FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS

ABSTRACT. This paper addresses the design of control charts for both variable ( x chart) and attribute (u and c charts) quality characteristics, when there is uncertainty about the process parameters or sample data. Derived control charts are more flexible than the strict crisp case, due to the ability of encompassing the effects of vagueness in form of the degree of expert's presumption. We extend the use of proposed fuzzy control charts in case of linguistic data using a developed defuzzifier index, which is based on the metric distance between fuzzy sets

A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments

Hubs are centers for collection, rearrangement,and redistribution of commodities in transportation networks. In this paper, non-linear multi-objective formulations for single and multiple allocation hub maximal covering problems as well as the linearized versions are proposed. The formulations substantially mitigate complexity of the existing models due to the fewer number of constraints and variables. Also, uncertain shipments are studied in the context of hub maximal covering problems. In many real-world applications, any link on the path from origin to destination may fail to work due to disruption. Therefore, in the proposed bi-objective model, maximizing safety of the weakest path in the network is considered as the second objective together with the traditional maximum coverage goal. Furthermore, to solve the bi-objective model, a modified version of NSGA-II with a new dynamic immigration operator is developed in which the accurate number of immigrants depends on the results of the other two common NSGA-II operators, i.e. mutation and crossover. Besides validating proposed models, computational results confirm a better performance of modified NSGA-II versus traditional one

Five crisp and fuzzy models for supply chain of an automotive manufacturing system

Supply Chain Management (SCM) is a new approach to production planning. It integrates the components of supply chain in a holistic manner. Modeling this large-scale system, which contains all effective enterprises in production such as raw material suppliers, part manufacturers, assembly plants, distribution organizations, and the like, is challenging for managers, engineers and researchers. This paper concentrates on supply chain system modeling with fuzzy linear programming, and fuzzy expert system for an automobile plant. First, a linear programming model is developed in such a way that while the input data is fuzzy, the constraints are crisp. In the second linear model, the coefficients of the model are crisp while the constraints are fuzzy. In the third model, we aggregate the first and the second models into one fuzzy linear programming where all constraints and coefficients are fuzzy. In each case, we compare the results with those of classical SC models. Finally, a rule based fuzzy expert system for SC is developed and the results are compared with those of the classical and fuzzy LP models. The results of the fuzzy expert system show its superiority over the former crisp and fuzzy linear programming models

Facility location via fuzzy modeling and simulation

This paper presents a continuous facility location model with fuzzy methodology. The developments concern mainly to some drawbacks in the initial model which takes it far from being used in practice. A fuzzy modeling method is proposed to estimate the required functions in the initial model. Structure identification in the proposed fuzzy modeling method is carried out using subtractive clustering, and parameter identification is conducted via some heuristics as well as an optimization problem. Furthermore, a simulation method along with some heuristic relations is used for implementation and evaluation of the modified model. Efficiency of the proposed method to fuzzy modeling as well as the proposed simulation method is presented by a numerical example

Five crisp and fuzzy models for supply chain of an automotive manufacturing system

Supply Chain Management (SCM) is a new approach to production planning. It integrates the components of supply chain in a holistic manner. Modeling this large-scale system, which contains all effective enterprises in production such as raw material suppliers, part manufacturers, assembly plants, distribution organizations, and the like, is challenging for managers, engineers and researchers. This paper concentrates on supply chain system modeling with fuzzy linear programming, and fuzzy expert system for an automobile plant. First, a linear programming model is developed in such a way that while the input data is fuzzy, the constraints are crisp. In the second linear model, the coefficients of the model are crisp while the constraints are fuzzy. In the third model, we aggregate the first and the second models into one fuzzy linear programming where all constraints and coefficients are fuzzy. In each case, we compare the results with those of classical SC models. Finally, a rule based fuzzy expert system for SC is developed and the results are compared with those of the classical and fuzzy LP models. The results of the fuzzy expert system show its superiority over the former crisp and fuzzy linear programming models.</p

Supply Chain: Crisp and Fuzzy Aspects

This survey presents crisp and fuzzy models developed for the Supply Chain (SC). To this end, fuzzy concepts are first briefly reviewed. Then the structure of a supply chain system is explained. Recent challenges and ideas on these systems are also surveyed. Then, with reference to different aspects of an SC system, we classify the recent fuzzy models developed for different SC systems and compare them with related crisp models. Applications of the SC in manufacturing and service industries are investigated in each case

The incomplete hub-covering location problem considering imprecise location of demands

Facility location is a prime decision to be made in many organizations around the globe. The hub location problem is one of the main variants of the facility location problem, with applications in telecommunications, the airline industry, and etc. In this paper, we deal with an incomplete hub-covering network design problem, where the exact locations of demands are unknown and are estimated as fuzzy variables. An earlier model in the hub location literature has been modified to address the uncertainty in the problem. In order to solve this problem, an efficient simulation-embedded Variable Neighborhood Search (VNS) has been designed and its performance has been validated using the well-known CAB dataset

Supplier selection in a multi-item/multi-supplier environment

Suppliers play a pivotal role in success of any organization. Supplier Selection is a complicated problem due to the vagueness of data and also its multi-criteria nature and the real world still observes a noticeable gap between its theory and practice. The aim of this paper is to present a fuzzy decision-making approach to address this problem in a way that facilitates the process of decision making while not deteriorating its comprehensiveness. The main contributions of the paper are twofold: first, a model is developed to consider multiple suppliers and multiple items. Moreover, a piecewise linear membership function (PLMF) is proposed for a specific criterion and is shown how it leads to better solutions. The model functions well in cases where decision maker is sensitive about a specific criterion, in other words, when there are some unequal weights for objectives of the problem. Although asymmetric methods proposed by Zimmerman are a way to tackle the above-mentioned situations; it is demonstrated that how the proposed model brings about both efficiency and simplicity for decision maker which is originated from the utilization of PLMF

The large scale maximal covering location problem

The maximal covering location problem (MCLP) is a challenging problem with numerous applications in practice. Previous publications in the area of MCLP proposed models and presented solution methodologies to solve this problem with up to 900 nodes. Due to the fact that in real-life applications, the number of nodes could be much higher, this paper presents a customized Genetic Algorithm (GA) to solve MCLP instances, with up to 2500 nodes. Results show that the proposed approach is capable of solving problems with a fair amount of exactness. In order to fine-tune the algorithm, Tukey’s Least Significant Difference (LSD) tests are employed on a set of test problems