Seasonal Demand Research Articles

Tourism demand with subtle seasonality: Recognition and forecasting

Existing studies on tourism seasonality have been mainly identified at annual or monthly level and the seasonality in tourism demand forecasting has always been addressed by modeling season patterns. However, annual or monthly seasonality is coarse-grained and can’t capture the subtle changes emerging both in tourism theory and practice. This study recognizes tourism seasonality based on intra-day patterns and inter-day similarity and suggests a novel approach to addressing seasonality in tourism demand forecasting. The proposed three-step method contains tourism seasonality recognition, tourism seasonality matching, and tourism demand forecasting. The empirical findings, based on two attractions in China, demonstrate that the proposed method based on dynamic time warping and density-peak clustering can precisely capture tourism seasonality at the daily level. The method can also detect special tourism periods or subtle changes in seasonality, such as staggered peak travel phenomenon. Superior forecasting performance with seasonality matching is also revealed. This study sheds new light on tourism seasonality recognition and contributes to forecasting methodology.

Techno-economic-environmental performance of multi-stage evaporation based poly-generation system under multiple energy supply scenarios

To fulfil the seasonal energy demand in buildings, a novel thermally-driven poly-generation system is proposed based on organic Rankine cycle (ORC) and two-stage vapor compression cycle (TSVCC). The thermodynamic model of the combined cooling, heating and power (CCHP) system was constructed based on the laws of thermodynamics and energy-supply scenarios were also analyzed. Techno-economic-environmental performance of CCHP system was analyzed and compared with the traditional standalone energy-supply modes. Results show that the heating capacity of novel poly-generation system is at least 11% higher than single heating system. For the power generation performance, the novel poly-generation system is almost the same with the traditional ORC. The cooling capacity of poly-generation system is 8% worse than absorption refrigeration system due to the relatively low efficiency of ORC. The poly-generation system can meet the seasonal demand in buildings, the overall performance becomes more attractive with a rising heat source temperature, optimal operating parameters and proper working fluid. The net power output is 3199.37 kW and payback period will lower than 3 years if the heat source temperature is 473.15 K and R1233zd as the working fluid. Therefore, the proposed poly-generation system is excellent and should be employed in engineering applications.

Physicochemical Parameters of Water and Its Implications on Avifauna and Habitat Quality

Wetland ecosystems are essential for maintaining biological diversity and are significant elements of the global landscape. However, the biodiversity of wetlands has been significantly reduced by more than 50% worldwide due to the rapid expansion of urban areas and other human activities. The aforementioned factors have resulted in drastic antagonistic effects on species composition, particularly aquatic avifauna. The decline in wetland avifauna, which can be attributed to changes in water quality that impact aquatic habitats, is a major concern. In this study, we evaluated the impact of physicochemical parameters on aquatic avifauna in India’s first Conservation Reserve, a Ramsar site and an Important Bird Area. Water samples were collected on a monthly basis across nine different sites and various parameters, such as temperature, electrical conductivity, pH, biological oxygen demand, dissolved oxygen, total dissolved solids and salinity, were analyzed for pre-monsoon and post-monsoon seasons, while point count surveys were conducted to assess species richness and the density of waterbirds. Our findings show a positive correlation of species density with water temperature (r = 0.57), total dissolved solids (r = 0.56) and dissolved oxygen (r = 0.6) for pre-monsoon season and a negative correlation for dissolved oxygen (r = −0.62) and biological oxygen demand (r = −0.69) for post-monsoon season. We suggest that a synergistic effect of pH, salinity, biological oxygen demand and total dissolved solids may affect aquatic bird populations in Asan Conservation Reserve. Poor water quality was observed in a few sampling sites, which may negatively affect the number and density of waterbirds present. The findings of this study emphasize the importance of water quality in wetland conservation, particularly for aquatic avifauna.

