DÜNYANIN VE TÜRKİYE’ NİN DOĞAL GAZ TALEP SENARYOSU

Although the global energy demand varies from country to country, it is constantly increasing on a global scale. As per IEA’s projections, the usage of two energy sources will increase (renewable with 12% and natural gas with 28%) in the global energy demand until 2040. In the study, 48 number of experts/managers (Decision Makers-DM) working in the energy sector were interviewed to establish/determine 10 main criteria and 43 sub-criteria used in demand scenarios. In the study, fuzzy multi-objective mathematical model (by using fuzzy AHP, and fuzzy TOPSIS) is developed to calculate World's and Turkey’s natural gas demand under high and low demand scenarios. By the help of model, the usage of natural gas amount in World by regions between 2020 and 2030 is calculated. In Scenario-High it will increase by approx. 26 % between 2020 and 2030 and reached 4.800 bcm in 2040. In Scenario-Low it will increase by approx. 5 % from 2020 to 2030 and reached 4.000 bcm in 2030. It is the only fossil fuel expected to grow beyond 2030 since it is clean energy source. In Scenario-High natural gas demand by region is calculated/projected as follows: in 2030 North America 1250 bcm, Central and South America 250 bcm, Europe 650 bcm, Middle East 750 bcm, Eurasia 650 bcm, Asia Pacific 1250 bcm. In the study, under the high demand scenario it has been calculated that the usage of natural gas in Turkey will increase by 52% between 2020 and 2030 and reach approximately 76 bcm, and in the low demand scenario Turkey's total natural gas demand will decrease by approximately 9% and reach approximately 45 bcm. In the study by using Fuzzy TOPSIS method, 10 number of sectors are examined and “Energy sector” was the first and “Industry sector” was the second in the ranking of the sectors in terms of global and Turkey’s natural gas demand scenario. In the study, the usage of natural gas is the only fossil resource that is expected to increase in the global energy mix among fossil fuels in 2030. This is due to high reserve amount of natural gas, i.e. global conventional natural gas reserves with 206 trillion m3 and unconventional unexplored natural gas reserves with 354 trillion m3, and as well as being a clean and environmental-friendly energy source. Since it is a clean fossil fuel and it pollutes nature & air much less than other fossil fuels and has a minimum greenhouse gas emission amount compared to other fossil sources.

Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand

Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.

A comparative framework to impact assessment of objective function structure and supply/demand scenario on hydropower operation

The structure of objective functions in the reservoir optimization problem indicates the type of attitude to operation. This article presents an analytical framework to improve the structure of objective function by comparing 6 various forms of single-objective and bi-objective problems. Problems 1 and 2 were defined to compare two perspectives of operation, water supply versus energy generation. Problem 3 was also designed to examine the effect of the intra-annual electricity demand which was ignored in problem 2. Comparison of problems 4 and 5 shows the simultaneous effect of realistic water and electricity demand scenarios on finding an optimal Pareto front. Problem 6 considers a supply policy in which maximum hydropower generation in peak months is main strategy to reduce socio-economic tensions. These problems were analyzed for a period of 72 months in the operation of the Dez reservoir in the southwest of Iran. The results of comparisons showed that the average annual water supply in problem1 is 334 Mm3 higher than problem2, while the mean annual hydropower generation in problem2 compared to problem1 increases by 58.9 GWh. Hydropower generation in problem2 compared to problem3 experiences a 31.8% decrease in the peak period and a 111% increase in the non-peak months, it can impose significant problems on the National Electricity Network. The Pareto front for the problem 5 is better than the problem 4 at all points, meaning that the demand coefficient improves the Pareto front. The solutions of problem 6 can result in efficient meet of water and electricity demand in critical periods and incredibly improve practical planning.

