SALES FORECAST FOR AGGREGATE PLANNING: CASE STUDY OF AN INDUSTRIAL PRODUCTS COMPANY IN MEXICO

This case study presents the analysis through the use of sales estimation tools for planning demand for aggregate level as a finished product in a leading industrial products company in the market in Mexico. First, it aligned the demand plan and the supply plan, recommending the best execution scenario to increase operational efficiency and reduce the cost of operating the supply chain to increase the company's productivity and stay competitive. Then, after analysing the behaviour of the demand for selected products, the authors determined as the main affectation the inadequate precision of the method forecasting and the lack of an aggregate forecasting strategy that allows reducing the variation. Due to this, the most significant effort was concentrated on determining a better-forecasting model and the decision to aggregate the demand based on three relevant criteria: the demand pattern based on the Soft, Intermittent, Erratic or Irregular quadrant, the best method of the forecast for each product and the time in quarters. As a result, a reduction between 20% and 46% in the forecast variation can be obtained from the above.

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

Supply-Demand Matching of Medical Services in Haikou Under the Background of Hierarchical Diagnosis and Treatment - Based on Didi Travel Data

Background Implementation of the Healthy China Strategy and the hierarchical diagnosis and treatment system has injected new vitality into medical services. Given the insufficient supply of medical services and increasing demand for medical treatment, exploring the supply-demand pattern of medical services has become an urgent theoretical and practical problem to be solved. The equity of healthcare facilities has received widespread attention, but due to limited data, there is little research on the supply-demand pattern of medical services. This study focuses on evaluating the supply-demand matching pattern of medical services at different levels in Haikou City with big geographic data and promoting the realization of a balance between medical supply and demand. Methods This study utilizes spatial data of medical institutions, didi travel data, and population density data. Firstly, use the two-step floating catchment area method and GIS spatial analysis to explore characteristics of the supply-demand patterns of medical services at different levels in Haikou. Secondly, based on didi travel data, mine residents' demand for medical treatment. Then combined with population density data, divide supply-demand matching of medical institutions into four types. Finally, propose optimization strategies for the problems. Results The accessibility pattern of high-level medical institutions in Haikou presents high in the north and low in the south. The accessibility pattern of low-level medical institutions is the opposite. High-level medical institutions have a strong need for medical treatment, which is less hampered by distance. The healthcare demand of low-level medical institutions is small, and they mainly are medium- and short-distance medical travel. The types of medical services at different levels are mainly "low supply - low demand" and "high supply - low demand" types. Conclusions Medical services at different levels in Haikou are mainly in supply-demand imbalance. Therefore, we put forward optimization strategies to promote the equity of primary medical services, such as propelling the establishment and improvement of the hierarchical diagnosis and treatment system, building a new model of medical and health service supply, and strengthening balanced coverage of primary medical institutions. The mining of big geographic data is beneficial to alleviate the contradiction between medical supply and demand, although the data and methods need to be improved.

OPTIMUM UNIT COMMITMENT SOLUTION OF A POWER SYSTEM BASED ON SALP SWARM ALGORITHM

This paper presents an algorithm to solve the unit commitment problem in a power system. The proposed algorithm employs the Salp swarm algorithm technique to search the optimum unit schedule for a particular daily demand pattern and specific time horizon. Different constraints are taken into consideration, transition cost (start-up and shut down )cost, mean-up time, mean-down time, spinning reserve, and power balance. The proposed algorithm is applied to 10-units and 26-unit. The obtained results are compared with other methods. It reveals the robustness of the proposed algorithm in terms of minimizing overall running costs.

On Time-Dependent Trip Distance Distribution with For-Hire Vehicle Trips in Chicago

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

Identifying Ecosystem Service Critical Area

The purpose of this study was to establish a spatial multi-scale integrated assessment framework for critical areas of ecosystem service supply and demand, in order to provide theoretical support for regional ecological protection planning and refined management. Taking the typical hilly area of the upper reaches of the Yangtze River in China as an example, based on the assessment matrix of land use and ecosystem services, we used the method of spatial heterogeneity assessment and self-organizing feature mapping (SOFM) to explore the identification and regionalization of critical areas of ecosystem services at regional and small scales. The results show that there was spatial heterogeneity and scale effects of ecosystem services under the two scales. The small-scale supply–demand pattern was greatly affected by microtopography and land use patterns, and the importance of ecosystem services was as follows: forest area in the upper part of the mountain > orchard and dry area in the middle and lower part of the mountain > valley farming area > flat town and farming area. The regional-scale supply–demand pattern was greatly affected by landscape structure, location conditions and social economy, and the importance of ecosystem services was as follows: south > west > north > central. The SOFM network quantitatively identified four types of ecological areas with clear dominant functions at regional and small scales. The balance between supply and demand in the studied ecosystem service areas was I < II < III < IV, in which IV was the critical area in terms of supply and I was the critical area in terms of demand. This assessment framework can improve the spatial accuracy and objectivity of the quantification and mapping of ecosystem services, and provide new ideas for multi-scale identification and expression of ecosystem services.

Forecasting Production Trafo to Get SDOH Using Seasonal ARIMA Method in PT. XYZ

In the production process at PT. XYZ has a fluctuating data pattern and contains seasonality. This resulted in a reduction in the company's operational efficiency and difficulty in preparing supplies to meet uncertain demand. The method according to the demand pattern at PT. XYZ in this transformer product is the SARIMA method. The results of forecasting on transformer production at PT.XYZ gets the SARIMA(1,0,1)(1,1,1) model with influenced by the results observed at 13 weeks and errors at 14 weeks ago. The results of this forecast are used in determining the safety stock in 2021 with regard to SDOH. The SDOH planning in January 2021 will run out in 30 days with a stock plan of 838 units LV Busing so that a company policy needed to increase or decrease the stock plan if SDOH is below or above 30-35 days.

Market Transportasi UK: Regulasi atau Kompetisi

Tulisan ini merupakan ulasan market transportasi publik yang terjadi di Inggris terutama angkutan bus dan kereta api. Pada bagian awal membahas tentang mekanisme pasar layanan transportasi bus, kemudian bagaimana efek dari deregulasi yang dilakukan pada tahun 1990, serta bagaimana masalah dari deregulasi tersebut. Deregulasi berdampak pada ketidakstabilan pasar, pengaruhnya terhadap service atau layanan, kebijakan subsidi dan persaingan pada rute-rute yang disubsidi, alternative operator, hingga establishing demand pattern dan keinginan penumpang.

Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data

With the growth of the bike-sharing system, the problem of demand forecasting has become important to the bike-sharing system. This study aims to develop a novel prediction model that enhances the accuracy of the peak hourly demand. A spatiotemporal graph convolutional network (STGCN) is constructed to consider both the spatial and temporal features. One of the model’s essential steps is determining the main component of the adjacency matrix and the node feature matrix. To achieve this, 131 days of data from the bike-sharing system in Seoul are used and experiments conducted on the models with various adjacency matrices and node feature matrices, including public transit usage. The results indicate that the STGCN models reflecting the previous demand pattern to the adjacency matrix show outstanding performance in predicting demand compared with the other models. The results also show that the model that includes bus boarding and alighting records is more accurate than the model that contains subway records, inferring that buses have a greater connection to bike-sharing than the subway. The proposed STGCN with public transit data contributes to the alleviation of unmet demand by enhancing the accuracy in predicting peak demand.