Minimizing energy consumption via multiple installations aggregate production planning

Chaturvedi, Nitin Dutt

doi:10.1007/s10098-017-1376-3

Cited by 17 publications

(8 citation statements)

References 30 publications

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“…), and this is shown in Table (3) and for the year 2021 Note that this data was obtained from the records of that company. 2) Analysis of the results of the sub-contracting strategy: Table (5) shows that the months in which the contract is (positive), we mean that the production is greater than the demand, there is no need to contract, but for the months (6-9-11-12) the production is less than the demand and it is a sign (Negative) This matter pushes the company to contract with other companies to fill the shortfall or not, as the amount of production shortfall amounted to (24549) thousand tons in 2021, and the cost of contracting per ton (100,000) thousand dinars, i.e. at a total cost estimated at (2.454.900,000) billion, and this leads us to accept the second hypothesis which states that the company needs a sub-contract to fill the production shortfall, knowing that the company does not contract with other companies and in this case the company will be faced with several things, the first: If the company contracts with other companies to fill the shortfall, it does not achieve profits, but rather loses the amount of (3000) thousand dinars on each ton as expenses.…”

Section: Methodsmentioning

confidence: 99%

“…Many companies use the third strategy because it gives their users more flexibility in planning (Hahn & Brandenburg, 2018). If they are applied correctly, the company will be able to reduce its costs to the lowest possible limit and maximize its profits at the same time (Jamalnia et al, 2019) (Chaturvedi, 2017). Its outputs are inputs to the MRP system, which is the system that segments the products in detail and identifies the parts of each product.…”

Section: Aggregate Production Planning (App)mentioning

confidence: 99%

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Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

2022

Central European Management Journal

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Industrial organizations continuously seek to apply the foundations of total production planning strategies because of its positive role in helping these organizations determine the quantity of production required to meet market demand in the presence of crises and challenges such as epidemics and natural disasters, as well as accurately determining the organization's needs of workers, time and storage. Without an increase in that, this contributes to helping the organization reduce the total costs incurred by the product lab increase profits and achieve customer satisfaction by providing the product to him on time and at the appropriate cost. Through a case study in it, and to determine the sample of the study, the North Gas Company was chosen as a representative sample of other Iraqi gas companies. The study reached many conclusions, the most important of which was the company's suffering from the high cost of storage and the number of workers too large, which made the cost higher than your revenue. Harmony Search Algorithm contributed to reducing these costs and enhancing and improving revenues. The study also presented many proposals that address the problems of total planning. The company's production, thus improving its financial position.

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Section: Methodsmentioning

confidence: 99%

Section: Aggregate Production Planning (App)mentioning

confidence: 99%

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

2022

Central European Management Journal

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“…Demand forecasting has certain common characteristics that must be considered in evaluating a demand forecasting process and the forecasting methods used [23]. The characteristics of demand forecasting are as follows: -Causal factors that occurred in the past are assumed to remain relevant in the future.…”

Section: Forecastingmentioning

confidence: 99%

Production Planning and Control of Flooring Using Aggregate Planning Method

Rusindiyanto,

Rr. Rochmoeljati,

Yekti Condro Winursito

et al. 2023

Technium

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PT. X a is a private company engaged in the wood processing industry. To avoid production excesses or shortages in the company, production planning using the Aggregate Planning Method can be utilized to meet all consumer demands. The variables used include demand data, production costs, setup costs, and manufacturing time. Through this research, the company can fulfill consumer demands and minimize production costs by implementing aggregate planning. Additionally, the company can forecast future demand, enabling them to anticipate fluctuations in consumer demand. By using aggregate production planning, a total production time of 8657 hours can be scheduled to fulfill a demand of 7121 m3. The cost savings achieved after implementing aggregate planning. The total proposed production cost using aggregate planning is IDR.1.383.921.682 and actual cost is IDR 1.477.900.968. So that the company can save production costs of IDR 93,979,286 or 6.35%. For the total production cost of the upcoming year 2023 by implementing aggregate planning based on the forecasting data, the cost is IDR 1.534.711.812.

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“…Deterministic Single Aghezzaf & Artiba, 1998;Silva & JoÄOoisboa, 2000;Pradenas & Pe˜nailillo, 2004;Fahimnia et al, 2005;Pipery & Vachony, 2001;Singhvi & Shenoy, 2002;Techawiboonwong & Yenradee, 2003;Mazzola et al, 1998;Sillekens et al, 2011;Zhang et al, 2012;Ramezanian et al, 2012;Chaturvedi & Bandyopadhyay, 2015;Chaturvedi, 2017Multiple Leung & Chan, 2009Leung et al, 2003;Chakrabortty & Akhtar Hasin, 2013;Entezaminia et al, 2016;Abu Bakar et al, 2016;Mehdizadeh et al, 2018 Uncertain Fuzzy Single Baykasoglu, 1999;Wang & Fang, 2000;Fung et al, 2003;Tang et al, 2000;Tang et al, 2003;Ning et al, 2006;Aliev et al, 2007;Chen & Huang, 2010;Iris & Cevikcan, 2014;Rahmani et al, 2014;Chakrabortty et al, 2015Multiple Wang & Fang, 1997Baykasoglu & Gocken, 2006;Sakallı et al, 2010;Baykasoglu & Gocken, 2010;Sadeghi et al, 2013;da Silva & Silva Marins, 2014;Gholamian et al, 2016;Fiasché et al, 2016;Chauhan et al, 2017;…”

Section: Objective Function Articlesmentioning

confidence: 99%

Aggregate production planning: A literature review and future research directions

Cheraghalikhani¹,

Khoshalhan²,

Mokhtari³

2019

10.5267/j.ijiec

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Aggregate production planning (APP) is concerned with determining the optimum production and workforce levels for each period over the medium term planning horizon. It aims to set overall production levels for each product family to meet fluctuating demand in the near future. APP is one of the most critical areas of production planning systems. After the state-of-the-art summaries in 1992 by Nam and Logendran [ Nam, S. J., & Logendran, R. (1992). Aggregate production planning-a survey of models and methodologies. European Journal of Operational Research, 61(3), 255-272. ], which specifically summarized the various existing techniques from 1950 to 1990 into a framework depending on their abilities to either produce an exact optimal or near-optimal solution, there has not been any systematic survey in the literature. This paper reviews the literature on APP models to meet two main purposes. First, a systematic structure for classifying APP models is proposed. Second, the existing gaps in the literature are demonstrated in order to extract future directions of this research area. This paper covers a variety of APP models' characteristics including modeling structures, important issues, and solving approaches, in contrast to other literature reviews in this field which focused on methodologies in APP models. Finally some directions for future research in this research area are suggested.

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Minimizing energy consumption via multiple installations aggregate production planning

Cited by 17 publications

References 30 publications

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Production Planning and Control of Flooring Using Aggregate Planning Method

Aggregate production planning: A literature review and future research directions

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