In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (via fuzzy sets and fuzzy rule bases). The FLEXFIS and e...

In this research work, a wholesaler-retailer-customer supply chain model for a deteriorating item is considered, where the retailer's warehouse in the market place has a limited capacity. The retailer can rent an additional warehouse (rented warehouse) if needed, with a higher rent compared to the existing warehouse (own warehouse). The customers' demand of the item is linearly influenced by th...

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level ...

In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...

Fuzzy{logic theory is a valuable tool to structure ad{hoc designs where model{based designs are not suitable or even not available. There exist numerous examples of industry proven fuzzy{logic solutions in the domain of control, pattern recognition and expert systems. The design of fuzzy systems demand a great deal of experience and intuition. Due to the many degrees of freedom in the design of...

In this paper, we consider fuzzy inventory with backorder. First, we fuzzify the storing cost a, backorder cost b, cost of placing an order c, total demand r, order quantity q, and shortage quantity s as the triangular fuzzy numbers in the total cost. From these, we can obtain the fuzzy total cost. Using the signed distance method to defuzzify, we get the estimate of the total cost in the fuzzy...

Using Greedy Clustering Method to Solve Capacitated Location-Routing Problem with Fuzzy Demands Abstract In this paper, the capacitated location routing problem with fuzzy demands (CLRP_FD) is considered. In CLRP_FD, facility location problem (FLP) and vehicle routing problem (VRP) are observed simultaneously. Indeed the vehicles and the depots have a predefined capacity to serve the customerst...

Three decades of fuzzy logic systems had led to development of first generation fuzzy logic based hardware which provided faster fuzzy control solutions incorporating inference engines. The emergence of corporate computing is likely to place heavy demand on soft computing which has fuzzy logic as its core. In order to meet the challenges of the second generation fuzzy logic based hardware, issu...

This paper presents an overall control scheme for wide-range multiobjective-optimal operation of a fossil-fuel power unit. The scheme provides the means to accommodate operating scenarios characterized by multiple operating requirements, and to attain optimal operation along any arbitrary unit load-demand pattern. The proposed strategy builds upon current coordinated control schemes, which typi...

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbe...