the location-routing problem is a relatively new branch of logistics system. its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. in this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. another important issue in this regard ...

This paper presents a novel approach to modelling time series datasets using fuzzy trend information. A time series is described using natural linguistic terms such as rising more steeply and falling less steeply. These natural shape descriptors enable us to produce a glass box model of the series. The linguistic shape descriptors are represented in our system by a new feature called the trend ...

this paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. the mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. furthermore, ...

Many traditional environment applications base their operations on using sensor. For instance, the Tagged World uses the information from sensors to identify user behaviour and to provide services. We present a method to limit an user behaviour through external user knowledge: a Temporal Window. However, an user behaviour has random imprecision by definition. Thus, we have adapted the method to...

An adaptive predictive control methodology is applied for a fossil fuel boiler control. The control algorithm takes advantage of a neuro-fuzzy identifier system for prediction of the boiler response in a future time window. An optimizer algorithm based on evolutionary programming technique (EP) uses the identifier-predicted outputs and determines input sequence in a time window. The present opt...

todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. in this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming...