Optimal management of Supply Chain: Modeling of Decision Rules

Author(s):  
Dorota Bochnacka
2013 ◽  
Vol 411-414 ◽  
pp. 2085-2088
Author(s):  
Xiao Qing Geng ◽  
Yu Wang

In this paper, the rough set theory is applied to reduce the complexity of data space and to induct decision rules. It proposes the generic label correcting (GLC) algorithm incorporated with the decision rules to solve supply chain modeling problems. This proposed approach is agile because by combining various operators and comparators, different types of paths in the reduced networks can be solved with one algorithm.


2008 ◽  
Vol 27 (1) ◽  
pp. 3-13
Author(s):  
Charu Chandra ◽  
Jānis Grabis

Multiple interrelated decision-making models are frequently used in supply chain modeling. Model integration is a precondition for efficient development and utilization of these models. This paper discusses use of modern information technology (IT) techniques and methods for integration of supply chain decision-making models. The overall approach to using IT at various stages of model development is presented. Data and process modeling techniques are used to developed semi-formalized representation of integrated models. These models support integration of decision-making components with other parts of supply chain information system. Process modeling is also used to describe interrelationships among multiple decision-making models. This representation is used as the basis for implementation of integrated models. The service-oriented architecture is proposed as an implementation platform. The presented discussion serves as the basis for further developments in developing integrated supply chain decision-making models.


2017 ◽  
Vol 37 (10) ◽  
pp. 1520-1540 ◽  
Author(s):  
S.C. Lenny Koh ◽  
Angappa Gunasekaran ◽  
Jonathan Morris ◽  
Raymond Obayi ◽  
Seyed Mohammad Ebrahimi

Purpose In response to calls for conceptual frameworks and generic theory building toward the advancement of sustainability in supply chain resource utilization and management, the purpose of this paper is to advance a circular framework for supply chain resource sustainability (SCRS), and a decision-support methodology for assessing SCRS against the backdrop of five foundational premises (FPs) deduced from the literature on resource sustainability. Design/methodology/approach Taking a conceptual theory-building approach, the paper advances a set of SCRS decision-support criteria for each of the theoretical premises advanced, and applies the theory of constraints to illustrate the conceptual and practical applications of the framework in SCRS decision making. Findings This study uses recent conceptualizations of supply chains as “complex adaptive systems” to provide a robust and novel frame and a set of decision rules with which to assess the interconnectedness of environmental, economic, and social capital of supply chain resources from pre-production to post-production. Research limitations/implications The paper contributes to theory building in sustainability research, and the SCRS decision framework developed could be applied in tandem with existing quantitative hybrid life-cycle and input-output approaches to facilitate targeted resource sustainability assessments, with implications for research and practice. Originality/value The novel SCRS framework proposed serves as a template for evaluating SCRS and provides a decision-support methodology for assessing SCRS against the five theorized FPs.


2019 ◽  
Vol 66 (4) ◽  
pp. 509-519 ◽  
Author(s):  
Shaghaygh Akhtari ◽  
Taraneh Sowlati ◽  
Verena C Griess

Abstract Economic viability is one of the main considerations in bioenergy and biofuel projects and is impacted by uncertainty in biomass availability, cost, and quality, and bioenergy and biofuel demand and prices. One important aspect of decisionmaking under uncertainty is the viewpoint of the decision maker towards risk, which is overlooked in the biomass supply chain management literature. In this paper, we address this gap by evaluating alternative supply chain designs taking into account uncertain future conditions resulting from changes in biomass availability and cost, and bioproduct and energy prices. Three decision rules, maximax, minimax regret, and maximin, representing, respectively, optimistic, opportunistic, and pessimistic perspectives, are used for evaluation. It is assumed that the decision maker has knowledge about the potential future events, but the likelihood of their occurrence is unknown. According to the results of the case study, investment in bioenergy and biofuel conversion facilities was recommended based on optimistic and opportunistic viewpoints. Production of both bienergy and biofuels would not be profitable under pessimistic conditions. Therefore, investment in only bienergy facilities was prescribed under pessimistic conditions.


2013 ◽  
Vol 5 (2) ◽  
pp. 55-66 ◽  
Author(s):  
M Outwater ◽  
C Smith ◽  
K Wies ◽  
S Yoder ◽  
B Sana ◽  
...  

Author(s):  
Chun Che Huang ◽  
Wen Yau Liang ◽  
Shian Hua Lin

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