Hierarchical production planning with demand constraints

Yan, Hong‐Sen; Xiaodong, Zhang; Jiang, Min

doi:10.1016/j.cie.2004.01.012

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Hongsen Yan constructed a model with linear objective function and constraints. And he could easily apply interaction\prediction algorithm to solve the problems of production plan for a large-scale manufacturing system [3], [4]. Silva formulated an optimal feedback control model to adjust aggregate industrial resources [12].…”

Section: ) In De Recombination Is Expressed By Cr (Cross Rate)mentioning

confidence: 99%

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Non-linear optimal control of manufacturing system based on modified differential evolution

Zhang¹,

Ding²

2006

The Proceedings of the Multiconference on "Computational Engineering in Systems Applications"

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This study considers the non-linear optimal control of the complex manufacturing system. We construct a mathematical model, which decomposes the complex manufacturing system into many correlated working centers, where the number of inventory and production evolves dynamically. It aims to minimize the cost of production control. We can utilize MDE algorithm to solve this model. The model can be easily used in multi-product, multi-center, multi-period manufacturing system.

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Section: ) In De Recombination Is Expressed By Cr (Cross Rate)mentioning

confidence: 99%

“…In their model, the demand was seen as a constant incorporated into the constraints. He applied lager-scale system control theory to solve HPP [3], [4]. In contrast to the previous work, many researchers have R. Zhang is an assistant professor of school of economics and management at the Beihang University of China.…”

mentioning

confidence: 95%

Non-linear optimal control of manufacturing system based on modified differential evolution

Zhang¹,

Ding²

2006

The Proceedings of the Multiconference on "Computational Engineering in Systems Applications"

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“…At the same time, such APP can provide production information to effectively utilize an organization's resources to satisfy various demands. Aggregate production planning determines not only which output levels have to be met and what appropriate input mix of resources should be used, but also how to meet prescribed requirements at minimum cost without violating capacity limits [3].…”

Section: Introductionmentioning

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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Integrated optimization of dynamic cell formation and hierarchical production planning problems

Xue

Offodile

2020

Computers & Industrial Engineering

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Hierarchical production planning with demand constraints

Cited by 9 publications

References 19 publications

Non-linear optimal control of manufacturing system based on modified differential evolution

Non-linear optimal control of manufacturing system based on modified differential evolution

Solving Aggregate Production Planning Problems: An Extended TOPSIS Approach

Integrated optimization of dynamic cell formation and hierarchical production planning problems

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