COMPARING SCHEDULING APPOINTMENT RULES PERFORMANCE IN HEALTH CARE UNITS: A DISCRETE EVENT SIMULATION APPROACH

Health care systems are affected by sudden increases in demand that can be generated by factors such as natural disasters, terrorist attacks, epidemics, among others. Patient demand can be divided between scheduled and walk-in and, in pandemic scenarios, both of them must be managed in order to avoid higher patient waiting times or number in queue. A discrete event simulation model is proposed in order to evaluate critical indicators like: patient waiting times, number in queue, resource utilization (doctors), using four different patient schedule appointment rules. In this study it was also considered patients impunctuality, walk-in patients and no-show in different scenarios. The best schedule appointment rules for each demand scenario were evaluated. After comparing six performance indicators, four schedule appointment rules in nine different scenarios it was found that the most known scheduling rule had the lowest queue sizes at scenarios with low or no walk-in patients, whereas, as the unpredictability of the scenarios rose, other rules outperformed it. It was also presented to exist an inverse relation between queue size and the physician idle time. Keywords: discrete event simulation, idle-time, queue management, appointment scheduling, health care.

Trade-Offs between Agricultural Production, GHG Emissions and Income in a Changing Climate, Technology, and Food Demand Scenario

Climate-smart agriculture targets integrated adaptation and mitigation strategies for delivering food security and greenhouse gas emissions reduction. This study outlines a methodology to identify the trade-offs between food production, emissions, and income under technology and food demand-shift scenario and climate change. The methodology uses Climate Smart Agricultural Prioritization (CSAP) toolkit a multi-objective land-use allocation model, and detailed databases, characterizing the agricultural production processes at the land-unit scale. A case study has also been demonstrated for Bihar, a state in India. The quantification of trade-offs demonstrates that under different technology growth pathways alone the food self-sufficiency for Bihar cannot be achieved whilst the reduction in emission intensity targets are achievable up to 2040. However, both food self-sufficiency and reduction in emission intensity can be achieved if we relax constraints on dietary demand and focus on kilo-calories maximization targets. The district-level analysis shows that food self-sufficiency and reduction in emission intensity targets can be achieved at a local scale through efficient crop-technology portfolios.

Mathematical Analysis of Uncertainty in Bangladesh Agricultural Sector

This study presents three different mathematical models for profit optimization of agricultural products in Bangladesh. The prime focus of the paper has been to develop a Mixed Integer Linear Programming (MILP) model and analyze this model for two situation of demand uncertainty. Considering demand will be known before and after production. For the mentions of above two cases, we investigate the change of solution applying least demand, maximum perhaps demand and extreme demand scenarios. I think this is real life problem and this analysis will be helpful for all types of agricultural producers. The proposed MILP model is to maximize the total profit and also to estimate the profitable production locations. The formulated MILP model were solved by A Mathematical Programming Language (AMPL) and results obtained by appropriate solver MINOS. Numerical example with the sensitivity of several parameters has been deployed to validate the models. Results show that maximum perhaps demand scenario gets better solution according to our expected value compare of other two scenarios.

Facing COVID 19 pandemic: experience from a clinical microbiology laboratory in Spain

Objectives: To calculate the turnaround time of results to be able in the electronic medical record. Postulate some available tools regarding laboratory management to assume full response in an increasing demand scenario. Materials and methods: Retrospective analysis of all samples reaching the lab since February 17 until May 10 was performed using LIS. Records of personnel management and equipment delivery were consulted. Time to results was measured as the difference in hours between time of analytic request and the date of result upload to the electronic medical record. Results: Time to result started at 24 hours and continually decreased over time reaching stability on week 10 around 6.5 hours. Active measurements taken fall into groups: personnel management, Laboratory schedules and technical capacity. Conclusion: Adoption of an uninterrupted sample processing method (24/7) and the implementation of high throughput systems are the best options for increasing results performance, where other measurements like redistributing and re train personnel would be more successfully implemented. Keywords: clinical laboratory services, SARS-CoV-2, medical laboratory personnel, clinical laboratory techniques, health policy, COVID-19

Controlling transient gas flow in real-world pipeline intersection areas

Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network, they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany’s largest transmission system operators to develop a decision support system for their dispatching center, we investigated how to automatize the control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed-integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real-world data, we demonstrate the performance of our approach.