Reconnaissance Survey for Potential Energy Storage and Carbon Dioxide Storage Resources of Petroleum Reservoirs in Western Europe

Energy producers and utilities use oil and gas reservoirs for gas storage to meet peak seasonal demand or to supplement intermittent energy production. These reservoirs are also suitable for the long-term storage of carbon dioxide (CO2), a greenhouse gas. This study reports on a reconnaissance analysis of the potential magnitude of storage resources in 9424 known oil and gas reservoirs from 24 countries within highly industrialized western Europe. To standardize the storage resources of the oil and gas reservoirs, their volumetric capacity is expressed in terms of metric tons (mass) of CO2. Estimates of recoverable oil and gas at the surface are converted to subsurface volumes and then converted to the equivalent mass of CO2 at reservoir conditions. The results indicate 36.7 gigatons of CO2 could be stored, with oil reservoirs accounting for 32% of that total and natural gas reservoirs comprising the remaining 68%. About four-fifths of the reservoir storage resource is offshore, with about three-fourths of that offshore resource at water depths of 200 m or less. Most countries do not have the reservoir storage resources to store 15 years of CO2 at 2017 emission levels. With few exceptions the bulk of the storage is offshore for countries that do have at least 15 years of storage. The expansion of natural gas storage for strategic purposes in abandoned onshore gas reservoirs is not expected to seriously impact CO2 storage. The contribution of this analysis is the description of the spatial distribution of potential storage and physical accessibility.

Examining Seasonality in Tourism with Special Reference to the Recent Effects of Covid-19 – The Case of the Sopron-Fertő Tourism Destination (Hungary)

Abstract Seasonality, meaning that the vast majority of tourist locations do not distribute arrivals evenly throughout the year, in tourism, it is widely regarded as one of the most crucial problems for the majority of the world’s destinations, particularly in terms of sustainability. Nevertheless, despite its importance and that this unbalanced nature has become one of tourism’s most distinguishing characteristics, we can say that no globally comparable international measurement of seasonality is currently available. Based on the abovementioned phenomena, the main objective of the present study and research is to detect and analyse the tourism seasonality of a case study area, the Sopron-Fertő destination (Hungary), using multiple methodologies such as the Gini index, a representative consumer questionnaire and expert interviews. The analysis demonstrates seasonality in tourism demand between 2008–2021, based on which implications on product development and new directions and challenges, influenced by the most recent impacts of the COVID-19 pandemic, are revealed and suggested.

Seasonality of tourism demand in Turkey: a multi-methodical analysis

ABSTRACT Analyzing seasonality in the modern tourism industry is essential for successful organization and destination management. The purpose of this paper is therefore to provide an in-depth analysis of seasonality in the main destinations in Turkey. The methods used include the seasonal index of the time series model, the seasonality indicator, the Gini coefficient, the Theil index, the Lorenz Curve, and the coefficient of variation. The results suggest that domestic tourism was moderately seasonal, whereas inbound tourism was highly seasonal during 2017–2020. Moreover, capacity utilization on average was approximately half of the relative capacity, which indicates a substantial discrepancy. One of the salient findings of this study is that the novel coronavirus disease has aggravated the seasonal fluctuations in the country’s tourism demand. In addition, the paper provides an adjustment to an interpretation of the Gini coefficient.

THE IMPACT OF COVID-19 ON AGRI-FOOD ENTERPRISES IN THE PELOPONNESE REGION

When the COVID-19 pandemic broke out, it affected the economic and social life of people worldwide. This paper aims to investigate the problems arising from the pandemic in the production process of 405 agri-food enterprises including producers (farmers, livestock farmers, fishermen), processors/micro, small, and medium agri-food enterprises located in a predominantly rural area of Greece, the Peloponnese region. The data were obtained by a detailed online questionnaire survey (quantitative analysis). According to the analysis of the results, it was found that the responses regarding the distribution of products, were almost equally divided between low and high difficulty. Furthermore, despite the increased production costs by implementing sanitary protocols, the agri-food enterprises absorbed it internally and did not pass it through the price of the product. Both the quantity and the quality of produced foodstuffs were practically hardly affected. Also, agri-food enterprises have not been seriously affected by product loss and waste. Furthermore, lockdowns are contributing to labor shortages for agriculture enterprises, particularly those characterized by periods of peak seasonal labor demand. The usage of e-shops was limited and almost half of the enterprises were not satisfied with State funding. The uncertain economic environment creates insecurity among producers and agri-food entrepreneurs for sustainability and future investments. Local and regional authorities together with the State must take relief measures to provide financial support to producers and agri-food entrepreneurs, so that the food supply chain functions properly and we do not face a food crisis

