Analysis of collusion and competition in electricity markets using an agent-based approach

As a result of liberalization, deregulated electricity markets were formed to provide affordable electricity for consumers through promoting competition. Although the new market is expected to serve this purpose, even the earliest deregulated electricity markets are prone to threats that may disrupt the competition. While the independent system operator, responsible for administering the electricity markets, aims to provide the consumer with the lowest possible electricity price, lack of competition may increase prices. We consider the effect of three major factors hand-in-hand on that may affect the level of competition in the market: the independent system operator’s market-clearing mechanism as a strategic choice, strategic bidding behavior of generation companies and the transmission network

Modelling Integrated Multi-item Supplier Selection with Shipping Frequencies

There are many benefits for coordination of multiple suppliers when single supplier cannot satisfy buyer demands.  In addition, buyer needs to purchase multiple items in a real supply chain. So, a model that satisfies these requests has many advantages. We extend the existing approaches in the literature that assume all suppliers need to be put on a common replenishment cycle and each supplier delivers exactly once in a cycle. More specifically, inspired by approaches that perform well for the Economic Lot Scheduling Problem, we assume an integer number of times a supplier can ship available items in an overall replenishment cycle. Because of complexity issue, a new approach based on genetic algorithm is employed to solve the presented model. Results depict that new model is more beneficial and practical

Improvement of imperialist colony algorithm by employment of imperialist learning operator and implementing in travel salesman problem

This study tries to enhance imperialist colony algorithm (ICA) in the context of travel salesman problem (TSP). By adding new learning operator, imperialist learns from colonies that have suitable cost in which manner that improves the solution of problems. We believe that controlled learning improvement is better than uncontrolled one. The efficiency of new operator represented with the variety of instances from TSPLIB. We evaluate the approach on standard TSP test problems and show that it performs better, with respect to solution quality and computation time than ICA without new learning operator

Putative microRNA analysis of the kiwifruit actinidia chinensis through genomic data

MicroRNAs are important regulators in the cells that are well defined in various roles. With the advent of new generation sequencing technologies, identification of miRNAs studies increase rapidly. In here, we identified 58 putative miRNAs through kiwifruit genome by using in silico methods. The computational analysis was done through genome and transcriptome data of Chinese kiwifruit cultivar ‘Hongyang’ which has important properties including the high content of vitamin C, carotenoids and flavonoids. Since kiwifruit shares some portion of its genes with diverse plant families, this study may contribute to the further biotechnological studies in other close relatives

An improved approach to exchange non-rectangular departments in CRAFT algorithm

In this Paper, an algorithm which improves CRAFT algorithm’s efficacy is developed. CRAFT is an algorithm widely used to solve facility layout problems. Our proposed method, named Plasma, can be used to improve CRAFT results. In this note, Plasma algorithm is tested in several sample problems. The comparison between Plasma and classic CRAFT and also Micro-CRAFT indicates that Plasma is successful in cost reduction in comparison with CRAFT and Micro-CRAFT

In silico analysis of MicroRNAs in spinacia oleracea genome and transcriptome

Plant microRNAs (miRNAs) are small non-coding RNAs, about 21-24 nucleotides, which have important regulatory roles in growth, development, metabolic and defense processes. These critical elements regulate pathways either by inducing translational repression or messenger RNA (mRNA) decay. With the advent of the next-generation sequencing technologies and newly developed bioinformatics tools, the identification of microRNA studies by computational methods have been increased. Thus, the sequencing information provides us information for mining some known and unknown miRNAs in plants. In this study, we predict 34 putative miRNAs from Spinacia oleracea genome and two putative miRNA families from spinach transcriptome by using homology-based conservation method. RepeatMasker program is utilized to mask and eliminate five miRNA families out of 34 putative miRNA families from spinach genome. Finally, we analyze the targets of putatively identified miRNAs and their representation of genes (the copy number of each miRNA) throughout the genome

Parallelized neural network system for solving Euclidean traveling salesman problem

We investigate a parallelized divide-and-conquer approach based on a self-organizing map (SOM) in order to solve the Euclidean Traveling Salesman Problem (TSP). Our approach consists of dividing cities into municipalities, evolving the most appropriate solution from each municipality so as to find the best overall solution and, finally, joining neighborhood municipalities by using a blend operator to identify the final solution. We evaluate the performance of parallelized approach over standard TSP test problems (TSPLIB) to show that our approach gives a better answer in terms of quality and time rather than the sequential evolutionary SOM

A model for cost- and greenhouse gas optimal material and energy allocation of biomass and hydrogen

BENOPT, an optimal material and energy allocation model is presented, which is used to assess cost-optimal and/or greenhouse gas abatement optimal allocation of renewable energy carriers across power, heat and transport sectors. A high level of detail on the processes from source to end service enables detailed life-cycle greenhouse gas and cost assessments. Pareto analyses can be performed, as well as thorough sensitivity analyses. The model is designed to analyse optimal biomass and hydrogen usage, as a complement to integrated assessment and power system models

Future renewable energy targets in the EU: Impacts on the German transport

The transport sector is at the center of discussions on accelerating the energy transition due to its still increasing contribution to greenhouse gas emissions worldwide; therefore, the EU has set binding targets for the use of renewable energy in transport through the Renewable Energy Directive. To analyze the economic impact of these targets, we developed an optimization model that considers bio- and electricity-based fuel options, various transport sectors, and future policy requirements. Our study of the German transport sector found that imported alternative fuels play a key role in reducing fossil fuel usage. We also identify two technological and managerial obstacles: policymakers need to prioritize the rapid electrification of vehicles in the near future; and in the distant future, more attention is needed in research for new technologies in commercial transport. Although our findings are tailored to Germany, the employed approach can be transferred to other models and countries