Reflective Nested Simulations Supporting Optimizations within Sequential Railway Traffic Simulators

This article describes and discusses railway-traffic simulators that use reflective nested simulations. Such simulations support optimizations (decision-making) with a focus on the selection of the most suitable solution where selected types of traffic problems are present. This approach allows suspension of the ongoing main simulation at a given moment and, by using supportive nested simulations (working with an appropriate lookahead), assessment of the different acceptable solution variants for the problem encountered—that is, a what-if analysis is carried out. The variant that provides the best predicted operational results (based on a specific criterion) is then selected for continuing the suspended main simulation. The proposed procedures are associated, in particular, with the use of sequential simulators specifically developed for railway traffic simulations. Special attention is paid to parallel computations of replications both of the main simulation and of supportive nested simulations. The concept proposed, applicable to railway traffic modelling, has the following advantages. First, the solution variants for the existing traffic situation are analyzed with respect to the feasibility of direct monitoring and evaluation of the natural traffic indicators or the appropriate (multi-criterial) function. The indicator values compare the results obtained from the variants being tested. Second, the supporting nested simulations, which potentially use additional hierarchic nesting, can also include future occurrences of random effects (such as train delay), thereby enabling us to realistically assess future traffic in stochastic conditions. The guidelines presented (for exploiting nested simulations within application projects with time constraints) are illustrated on a simulation case study focusing on traffic assessment related to the track infrastructure of a passenger railway station. Nested simulations support decisions linked with dynamic assignments of platform tracks to delayed trains. The use of reflective nested simulations is appropriate particularly in situations in which a reasonable number of admissible variants are to be analyzed within decision-making problem solution. This method is applicable especially to the support of medium-term (tactical) and long-term (strategic) planning. Because of rather high computational and time demands, nested simulations are not recommended for solving short-term (operative) planning/control problems.

A Hybrid ANP Method for Evaluation of Government Data Sustainability

The evaluation of government data sustainability is a multicriteria decision making problem. The analytic network process (ANP) is among the most popular methods in determining the weights of criteria, and its limitation is the un-convergence problem. This paper proposes a hybrid ANP (H-ANP) method, which aims to improve the ANP by combining the weights obtained from the analytic hierarchy process (AHP). The proposed method is proved to be convergent since the network of the H-ANP is strongly connected. According to the simulation experiments, H-ANP is more robust than ANP under different settings of parameters. It also shows a higher Kendall cor-relationship and lower MSE with respect to AHP, compared with the existing method (e.g., the averagely connected ANP method). An empirical example is also provided, which uses H-ANP to evaluate the government data sustainability of a city.

USING A MULTI-CRITERIA DECISION-MAKING MODEL TO EVALUATE AND SELECT AN E-COMMERCE PLATFORM

The COVID-19 pandemic has led to disruptions in consumers' lifestyles and purchases, as well as businesses' online business models. Online platforms are increasingly used for shopping purposes. To evaluate and choose an e-commerce platform requires using many criteria and decision makers. Therefore, the process of evaluating and selecting an e-commerce platform is viewed as a multi-criteria decision-making problem. The objective of this study is to develop a multi-criteria decision-making model to help consumers evaluating the e-commerce platforms. In the proposed model, the ratings of alternatives and the weights of the criteria are evaluated using the linguistic variable. Simulation examples are used to show the effectiveness of the model in practice. Keywords: Fuzzy TOPSIS, E-Commerce Platform, Mcdm, Fuzzy Sets.

Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

Microgrid with hydrogen storage is an effective way to integrate renewable energy and reduce carbon emissions. This paper proposes an optimal operation method for a microgrid with hydrogen storage. The electrolyzer efficiency characteristic model is established based on the linear interpolation method. The optimal operation model of microgrid is incorporated with the electrolyzer efficiency characteristic model. The sequential decision-making problem of the optimal operation of microgrid is solved by a deep deterministic policy gradient algorithm. Simulation results show that the proposed method can reduce about 5% of the operation cost of the microgrid compared with traditional algorithms and has a certain generalization capability.

