective fractional programming problems and multiple objective fractional programming problems with a utility function. The major contributions of the proposed method are twofold. (1) The binary behavior of multiple objective fractional programming problems can easily be converted into a linearized program using the proposed fuzzy linearization strategy. The linearized program can easily be solved, using commercial linear programming packages, yielding an approximate global optimal solution, and (2) The utility function is also used to ensure that the qualification requirements for a multiple objective fractional programming problem are met, in contrast to most past mathematical approaches, which only use quantitative approaches to deal with such a problem. In addition, an illustrative example and a practical real case are provided to demonstrate the usefulness of the proposed model. The discussion of the practical problem will help decision makers to realize the usefulness of a utility function and the binary behavior in multiple objective fractional programming problems.

An effective zero-inventory-ordering policy for a single-warehouse multiple retailer problem with a modified all-unit discount

Abstract A warehouse is an important value-added service hub between a supplier, a retailer and a customer in a supply chain system. In order to encourage the retailer to purchase a large volume of goods and save in transportation costs, a modified all-unit discount cost structure is often used by warehouses. However, Chan, Muriel, Shen, and Simchi-Levi (2002) noted that the modified all-unit discount cost structure is an NP-hard problem. It is difficult to obtain the optimal solution for this problem. Therefore, Chan et al. developed a novel heuristic algorithm to solve a single-warehouse multiple retailer problem with a modified all-unit discount cost structure that was close to the optimal solution. This paper proposes an exact strategy to deal with the same problem that gives the optimal solution in polynomial time, without the need for binary variables, and regardless of the number of breakpoints. The proposed solution is also successfully demonstrated and can be extended to solve arbitrary piecewise linear functions.

On fuzzy multiple objective linear programming problems

Abstract In recent years, multiple objective decision making (MODM) has become more and more important, and multiple objective linear programming (MOLP) approaches have been widely used for solving MODM problems. Three popular fuzzy mathematical programming approaches, including maxmin, arithmetical average, and two-phase methods, are often employed to solve MOLP problems. However, maxmin and two-phase methods cannot obtain an efficient solution. Ideal and anti-ideal solutions are required in all traditional methods. Such solutions cannot easily be established as a model for a knowledge-based system or expert system. In order to enrich the body of knowledge related to the field of MOLP, a new multi-choice goal programming (MCGP) model is proposed to solve fuzzy MOLP (FMOLP) problems in which the ideal and anti-ideal solutions are no longer required. This reduces the complexity of the solution process for solving MODM. The original feasible region can be expended to a potential feasible region to determine the appropriate aspiration level for decision makers. The proposed methods can systematically solve MODM problem to obtain satisfactory solutions in one step. Thus, a model-based can easily be established for expert systems, knowledge-based system, and artificial intelligence systems. In addition, a revised utility function (UF) is derived to solve qualitative and quantitative MODM problems. Based on illustrative examples, the five methods (maxmin, arithmetical average, two-phase, goal programming (GP), and proposed models) are compared to reveal managerial implications. On the basis of these comparisons, DMs can easily determine the best suited solution for their specific MODM problems. Finally, a realistic example is provided to demonstrate the usefulness of the proposed methods.

A Decision for Predicting Successful Extubation of Patients in Intensive Care Unit

Approximately 40% of patients admitted to the medical intensive care unit (ICU) require mechanical ventilation. An accurate prediction of successful extubation in patients is a key clinical problem in ICU due to the fact that the successful extubation is highly associated with prolonged ICU stay. The prolonged ICU stay is also associated with increasing cost and mortality rate in healthcare system. This study is retrospective in the aspect of ICU. Hence, a total of 41 patients were selected from the largest academic medical center in Taiwan. Our experimental results show that predicting successful rate of 87.8% is obtained from the proposed predicting function. Based on several types of statistics analysis, including logistic regression analysis, discriminant analysis, and bootstrap method, three major successful extubation predictors, namely, rapid shallow breathing index, respiratory rate, and minute ventilation, are revealed. The prediction of successful extubation function is proposed for patients, ICU, physicians, and hospital for reference.

Using binary fuzzy goal programming and linear programming to resolve airport logistics center expansion plan problems

Display Omitted DAHP, ZOGP and BMGP methods for airport logistic center expansion plans are proposed of both qualitative and quantitative criteria.This study considers operating costs, plan budget and contractor assignment issues in the problem.Airport ground handling services (AGHS) companies have enhanced to provide convenient services for passengers and to broaden the AGHS market. In recent years, the number of direct flights between Taiwan and mainland China has grown rapidly, as charter flights have been turned into regular flights. This important issue has prompted airport ground handling service (AGHS) companies in Taiwan to enhance convenient services for passengers and to invest in airport logistics center expansion plans (ALCEP) to broaden the AGHS market. Due to their budgetary restrictions, AGHS companies need to outsource many of their services to contractors to implement these plans. This study proposes an ALCEP solution procedure to guide AGHS companies in adjusting their priority goals and selecting the best contractor according to their needs. This proposed procedure successfully solves the ALCEP problem and facilitates the assignment of contractors by considering both qualitative and quantitative methods.