A stochastic dynamic programming based model for uncertain production planning of re-manufacturing system

Li, Congbo; Liu, Fei; Cao, Haotian; Wang, Qiulian

doi:10.1080/00207540701837029

Cited by 68 publications

(29 citation statements)

References 19 publications

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“…Their efforts involve the simple enumeration of a few discrete cases to handle uncertainty in demand. Li et al [25] pointed out that the degree of uncertainty and complexity is very high in a re-manufacturing system.…”

Section: Current Methods To Tackle Uncertaintymentioning

confidence: 99%

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Azizi

Ali

Ping

2015

The South African Journal of Industrial Engineering

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Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent. OPSOMMINGProduksie deurset meet die vertoning en gedrag van 'n produksiesisteem. Produksie deurset modellering is ingewikkeld as gevolg van die onsekerhede in die produksielyn. Hierdie studie ondersoek die toepassing van die aanpasbare neuro-wasige afleidingsisteem om die deurset van produksie onderhewe aan vyf noemenswaardige produksie onsekerhede, naamlik skroot, opstel tyd, breek tyd, aanvraag en die vervaardiging leityd. Die effek van hierdie onsekerhede op die vervaardiging van vloerteëls is ondersoek deur 104 weeklikse observasies op die produksie onsekerhede oor 'n tydperk van 104 weke te neem. Die resultate van die model is vergelyk met 'n meervoudige lineêre regressie model. Die resultate toon dat die aanpasbare neuro-wasige afleidingsisteem in staat was om produksie deurset onderhewe aan onsekerheid te voorspel met 'n hoër akkuraatheid as die meervoudige lineêre regressie model. Dit word aangedui met 'n bepaalheidskoëffisient van 98 persent.

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Section: Current Methods To Tackle Uncertaintymentioning

confidence: 99%

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Azizi

Ali

Ping

2015

The South African Journal of Industrial Engineering

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“…Most recently, researchers looked at stochastic models. For example, APP models with stochastic (Li et al 2009;Zhang, Prajapati, and Peden 2011) and fuzzy parameters (Baykasoglu and Gocken 2010;Chen and Huang 2010).…”

Section: Optimalmentioning

confidence: 99%

Convex optimisation for aggregate production planning

Bushuev

2013

International Journal of Production Research

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This paper presents a new solution approach to the problem of aggregate production planning (APP). As identified by many researchers, the APP cost function is convex and piecewise. Thus, the convex optimisation approach can be applied to the APP problem. Solving the APP problem using convex optimisation is attractive since it leads to an improved solution over the classical solution methods and it can be applied to a wider range of functions. The classical Linear Decision Rule model of APP is solved using convex optimisation and the resulting solution is compared to three solution approaches which have been historically used to solve this model. The results suggest that convex optimisation may be an effective approach for solving certain types of planning models. IntroductionIn general, production is a process of the transformation of inputs (raw materials, information, etc.) into finished products (goods or services). To effectively manage a large number of components of a manufacturing system, a considerable number of decisions should be made on several organisational levels. The taxonomy proposed by Anthony (1965) classifies managerial decisions into three categories: strategic planning, tactical planning and operations control. This taxonomy has been used by many researchers (see e.g. Shim et al. 2002;Kempf, Keskinocak, and Uzsoy 2011).Strategic planning decisions have long-term implications and are affected by both internal and external information. Tactical planning is a middle-level activity which connects strategic planning and operations control. For tactical planning, the basic problem to be resolved is the allocation of resources such as capacity, work force availability, storage and resource distribution over a medium range planning horizon. Operations control decisions deal with short-term operational and scheduling problems which require complete disaggregation of the information generated at higher levels of the taxonomy. At the tactical level, aggregate production planning (APP) models are concerned with determining the production rate and work force level that should be set over a given horizon in order to meet the total demand and minimise the total expected cost (Kempf, Keskinocak, and Uzsoy 2011).All APP techniques face a problem of trade-off between the accuracy of the model in capturing the relevant features of the production-planning environment and the resulting model complexity (Nam and Logendran 1992). The accuracy means that the model precisely describes the problem. The complexity implies that an optimal solution algorithm of solving the model exists and small changes in the model will not require a new algorithm. Some of APP models are not accurate, because they assume linear or quadratic cost function which does not match the data (Groff and Muth 1972; Kempf, Keskinocak, and Uzsoy 2011). Other APP models use algorithms that do not fit the complexity requirement or do not guarantee an optimal solution. The above-mentioned problems are one of the main reasons why APP techniques are not...

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“…Ferguson et al [27] and Zikopoulos [28] also considered uncertainty regarding quality of returns as well. Other papers focused on stochastic demands and/or returns, e.g., Naeem et al [29], Li et al [30], and Xie et al [31]. Benedito and Corominas [32] also contributed to the literature modeling a hybrid system in which the quantity of returns depends on the quantities of products sold by using a Markov decision process.…”

Section: Literature Review On Remanufacturing Lot-sizing Problemsmentioning

confidence: 99%

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Macedo

Alem

Santos³

et al. 2015

Int J Adv Manuf Technol

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This paper presents mixed-integer programming models to hybrid manufacturing and remanufacturing lotsizing problems in which remanufactured products are treated as new ones, so that both manufactured and remanufactured products compete to meet the demands. Differently from previous studies, we consider an environment with multiple products, both manufacturing and remanufacturing costs, disposal, backlogging, and the inherent uncertainties of demands, return rates of usable products, and setup costs. In order to deal with these uncertainties, we propose a scenario-based two-stage stochastic programming model that assumes production and setup as first-stage decision variables, whereas inventory, disposal, and backlogging are defined as second-stage decision variables.We also analyze a risk-averse model in an attempt to reduce the dispersion of the second-stage costs. The main results of the present study indicate that setup costs for remanufacturing can be decisive in choosing between manufacturing or remanufacturing. Even though remanufacturing costs are lower, the process is still largely dependent on return rates and low storage costs for returned products.

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A stochastic dynamic programming based model for uncertain production planning of re-manufacturing system

Cited by 68 publications

References 19 publications

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Convex optimisation for aggregate production planning

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

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