This work develops a fuzzy linear programming (FLP) method for solving the transportation planning decision (TPD) problems with fuzzy goals, available supply and forecast demand. The proposed method attempts to minimize the total production and transportation costs and the total delivery time with reference to available supply and machine capacities at each source, as well as forecast demand an...

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...

1  Abstract— Demand response, which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With demand response, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated Energy Management System (EMS) have been developed...

In hierarchical service networks, facilities at di erent levels provide di erent types of service. For example, in health care systems, general centers provide low-level services, such as primary health care, while specialized hospitals provide high-level services. Because of the demand congestion at service networks, the location of servers and their allocation of demand nodes can have a stron...

A mixed 0-1 integer programming model of Combined Location Routing and Inventory Problem (CLRIP) with fuzzy random demand has been proposed in B2C E-commerce distribution environment. Demands of customers and distribution centers have been assumed to be fuzzy random variables.

During the past few years,many people have been interested in integrated production andmarketing planning strategies where demand and cost functions, both, depend on different parameters such as price and marketing expenditure. The primary concern on all previous models is the difficulty on estimating the model parameters such price and marketing elasticity to demand. In this paper, we propose ...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

1 Corresponding author E-mail address: [email protected] (Purnima K. Pandit)