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    Tytuł:
    A two-stage stochastic programming approach for production planning system with seasonal demand
    Autorzy:
    Mahmoud, Asmaa A.
    Aly, Mohamed F.
    Mohib, Ahmed M.
    Afefy, Islam H.
    Powiązania:
    https://bibliotekanauki.pl/articles/952860.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    process manufacturing system
    two-stage stochastic programming
    sampling average approximation
    Opis:
    Seasonality is a function of a time series in which the data experiences regular and predictable changes that repeat each calendar year. Two-stage stochastic programming model for real industrial systems at the case of a seasonal demand is presented. Sampling average approximation (SAA) method was applied to solve a stochastic model which gave a productive structure for distinguishing and statistically testing a different production plan. Lingo tool is developed to obtain the optimal solution for the proposed model which is validated by Math works Matlab. The actual data of the industrial system; from the General Manufacturing Company, was applied to examine the proposed model. Seasonal future demand is then estimated using the multiplicative seasonal method, the effect of seasonality was presented and discussed. One might say that the proposed model is viewed as a moderately accurate tool for industrial systems in case of seasonal demand. The current research may be considered a significant tool in case of seasonal demand. To illustrate the applicability of the proposed model a numerical example is solved using the proposed technique. ANOVA analysis is applied using MINITAB 17 statistical software to validate the obtained results.
    Źródło:
    Management and Production Engineering Review; 2020, 11, 1; 31--42
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part II (The State of Nebraska)
    Autorzy:
    Ghahremanlou, Davoud
    Kubiak, Wieslaw
    Powiązania:
    https://bibliotekanauki.pl/articles/1818474.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    sustainable petroleum supply chain
    two-stage stochastic programming
    government policies
    Opis:
    The accompanying part I (Ghahremanlou and Kubiak 2020) developed the Lean Model (LM), a two-stage stochastic programming model which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW), to study the policy impact on the Sustainable Petroleum Supply Chain (SPSC) using cellulosic ethanol. The model enables us to study the impact by running computational experiments more efficiently and consequently by arriving at robust managerial insights much faster. In this paper, we present a case study of the policy impact on the SPSC in the State of Nebraska using the model. The case study uses available real-life data. The study shows that increasing RFS2 does not impact the amount of ethanol blended with gasoline but it might lead to bankruptcy of the refineries. We recommend that the government consider increasing the BW because of its positive economic, environmental and social impacts. For the same reason, we recommend that the tax credit for blending the US produced ethanol with gasoline be at least 0:189 $/gal and the tariff for imported ethanol be at least 1:501 $/gal. These also make the State independent from foreign ethanol thereby enhancing its energy security. Finally, the change in policy impacts the SPSC itself, most importantly it influences the strategic decisions, however setting up a bio-refinery at York county and a blending site at Douglas county emerge as the most robust location decisions against the policy change in the study.
    Źródło:
    Decision Making in Manufacturing and Services; 2020, 14, 1; 57--80
    1896-8325
    2300-7087
    Pojawia się w:
    Decision Making in Manufacturing and Services
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)
    Autorzy:
    Ghahremanlou, Davoud
    Kubiak, Wieslaw
    Powiązania:
    https://bibliotekanauki.pl/articles/1818475.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    sustainable petroleum supply chain
    two-stage stochastic programming
    government policies
    Opis:
    Environmental concerns and energy security have led governments to establish legislations to convert Conventional Petroleum Supply Chain (CPSC) to Sustainable Petroleum Supply Chain (SPSC). The United States (US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and consumption for the last half century. Though these regulations have created new opportunities, they have also added new burdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to study the impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM), which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policy impact on the SPSC using cellulosic ethanol. The model, as any other general model available in the literature, makes it highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derive a Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently by arriving at robust managerial insights much faster. We present a case study of the policy impact on the SPSC in the State of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak 2020).
    Źródło:
    Decision Making in Manufacturing and Services; 2020, 14, 1; 23--55
    1896-8325
    2300-7087
    Pojawia się w:
    Decision Making in Manufacturing and Services
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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