Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management

Bioenergy has been recognized as an important alternative source of energy. The production of bioenergy is expected to increase in the years to come, and one of the most important obstacles in increased bioenergy utilization are the logistics problems, which involve complex and large-scale optimization problems. Solving these problems constitutes a daunting task, and often, traditional mathematical approaches fail to converge to the optimal solution within a reasonable time. Thus, more robust methods are required in order to overcome complexity. Metaheuristics are strategies for solving complex and large-scale optimization problems, which provide a near-optimal or practically useful solution. The aim of this chapter is to present a survey of metaheuristics and the available literature regarding the application of metaheuristics in the bioenergy supply chain field as well as the uniqueness and challenges of the mathematical problems applied to bioenergy.

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Blockchain in Supply Chain: Case Study of Lottemart Ciputat

ACMIT Proceedings ◽

10.33555/acmit.v5i1.73 ◽

2019 ◽

Vol 5 (1) ◽

pp. 38-49 ◽

Cited By ~ 1

Author(s):

B. K. Handoyo ◽

M. R. Mashudi ◽

H. P. Ipung

Keyword(s):

Decision Making ◽

Supply Chain ◽

Supply Chain Management ◽

Information Asymmetry ◽

New Technology ◽

Business Case ◽

Analysis Method ◽

Chain Management ◽

Production Processes

Current supply chain methods are having difficulties in resolving problems arising from the lack of trust in supply chains. The root reason lies in two challenges brought to the traditional mechanism: self-interests of supply chain members and information asymmetry in production processes. Blockchain is a promising technology to address these problems. The key objective of this paper is to present qualitative analysis for blockchain in supply chain as the decision-making framework to implement this new technology. The analysis method used Val IT business case framework, validated by the expert judgements. The further study needs to be elaborated by either the existing organization that use blockchain or assessment by the organization that will use blockchain to improve their supply chain management.

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MEASURING PERFORMANCE OF ENVIRONMENTALLY RESPOSNSIVE SUPPLY CHAIN MANAGEMENT : A CASE STUDY OF INDIAN INDUSTRIAL SECTOR

10.26488/iej.10.8.56 ◽

2018 ◽

Vol 10 (8) ◽

Author(s):

Narendra Kumar Sahu ◽

A.C. Shukla

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Industrial Sector ◽

Chain Management

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Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

Recent Patents on Computer Science ◽

10.2174/2213275911666181012121059 ◽

2019 ◽

Vol 12 (3) ◽

pp. 171-179 ◽

Cited By ~ 6

Author(s):

Sachin Gupta ◽

Anurag Saxena

Keyword(s):

Machine Learning ◽

Data Mining ◽

Supply Chain ◽

Supply Chain Management ◽

Product Life Cycle ◽

Consumer Preference ◽

Bullwhip Effect ◽

Machine Learning Techniques ◽

Chain Management ◽

Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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