Aggregate production planning considering organizational learning with case based analysis

Attia, El-Awady; Megahed, Ashraf; AlArjani, Ali; El-Betar, Ahmed; Duquenne, Philippe

doi:10.1016/j.asej.2021.09.002

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…In production planning, various factors are considered, such as demand forecasting, availability of raw materials, production capacity, and labor capabilities [10] [11]. The result of production planning is a production schedule that outlines the types of products, quantities, and production time schedules for a certain period [12]. Production planning is important to achieve a balance between supply and demand, avoid excess or shortage of inventory, and optimize the use of company resources.…”

Section: Production Planningmentioning

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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Section: Production Planningmentioning

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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“…A way to dissolve such an issue is by using particular quadratic programming (e.g. Holt et al [8]), and numerous models have spawned henceforth [9][10][11][12][13][14][15][16], among which subjective criteria are rare in the majority and rather transformed into quantitative ones [17]. Aydin and Tirkolaee [18] conducted a systematic review of the literature on APP over the last 50 years.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints ( 9) and (10) specify that the quantity of products sold is between the minimum known demand and the maximum forecast demand. Constraint (11) ensures that either inventory or backorders are included in the solution, but not both. Constraints ( 12) and ( 13) ensure that the machine times for manufacturing the products are limited to the regular and overtime machine capacity in the manufacturing facility.…”

Section: Constraintsmentioning

confidence: 99%

Solving Aggregate Production Planning Problems: An Extended TOPSIS Approach

Kao

Chiang

et al. 2022

Applied Sciences

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Aggregate production planning (APP) was developed for solving the problem of determining production, inventory, and workforce levels to meet fluctuating demand requirements over a planning horizon. In this work, multiple objectives were considered to determine the most effective means of satisfying forecasted demand by adjusting production rates, hiring and layoffs, inventory levels, overtime work, back orders, and other controllable variables. An extended technique for order preference via the similarity ideal solution (TOPSIS) approach was developed. It was formulated to solve this complicated, multi-objective APP decision problem. Compromise (ideal solution) control minimized the measure of distance, providing which of the closest solutions has the shortest distance from a positive ideal solution (PIS) and the longest distance from a negative ideal solution (NIS). The proposed method can transform multiple objectives into two objectives. The bi-objective problem can then be solved by balancing satisfaction using a max–min operator for resolving the conflict between the new criteria based on PIS and NIS. Finally, an application example demonstrated the proposed model’s applicability to practical APP decision problems.

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