Modeling Flow and Transport in Saline Aquifers and Depleted Hydrocarbon Reservoirs for Hydrogen Energy Storage

Summary Hydrogen (H2) is an attractive energy carrier, and its true potential is in decarbonizing industries, such as for providing heat for buildings and being a reliable fuel for trains, buses, and heavy trucks. Industry is already making tremendous progress in cutting costs and improving the efficiency of hydrogen infrastructure. Currently, heating is primarily provided by using natural gas and transportation by gasoline with a large carbon footprint. Hydrogen has a similarly high energy density, but there are technical challenges preventing its large-scale use as an energy carrier. Underground geologic storage of hydrogen in porous media (aquifers and hydrocarbon reservoirs) could offer substantial storage capacity at low cost as well as buffer capacity to meet changing seasonal electricity demands or possible disruptions in power supply. Underground geologic storage must have adequate capacity and ability to inject/extract high volumes with a reliable caprock. A thorough study is essential for a large number of site surveys to locate and fully characterize the subsurface geological storage sites both onshore and offshore. An isothermal compositional reservoir simulator was used to evaluate hydrogen storage and withdrawal from saline aquifers and depleted oil/gas reservoirs. The phase behavior, fluid properties, and petrophysical models were all calibrated against published laboratory data for density, viscosity, relative permeability, and capillary pressure for a given site. History-matched dynamic models of two CO2 injection field projects in saline aquifers and one natural gas storage in a depleted oil reservoir were considered as hypothetical hydrogen seasonal storage sites. A wide range of working gas volume/cushion gas volume ratios was observed, meaning that careful optimization is required for a successful storage project. For the aquifer cases, the range was 0.292 to 1.883 and a range of 1.045 to 4.4 was observed for the depleted hydrocarbon reservoir scenarios. For the saline aquifer cases, a higher injection rate, longer injection/withdrawal (I/W) cycles, and the use of pump wells to control the hydrogen plume spreading were all beneficial for improving the working gas/cushion gas ratio and the working gas volume. Plume control was important for storage in the oil reservoir in which changes in the well length location and orientation showed high sensitivity in the working and cushion gas volumes. Sensitivities to the initial gas saturation in the depleted gas reservoir scenarios suggested that both cushion and working gas volumes increased with the initial gas saturation while the ratio of working to cushion gas volumes decreased with the initial gas saturation. Finally, when comparing the ratios of working to cushion gas volumes, it was the highest for the depleted oil reservoir, followed by the depleted gas reservoir, and the aquifer.

Validating the dairy marketing performance of Mizan-Aman town, Bench-Sheko zone, Ethiopia

The broad objective of the study was to investigate the determinants of dairy market in Mizan-Aman town, Bench-Sheko zone, Ethiopia. For the purpose of the study, 228 sample households were selected from the town using a stratified random sampling technique and the field data were collected using questionnaires. The binary linear regression model analysis was employed to identify determinants that affect the dairy market. The model result showed that dairy market was strongly and significantly affected by use of improved feed, demand for milk, frequency of getting training, access for credit, and education of the household heads. Shortage of feed and seasonality of demand, particularly in fasting time are challenges of dairy marketing in the area. Dairy producers, retailers, farms, and cooperatives were found the main milk market channel of the study area. The dairy market in the study area uses an informal marketing system which shows the underdevelopment of dairy marketing. Thus, dairy development interventions should be aimed at addressing both dairy production technological gaps and marketing problems. Further, the study shows that there is a high demand for dairy products. Therefore, dairy processing industry’s establishment, support for dairy producers and improving access to services should receive due attention in order to improve dairy market in the study area by all dairy development stakeholders

