An aggregate production planning model with demand under uncertainty

Filho, Oscar Salviano Silva

doi:10.1080/095372899232579

Cited by 15 publications

(3 citation statements)

References 12 publications

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“…However, little attention has been given to model the aggregate production planning problems under an uncertain environment. 17 Lockett and Muhlemann 18 considered various real-life factors such as delaying production and the probability of production being scrapped or of extra work being required in the planning process. A stochastic programming model was formulated to determine the job assignment to different work centres over planning horizon.…”

Section: Introductionmentioning

confidence: 99%

A stochastic programming approach for multi-site aggregate production planning

Leung

Lai

2006

Journal of the Operational Research Society

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Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan-consisting of the quantity of production and the workforce level at each production plant-to fulfil market demand. This paper addresses the production planning problem with additional constraints, such as production plant preference selection. To deal with the uncertain demand data, a stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model. An analysis of the probability distribution of economic demand assumptions is performed. The impact of unit shortage costs on the total cost is also analysed.

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Section: Introductionmentioning

confidence: 99%

A stochastic programming approach for multi-site aggregate production planning

Leung

Lai

2006

Journal of the Operational Research Society

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“…For example, Rakes et al (1984) In recent years, fuzzy programming is the most frequently applied method to handling uncertainties in APP models. During the past decade, some authors have developed different APP models considering fuzzy demand information (Filho, 1999;Wang and Fang, 2000;Fung et al, 2003;Leung and Wu, 2004;Wang and Liang, 2004). In recent years, some other authors also researched multi-site aggregate planning considering uncertainty based on fuzzy theory (Jamalnia and Soukhakian, 2009;Mirzapour Al-e-hashem et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Aggregate production planning with learning effect and uncertain demand

Chen

Sarker

2015

Journal of Modelling in Management

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Purpose -The purpose of this paper is to study the impact of learning effect and demand uncertainty on aggregate production planning (APP), provide practitioners with some important managerial implications for improving production planning and productivity. Design/methodology/approach -Motivated by the background of one labour-intensive manufacturing firm -a mosquito expellant factory -an APP model considering workforce learning effect and demand uncertainty is established. Numerical example and comparison with other two models without considering learning and uncertainty of demand are conducted. Findings -The result shows that taking into account the uncertain demand and learning effect can reduce total production cost and increase flexibility of APP. Practical implications -Managerial implications are provided for practitioners with four propositions on improving workforce learning effect, i.e. emphasizing employee training, combing individual and organizational learning and reduction of forgetting effect. Originality/value -This paper has practice value in improving APP in labor-intensive manufacturing.

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“…The answer will be to seek models using the non-linear and dynamic programming. Certain authors suggested, in particular cases, other approaches (stochastic optimisation models, Monte Carlo) [7,10]. How can the S&OP be approached in the industrial case of multiple production lines?…”

Section: Future Prospects Of Sandopmentioning

confidence: 99%

Sales and Operations Planning Optimisation

Genin

Lamouri

Thomas

Applied Optimization

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Operations' planning requires making strategic decisions on inventories levels, on demands and operations constraints. The importance of these decisions leads to elaborate and optimise Sales and Operations Plans on a planning time fence at least as long as the budget. Models using linear programming give the "optimal" strategy but it does not resist frequent changes in parameters. Other mathematical tools as well as Taguchi methods are interesting in realising a simple but robust compromise.

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An aggregate production planning model with demand under uncertainty

Cited by 15 publications

References 12 publications

A stochastic programming approach for multi-site aggregate production planning

A stochastic programming approach for multi-site aggregate production planning

Aggregate production planning with learning effect and uncertain demand

Sales and Operations Planning Optimisation

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