A belief-rule-based inference method for aggregate production planning under uncertainty

Li, B.; Wang, H. W.; Yang, Jian‐Bo; Guo, Min

doi:10.1080/00207543.2011.652262

Cited by 32 publications

(13 citation statements)

References 76 publications

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“…After sales forecasts and resource requirements, the various alternate production plans are generated Liu et al, 2011;Mirapour et al, 2011;Li et al, 2013). Additionally, SCA provides useful insights to problems related to operations scheduling problems, which can be formulated as mixed integer linear programming problems (Wang and Lei, 2012;.…”

Section: Productionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,111

750

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The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) -that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

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Section: Productionmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,111

750

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show abstract

“…Even if the forecast is provided in interval forms, intended planning values are inferable from the belief rules with regard to different preferences. The belief-rule framework is concise, and makes it possible for decision maker to easily improve it by directly adjusting the rule weights and thus belief degrees (Li et al, 2013). Contrary to regular multi attribute decision analysis (MADA) methods that present an MADA problem under decision matrix structure, ER utilises a belief decision matrix where a distribution which depends on a belief structure is used for assessing the alternatives with respect to different attributes.…”

Section: Evidential Reasoningmentioning

confidence: 99%

“…Paint company Love and Turner (1993); ; Li et al (2013); Hsieh and Wu (2000); Turksen and Zhong (1988); Dejonckheere et al (2003) Paint products 6…”

Section: Literature With Respect To the Applied App Strategymentioning

confidence: 99%

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Jamalnia

Yang

Feili

et al. 2019

Int J Adv Manuf Technol

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This is the first literature survey of its kind on aggregate production planning (APP) under uncertainty. Different types of uncertainty, such as stochasticity, fuzziness and possibilistic forms, have been incorporated into many management science techniques to study APP decision problem under uncertainty. In current research, a wide range of the literature which employ management science methodologies to deal with APP in presence of uncertainty is surveyed by classifying them into five main categories: stochastic mathematical programming, fuzzy mathematical programming, simulation, metaheuristics and evidential reasoning. First, the preliminary analysis of the literature is presented by classifying the literature according to the abovementioned methodologies, discussing about advantages and disadvantages of these methodologies when applied to APP under uncertainty, and concisely reviewing the more recent literature. Then, APP literature under uncertainty is analysed from management science and operations management perspectives. Possible future research paths are also discussed on the basis of identified research trends and research gaps.

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“…Günther (1982) used simulation to compare the performance of linear programming models and linear decision rules in situations of demand uncertainty, indicating that the more uncertain the demand, the latter produces better results than the former; however, he does not develop a specific model to deal with the issue of uncertainty. Li, Wang, Yang, Guom, Qi (2013) present a technique (belief-rule-based inference) for conditions of demand uncertainty, in which plans are evaluated in scenarios with different probabilities.…”

Section: Methodsmentioning

confidence: 99%

Aggregate planning for probabilistic demand with internal and external storage

Biazzi

2018

JOSCM

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This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits. KEYWORDS | Inventory, non-stationary probabilistic demand, aggregate production planning, sales and operations mathematical models, no ordering costs.

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A belief-rule-based inference method for aggregate production planning under uncertainty

Cited by 32 publications

References 76 publications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

Aggregate planning for probabilistic demand with internal and external storage

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