Availability of Supplemental Corn for Sandhill Cranes, Light Geese, and Dabbling Ducks Wintering in New Mexico

Abstract The Middle Rio Grande Valley of central New Mexico overwinters the majority of the Rocky Mountain Population of greater sandhill cranes Antigone canadensis tabida and numerous Midcontinent Population lesser sandhill cranes A. c. canadensis, light geese (i.e., snow Anser caerulescens and Ross's geese Anser rossii), and dabbling ducks (i.e, Anas spp.). Stemming from changes in agricultural practices and loss of habitat, these species have become largely dependent on public lands where corn supplements natural foraging resources, providing a key dietary component while also discouraging crop depredation on private lands. To evaluate if supplemental corn provides a sufficient resource base, we estimated energy available in corn resources and seasonal energy requirements of the sandhill cranes, which consisted of the Rocky Mountain Population and Midcontinent Population. Additionally, we considered energetic requirements for two other wintering guilds, light geese and dabbling ducks. Depletion rates of postmowed corn increased throughout winter from 3,673 ± 843 kg/ha/d (mean ± SE) in December to 7,014 ± 1,884 kg/ha/d in February. The estimated seasonal energetic requirement of the Rocky Mountain Population and Midcontinent Population was 1.14 × 109 kcal (95% CI = 1.07 × 109 to 1.21 × 109 kcal). The combined seasonal energetic demand of the Rocky Mountain Population, Midcontinent Population, light geese, and dabbling ducks was 4.23 × 109 kcal (95% CI = 3.81 × 109 to 4.53 × 109 kcal) or 911,813 kg of corn (95% CI = 820,821–1,006,894 kg). From 2014 to 2017, corn production on public lands in the Middle Rio Grande Valley was 1,052,006 ± 58,965 kg grown on an average of 76 ha, sufficient for winter energetic requirements of all guilds. Interagency collaboration, effective planning during the growing season, and strategic schedules for mowing supplemental corn crops that integrate population abundances and their respective energy demands should further promote success of the supplemental corn program in the Middle Rio Grande Valley.

Forecasting the Tourism Demand of Türkiye Using Artificial Neural Network (ANN) Approach

Modelling and forecasting the tourism demand is an important research area for both public and private sectors for planning the travel and accommodation facilities in advance. Therefore, modelling the tourism demand in terms of the number of tourists is an active research area for both tourism, economics and financial studies. In this work, the tourism demand of Türkiye is modelled using artificial neural network methods. The tourism demand represented by the number of tourists is taken as the dependent variable while the past values of the number of tourists is considered as the inputs. Furthermore, the tourism revenue is also modelled employing the same autoregressive artificial neural network approach. Monthly data in the period of 2008-2022 which are taken from the official sources are used as the research material. The multilayer perceptron type artificial neural networks with nonlinear neural activation functions is selected for the accurate modelling of the highly seasonal tourism demand data. The modelling and forecasting results show that the developed artificial neural network reaches high accuracy for modelling both the tourism demand and the tourism revenue such that the coefficient of determination of the developed artificial neural network models have the values of R2=0.949 and R2=0.840 for the tourism demand and tourism revenue, respectively, providing an objective assessment of the accuracy of the developed models. It is concluded that the multilayer perceptron based artificial neural network methods can be used to model the tourism demand therefore this type of modelling is expected to be helpful for tourism planners by providing them the tourism demand forecast in advance.

