Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model

The principal objective of the present study is to examine the possibilities of using a Gompertz-type innovation diffusion process as a stochastic growth model of natural-gas consumption in Spain, and to compare our results with those obtained, on the one hand, by stochastic logistic innovation modelling and, on the other, by using a stochastic lognormal growth model based on a non-innovation diffusion process. Such a comparison is carried out taking into account the macroeconomic characteristics and natural-gas consumption patterns in Spain, both of which reflect the current expansive situation characterizing the Spanish economy. From the technical standpoint a contribution is also made to the theory of the stochastic Gompertz Innovation diffusion process (SGIDP), as applied to the case in question.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.     

A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm

The location of manufacturing facilities is one of the most important strategic decisions considered in the design of logistic systems. Another important strategic decision is the structure and management of the fleets. Most often, even if two types of problem (i.e., location of facilities and vehicle routing) have occurred in a given scenario, they have been studied and solved separately. This paper presents a new integrated mathematical model for a bi-objective multi-depot location-routing problem where the total demand served is to be maximized and the total cost, consisting of start-up of the facility, fixed and variable depots and variable delivery cost, is to be minimized. Since this type of the problem is NP-hard, a new multi-objective scatter search (MOSS) algorithm is proposed to obtain the Pareto frontier for the given problem. To validate the performance of the proposed MOSS algorithm in terms of the solution quality and diversity level, various test problems are carried out and the efficiency of this algorithm based on some comparison metrics is compared with the elite tabu search (ETS). The computational results show that the proposed MOSS outperforms the ETS, especially in large-sized problems.     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

Determination of the flow stress of five AHSS sheet materials (DP 600, DP 780, DP 780-CR,DP 780-HY and TRIP 780) using the uniaxial tensile and the biaxial Viscous Pressure Bulge (VPB) tests

Room temperature uniaxial tensile and biaxial Viscous Pressure Bulge (VPB) tests were conducted for five Advanced High Strength Steels (AHSS) sheet materials, and the resulting flow stress curves were compared. Strain ratios (R-values) were also determined in the tensile test and used to correct the biaxial flow stress curves for anisotropy. The pressure vs. dome height raw data in the VPB test was extrapolated to the burst pressure to obtain the flow stress curve until fracture. Results of this work show that the flow stress data can be obtained to higher strain values under biaxial state of stress. Moreover, it was observed that some materials behave differently if subjected to different state of stress. These two conclusions, and the fact that the state of stress in actual stamping processes is almost always biaxial, suggest that the bulge test is a more suitable test for obtaining the flow stress of AHSS sheet materials for use as an input to Finite Element (FE) simulation models.