Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Abstract:Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers

Fuzzy Random Facility Location Problems with Recourse

Abstract-The objective of this paper is to study facility location problems under a hybrid uncertain environment involving randomness and fuzziness. A two-stage fuzzy random facility location model with recourse is developed in which the demands and the costs are assumed to be fuzzy random variables. As in general the fuzzy random parameters in the model can be regarded as continuous fuzzy random variables with infinite realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this fact, the recourse function cannot be calculated analytically, which implies that the model cannot benefit from the use of methods of classical mathematical programming. In order to solve the location problems of this nature, we first develop techniques of fuzzy random simulation. In the sequel, by combining the fuzzy random simulation, simplex algorithm and binary particle swarm optimization (BPSO), a hybrid algorithm is proposed to solve the two-stage fuzzy random facility location model. Finally, an illustrative numerical example is provided

A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies

Tasks complexity and decision making under uncertainty

Ova studija imala je za cilj da proveri na koji način kompleksnost zadatka, u sadejstvu s drugim potencijalnim faktorima, utiče na odlučivanje u uslovima neizvesnosti. Kompleksnost zadatka je objektivno definisana preko dve varijable: broja potencijalnih događaja u budućnosti (dva, tri i pet događaja) i razlika u neto vrednostima ishoda (male i velike razlike). U oba slučaja, kompleksnost je zasnovana na neizvesnosti. Sprovedena su četiri eksperimenta: u dva eksperimenta zadaci su bili smešteni u ekonomski, a u dva u medicinski domen. Pored dve varijable kojima je operacionalizovana kompleksnost, u svakom od eksperimenata variran je po još jedan faktor, koji se odnosi na karakteristike zadatka: referentna tačka (zona gubitka i zona dobitka), stepen uključenosti u odlučivanje (donošenje odluke za sebe i donošenje odluke za drugog) i okvir (pozitivan i negativan). Zadaci su osmišljeni tako da se na osnovu donesene odluke može jednoznačno odrediti koji od tri modela maximax, maximin, minimax se nalaze u osnovi izbora. Ujedno, ovakva procedura je omogućila objektivno praćenje promene strategije odlučivanja u zavisnosti od eksperimentalnih uslova. Nalazi ukazuju da se prilikom odlučivanja ispitanici oslanjaju na sva tri modela odlučivanja, s tim da je većina odluka donesena tako da smanji rizik od gubitka, tj. na osnovu maximin i minimax modela. Utvrđene su interakcije kompleksnosti zadataka, s jedne strane, i domena, stepena uključenosti i referentne tačke, s druge strane. Kada se radi o kompleksnijim zadacima, tj. u slučaju većeg kognitivnog opterećenja, ispitanici pokušavaju da olakšaju proces odlučivanja, zbog čega dolazi do većeg ispoljavanja kognitivnih pristrasnosti, kao što su uticaj okvira i averzija prema gubitkuThe aim of this study is to explore how task complexity and other potential factors impact decision making under uncertainty. Task complexity is objectively defined through two variables: the number of potential events in the future (two, three and five) and the difference in the net value of the outcomes (small and big differences). In both cases the complexity is based on uncertainty. Four experiments were carried out. In two experiments the tasks were placed in an economic domain, whereas the other two tasks were placed in a medical domain. Beside the two variables through which complexity was conducted, in each of the experiment one more factor referring to task characteristics was varied, i.e. the reference point (domain of loss and domain of gain), the level of decision making involvement (either for yourself or for somebody else) and the frame (as either positive or negative). The tasks were designed in a way that, based on the decision made, one can unambiguously define which of the three models – maximax, maximin or minimax – are at the base of the choice. In addition, this procedure enabled objective tracking of the change strategy of uncertain decision making depending on the experimental conditions. The findings show that in the course of decision making process, the participants rely on all three models of decision making, provided that most decisions are made in order to reduce the risk of loss, i.e. based on maximin and minimax model. Interactions of the task complexity, on one hand, and the domain, the level of involvement and the reference point, on the other hand, were determined. In the case of more complex tasks, i.e. in the case of greater cognitive load, the participants try to make decision making easier, which leads to more cognitive biases, such as frame impact and loss aversio

Inventory consideration and management in two supply chain problems

Ph.DDOCTOR OF PHILOSOPH

Location-allocation models for relief distribution and victim evacuation after a sudden-onset natural disaster

Quick response to natural disasters is vital to reduce loss of and negative impact to human life. The response is more crucial in the presence of sudden-onset, difficult-to-predict natural disasters, especially in the early period of those events. On-site actions are part of such response, some of which are determination of temporary shelters and/ or temporary medical facility locations, the evacuation process of victims and relief distribution to victims. These activities of last-mile disaster logistics are important as they are directly associated with sufferers, the main focus of any alleviation of losses caused by any disaster. This research deals with the last-mile site positioning of relief supplies and medical facilities in response to a sudden-onset, difficult-to-predict disaster event, both dynamically and in a more coordinative way during a particular planning time horizon. Four mathematical models which reflect the situation in Padang Pariaman District after the West Sumatera earthquake were built and tested. The models are all concerned with making decisions in a rolling time horizon manner, but differ in coordinating the operations and in utilization of information about future resource availability. Model I is a basic model representing the current practice with relief distribution and victim evacuation performed separately and decisions made only considering the resources available at the time. Model II considers coordination between the two operations and conducts them with the same means of transport. Model III takes into account future information keeping the two operations separate. Model IV combines the features of Models II and III. The four models are approached both directly and by using various heuristics. The research shows that conducting relief distribution and victim evacuation activities by using shared vehicles and/or by taking into account future information on resource availability improves the current practice . This is clearly demonstrated by the experimental results on small problems. For large problems, experiments show that it is not practical to directly solve the models, especially the last three, and that the solution quality is poor when the solution process is limited to a reasonable time. Experiments also show that the heuristics help improve the solution quality and that the performances of the heuristics are different for different models. When each model is solved using its own best heuristic, the conclusions from results of large problems get very close to those from small problems. Finally, deviation of future information on resource availability is considered in the study, but is shown not to affect the performance of model III and model IV in carrying out relief distribution and victim evacuation. This indicates that it is always worthwhile to take into account the future information, even if the information is not perfect, as long as it is reasonably reliable