OPTIMIZATION OF LOGISTICS PROCESS MANAGEMENT IN TRADE

The article considers the theoretical and methodological and practical principles of logistics processes management. Current trends in logistics development are outlined. It is shown that the trends demonstrate active implementation in the management processes of the elements of Logistics 4.0. The expediency of using economic-mathematical methods and models to solve logistics tasks is reasoned. An overview of ecommerce processes, typical for today, both in the world and in Ukraine, which directly cause changes in logistics, is made. The problem of optimization of transport costs for delivery of goods from the warehouses of the online store "Hope" to customers, taking into account the non-stationarity (seasonality) factor is defined. The economic-mathematical model of the problem of logistics processes management has been built and adapted as follows: selection of two seasonally-leased warehouses from five reserve ones and optimization of transport costs for delivery of goods from warehouses of the online store to customers. This optimization model was implemented to solve the problem of optimizing logistics costs for the online store "Hope", taking into account seasonal demand, transport tariffs, warehouses capacity and forecasted sales volumes. Various ways to solve the problem of logistics process management, which allows us to show the additional benefits of using these methods and determine the effectiveness of the decision taken are considered as well. The solution for the choice of warehouses and the optimal plan for delivery of goods to customers in order to minimize transportation costs is obtained. It is shown that with the use of economic and mathematical modeling to solve the problem of seasonally adaptive transition for logistics process management, it is possible to make the best choice of two additional leased warehouses from the five reserve ones, to find the optimal delivery plan, to reduce the total length of delivery route and, consequently, the costs of transportation of goods, to avoid shortages of goods in warehouses, in a timely manner and with minimal costs to satisfy the interests of customers of online store "Hope" goods. The economic interpretation of the results of the models implementation is presented and recommendations for their use are provided to achieve minimization of transportation costs, maximum flexibility, speed, reliability of the supply chain, ie to make effective management decisions.

The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain

Amongst all future developments it is the electrification of heat that is anticipated to have the largest impact on seasonal and interannual electricity demand. There is therefore a need to accurately quantify and assess this impact. This paper uses a combination of existing advanced techniques to modify the historic electricity demand to incorporate the impact of heat pumps alone for long-term historic weather data using Great Britain as an example. The methods for generating time series were compared and extensively validated. This includes comparisons with measured data that have not been used previously for this purpose. The research reveals that for predicted 2050 heat pump penetration levels the monthly demand for electricity doubles in winter. This leads to an increase of approximately 30 TWh for each winter month and a 37 % increase in year-to-year variability of electricity demand due to weather. Peak electricity demand is very sensitive to the method of generating heat demand and the assumptions on hourly heat pump operating profiles, suggesting inaccuracies of 25 % in estimates of future peak demand. This work, rather than just assessing the impact of projected changes provides a reference case for policy makers to guide the decision process and planning for future scenarios.

LNG point supply of villages and towns in China: Challenges and countermeasures

Liquefied natural gas (LNG) point supply is one of the most flexible and convenient methods advocated by China for distributing energy to end-users such as towns and villages where it is difficult or uneconomical to construct gas pipelines. This study sorts out the advantages and challenges and designs the optimization model for LNG point supply in China based on a mixed-integer linear programming (MILP) model. In this model, the goal of optimization is to minimize economic and environmental costs in the construction and operation stages. By optimizing location-allocation, gas storage capacity, vaporization capacity, equipment selection, and pipeline distribution, the model meets the fluctuating seasonal demand for natural gas. This model design uses the actual construction and operation-related parameters of the vaporization station and pipeline, and applies the model to four different types of towns. Through sensitivity analysis, this study finds that the sensitivity of construction cost is relatively high.

Summer indoor queen banking as an alternative to outdoor queen banking practices

Honey bee queens are the most critical individuals of a complex eusocial society. Queen banking is the storage of queens individually in cages and placed in a colony to be cared for by worker bees. Queen producers bank excess queens as seasonal demand subsides in the summer to provide an on-demand supply to beekeepers. This approach, however, might be threatened by wildfires, decreased forage availability, and climate change. For this study, we compared current summer outdoor queen banking practices in northern California with banking in indoor temperature-controlled storage facilities to investigate the effectiveness of indoor storage as an alternative to outdoor storage in the summer. Treatments were separated into three groups: indoor queen banks, outdoor queen banks, and a set of unbanked control queens provided with three different stocking rates (50, 100, and 198 queens per bank). Queen quality parameters and survival data were assessed using laboratory and field assessment methods. We found no significant difference in queen quality parameters apart from the weight of indoor queens banked at the rate of 100, which were significantly lower than the other banking rates. Queens stored indoors had a significantly higher survival of 78 ± 1% than queens stored outdoors with a survival of 62 ± 3%. Indoor banking performed better in quality and survival as compared to outdoor queen banking. Therefore, queen banking has the potential to mitigate the valuable fall queen supply caused by rising hot summer temperatures.

