A method for the hierarchical planning of the structure, dimension and material requirements of manufacturing systems

Timm, Thorsten; Blecken, Alexander

doi:10.1080/00207543.2010.495735

Cited by 6 publications

(3 citation statements)

References 19 publications

(21 reference statements)

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“…Of the 177 references addressing production planning for multiple items, 106 references correspond to final products and 71 references consider multi-level product structures. This latter group of papers corresponds not only to MRP models, but also to SCP (27 references), APP (Jayaraman, 2006;Ng, 2007a, 2007b; and HPP (Aghezzaf et al, 2011;Timm and Blecken, 2011). Table 3 summarizes the works reviewed in terms of number of products and number of levels.…”

Section: Number Of Products and Number Of Levelsmentioning

confidence: 99%

“…Most of these works present only overtime (23 references) or subcontracting (10 references) decisions in isolation. Moreover, the combination of overtime and subcontracting production is addressed by 10 references (Erromdhani et al, 2012;Galasso et al, 2009;Jamalnia and Soukhakian, 2009;Leung et al, 2006;Liang, 2007;Merzifonluoglu et al, 2007;Mirzapour Al-e-hashem et al, 2011;Pastor et al, 2009;Timm and Blecken, 2011;Xue et al, 2011). Mula et al (2008), Lusa et al (2009) and Peidro et al (2010) propose the simultaneous modeling of overtime and undertime decisions, while only Fandel and Stammen-Hegene (2006) consider undertime decisions in isolation.…”

Section: 6c Production Timesmentioning

confidence: 99%

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A review of discrete-time optimization models for tactical production planning

Díaz‐Madroñero

Peidro

2014

International Journal of Production Research

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This paper presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, setup times, parallel machines, overtime capacities and network-type multi-site configuration stand out in terms of extensions; (4) the most widely used modeling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modeling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.

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Section: Number Of Products and Number Of Levelsmentioning

confidence: 99%

Section: 6c Production Timesmentioning

confidence: 99%

A review of discrete-time optimization models for tactical production planning

Díaz‐Madroñero

Peidro

2014

International Journal of Production Research

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“…Attainment of accuracy in raw materials mix for production process has been the major problem in many production organizations in developing countries. There were many studies on material planning for production process that dealt with single-and multi-material management [1][2][3][4][5][6][7][8][9][10][11]. Many of these studies were deficient in determining simultaneously the appropriate quantities of the materials needed for the manufacturing of a particular product.…”

Section: Introductionmentioning

confidence: 99%

Development of a Raw Material Requirement Balancing Model for a Production Process

Kareem

2013

AMR

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Attainment of accuracy in raw materials mix for production processes has been the major problem in many production organizations in developing countries. Effects of unequal proportions of material required for a production process are not fully considered in the past studies. This study modeled the raw material requirements in a production process using proportionality based mixed linear programming approach. The objective was to find optimal mix of raw materials for the production of a unit tonnage of a product. The performance of the model was tested by comparing it with alternatively formulated model based on conventional material mix. This model was tested using a cement production system from which blasted limestone, crushed limestone; raw meal, gypsum, red alluvium, clinker and coal were used as raw materials. The conventional method of mixing materials led to 50 % surplus and shortage of materials in the process as compared with the new scheme. The model would be a good tool for accurate prediction of quantity of the raw material required in the production process.Nomenclature, the proportion of material used per ton of the processed product, the quantity of material in processed product (ton), the material proportional per ton of the product, andthe total quantity of materials needed per ton of the product,..., the counter for material type,..., the counter for material proportionality variant

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Integrated versus hierarchical approach to aggregate production planning and master production scheduling

2016

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The hierarchical planning concept is commonly used for production planning. Dividing the planning process into subprocesses which are solved separately in the order of the hierarchy decreases the complexity and fits the common organizational structure. However, interaction between planning levels is crucial to avoid infeasibility and inconsistency of plans. Furthermore, optimizing subproblems often leads to suboptimal results for the overall problem. The alternative, a monolithic model integrating all planning levels, has been rejected in the literature because of several reasons. In this study, we show that some of them do not hold for an integrated production planning model combining the planning tasks usually attributed to aggregate production planning and master production scheduling. Therefore, we develop a hierarchical and an integrated model considering both levels, aggregate production planning and master production scheduling. Computational tests show that it is possible to solve the integrated model and that it outperforms the hierarchical approach for all instances. Moreover, an indication is given why and when integration is beneficial.

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A method for the hierarchical planning of the structure, dimension and material requirements of manufacturing systems

Cited by 6 publications

References 19 publications

A review of discrete-time optimization models for tactical production planning

A review of discrete-time optimization models for tactical production planning

Development of a Raw Material Requirement Balancing Model for a Production Process

Integrated versus hierarchical approach to aggregate production planning and master production scheduling

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