Samsun İlinde Kurulabilecek Kompost Tesislerinin COCOSO ve WASPAS Yöntemiyle Optimallik Sıralaması

The compost product, which offers many benefits such as the evaluation of organic wastes, improvement of soil structure, neutralization of toxins and pH balance of the soil, has significant potential for the improvement of our country's lands. Considering the development of animal existence in our country, the production of compost product to be obtained from feces, which is the product of these animal beings, is an issue that needs to be emphasized. The choice of plant location, which must be determined for an investment to be made for the acquisition of this product emerges as a separate problem. For this reason, in this study, the order of optimality among the alternatives for compost plant installation is considered as a multi-criteria decision making problem. For this purpose, the criteria determined for 10 clusters with the potential of 35,829 animals that can produce compost in Samsun were weighted by the SWARA method. The optimal ranking of these 10 compost clusters was carried out using the COCOSO and WASPAS methods, by means of the criteria weights taken into consideration. According to the ranking results obtained from both methods, it was determined that the cluster number 27 was in the first rank, the cluster no 13 was in the second rank, and the cluster no 14 was in the third rank.

Multi-attribute Group Decision-making (MAGDM) using Fuzzy Linguistic Modeling Integrated with the VIKOR Method for Car Purchasing Model

The change in the trend of transportation, increasing per capita income, expectation of better lifestyle, easy finance, and reduced cost of the automobile are some of the main factors that enable a commoner to have his/her own car. Therefore, it is essential to comprise such features in cars that offer qualities enabling the ease of consumer’s decision-making and comfort to purchase a car individually. Purchasing a car is a complicated multi-criteria decision-making problem as an individual may have different preferences for different criteria attributes. The attributes may be conflicting in nature depending on the need of the individual customer. Generally, it becomes quite difficult to assign ratings to these attributes based on numeric values. Therefore, the decision-making process relies on an idiosyncratic finding of the decision-makers which is in practice fuzzy with uncertainities. Hence, this article is a case study that deals with a hierarchy MCDM approach in accordance with the fuzzy logic and VIKOR method to solve a car purchasing problem.

Creating a common priority vector in intuitionistic fuzzy AHP: a comparison of entropy-based and distance-based models

In the case of conflicting individuals or evaluator groups, finding the common preferences of the participants is a challenging task. This statement also refers to Intuitionistic Fuzzy Analytic Hierarchy Process models, in which uncertainty of the scoring of individuals is well-handled, however, the aggregation of the modified scores is generally conducted by the conventional way of multi-criteria decision-making. This paper offers two options for this aggregation: the relatively well-known entropy-based, and the lately emerged distance-based aggregations. The manuscript can be considered as a pioneer work by analyzing the nature of distance-based aggregation under a fuzzy environment. In the proposed model, three clearly separable conflicting groups are examined, and the objective is to find their common priority vector, which can be satisfactory to all participant clusters. We have tested the model results on a real-world case study, on a public transport development decision-making problem by conducting a large-scale survey involving three different stakeholder groups of transportation. The comparison of the different approaches has shown that both entropy-based and distance-based techniques can provide a feasible solution based on their high similarity in the final ordinal and cardinal outcomes.

Event-Based Evaluation of Electricity Price Ensemble Forecasts

The present paper considers the problem of choosing among a collection of competing electricity price forecasting models to address a stochastic decision-making problem. We propose an event-based evaluation framework applicable to any optimization problem, where uncertainty is captured through ensembles. The task of forecast evaluation is simplified from assessing a multivariate distribution over prices to assessing a univariate distribution over a binary outcome directly linked to the underlying decision-making problem. The applicability of our framework is demonstrated for two exemplary profit-maximization problems of a risk-neutral energy trader, (i) the optimal operation of a pumped-hydro storage plant and (ii) the optimal trading of subsidized renewable energy in Germany. We compare and contrast the approach with the full probabilistic and profit–loss-based evaluation frameworks. It is concluded that the event-based evaluation framework more reliably identifies economically equivalent forecasting models, and in addition, the results suggest that an event-based evaluation specifically tailored to the rare event is crucial for decision-making problems linked to rare events.