Dimensioning and Lifetime Prediction Model for a Hybrid, Hydrogen-Based Household PV Energy System Using Matlab/Simulink

This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system.

A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.

Effects of Soil Data Accuracy on Outputs of Irrigation Scheduling Tools

Highlights The effects of soil data accuracy on estimated water fluxes by an irrigation scheduling model were investigated. Free and frequently used web soil survey (WSS) soil textural data underestimated sand particles in 89% of cases. Forty-nine percent of the estimated differences in seasonal irrigation based on WSS and measured soil data were within ±25 mm. In most cases, use of WSS data resulted in larger evaporation, smaller deep percolation, and larger runoff compared to those based on measured soil data. Abstract. A widely used irrigation scheduling method is based on modeling soil water balance, which requires several key inputs, including soil data. Many scheduling tools developed using this method rely on publicly available soil data, such as the United States Department of Agriculture's Web Soil Survey (WSS). While soil survey data are a valuable source of information for general farm and natural resource planning and management at large scales, inaccuracies in soil conditions at field and subfield scales can hamper efficient agricultural water management through irrigation scheduling tools. To illuminate the implications of the localized inaccuracies, this study estimated the errors in WSS soil textural data at 18 sites in three regions of western Oklahoma through comparison with in-situ sampling (ISS) data. The effects of errors on estimated water fluxes were also investigated for dominant crops of each region over a 15-year (2006-2020) period. The findings demonstrated that WSS soil textures were finer than ISS at most sites and soil layers, resulting in generally greater root zone total available water estimates. Differences in seasonal irrigation demand estimates when WSS data were used instead of ISS reached 20% at one site but were within ±9% among the regions. Half of the estimated seasonal irrigation differences for all sites, years, and crops were within ±25 mm. Soil evaporation, deep percolation, and runoff fluxes were also impacted by soil data source, albeit to a smaller degree than irrigation, at levels and directions (over or underestimation) that were dependent on the sign and magnitude of WSS errors, as well as precipitation amounts and timing. Overall, errors in WSS data may not have a major impact at regional scales, but the effects on individual irrigated farms may be severe depending on the magnitude of difference between WSS data and true soil conditions. Keywords: Irrigation demand, Soil water balance, SSURGO, Water fluxes, Web soil survey.

Mathematical support and software systems for spare parts management of grain harvesting machinery

Agricultural production is characterized by the seasonality of technological operations, the ability to carry out which in a strictly set time depends on many factors of the external environment and the reliability of agricultural machinery. The harvest of grain and other crops by combines occupies a special place among field works. Operative carrying out of these works without down time of the grain-harvesting techniques provides the minimum losses of the agricultural production. It is possible to minimize downtime of harvesters due to technical reasons if the enterprise has at its warehouse a seasonal reserve of spare parts that are in great demand during harvesting works. The methods of reserve calculation should consider not only the reliability of some harvester parts, but the cost damage of harvester downtime and extra costs of spare parts purchase and storage. With such a large number of external factors, traditional methods are difficult to calculate. With the development of computer technology, the methods of evolutionary calculations have been widely used, in particular, genetic algorithms that allow creating self-adjusting models capable of analysing the examined indicators for the past years, reacting to the changes of current external conditions, and making short-term forecasts of the values of optimised variables. The purpose of this research was to develop a genetic algorithm and software that will enable us to meet the seasonal demand for spare parts from combine harvesters. The software allowed us to identify 66 of the most in-demand items out of 2800 warehouse items that should be reserved for the season of harvesting crops. The efficiency of the proposed solutions is proved by the reduction of the downtime of combines due to technical reasons by 37% and the increase of their shift productivity by 11.4%; at the same time, the combine reliability index, the operational readiness factor, increases by 4.38%.

Study on fuzzy inventory model for deteriorating items with recurring seasonal demand pattern

Study on fuzzy inventory model for deteriorating items with recurring seasonal demand pattern