Impact of carbon pricing policies on the cost and emission of the biomass supply chain: Optimization models and a case study

2020 ◽  
Vol 267 ◽  
pp. 115069 ◽  
Author(s):  
Krishna Teja Malladi ◽  
Taraneh Sowlati
Keyword(s):  
Supply Chain ◽  
Optimization Models ◽  
Supply Chain Optimization ◽  
Carbon Pricing ◽  
Pricing Policies ◽  
Biomass Supply ◽  
The Cost

A Biomass Supply Chain Optimization Framework With Linear Approximation of Biomass Yield Distributions for Improved Land Use

2019 ◽  
Author(s):  
Nathanial Cooper ◽  
Anna Panteli ◽  
Nilay Shah
Keyword(s):  
Supply Chain ◽  
Biomass Yield ◽  
Supply Chain Optimization ◽  
Optimization Framework ◽  
Biomass Supply ◽  
Yield Information ◽  
Supply Chain Optimisation ◽  
The Impact

Abstract Biomass and the bio-economy have strong potential to help shift dependency away from petroleum. Supply chain optimisation (SCO) has been used to help other industries and can be used to boost biomass industry viability. Biomass supply chain models frequently average the biomass yield of large tracts of land in their calculations. However, there can be large variation in the biomass yield within those tracts, losing useful information. This work presents a biomass SCO framework which approximates the available quality of land by piecewise linearly approximation of the biomass yield distribution, and incorporates this information into the optimisation. The linear estimates of the biomass yield distributions allow the SCO model to make more informed decisions about quantity and location of biomass growth operations, affecting all downstream decisions. A case study of mainland Great Britain has been examined using the framework to illustrate the impact of retaining biomass yield information in the optimisation, versus averaging the yield across tracts of land. The case study found that using biomass yield linear estimates reduced the overall land usage by 10%. Further, it improved biomass output, which increased the quantity of bio-products produced. All of this led to an increase in the overall profit.

scholarly journals Mathematical Analysis of Supply Chain Optimization focusing on Artificial Neural Network: A case study in Apparel Manufacturing Organization

2022 ◽  
Author(s):  
Shibbir Ahmad ◽  
Mohammad Kamruzzaman
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Selection Criteria ◽  
Manufacturing Industry ◽  
Supply Chain Optimization ◽  
Apparel Manufacturing ◽  
Manufacturing Organizations ◽  
Manufacturing Organization ◽  
The Right

Abstract In this study, implemented artificial nueral network (Ann) in apparel manufacturing organizations to optimize the supply chain converging on right supplier selection by analyzing their performance criteria.Moreover, data collected from three diffrents factory to analyze the efficiney and profit -loss status of that units. Furthermore, analyze the supplier selection criteria of three suppliers in order to select the right supplier at the real time in apparel manufacturing industry . This study shows that it can be saved 20 % of the total cost.

The Board-Executive Imperative

2015 ◽  
pp. 1-32
Author(s):  
Usman A. Ghani
Keyword(s):  
Supply Chain ◽  
Senior Executives ◽  
Supply Chain Optimization ◽  
Supply Chain Strategy ◽  
Firm Scope ◽  
Corporate Context ◽  
Primary Responsibility ◽  
The Cost ◽  
Over Time

This chapter provides a fresh outlook for supply chain optimization by advocating the involvement of boards and top-teams that are uniquely positioned to address a confluence of three strategic responsibilities of a firm: scope and significance; people and culture; and measures and metrics. It provides a holistic corporate context and grapples with tougher issues often deferred or stalled as other initiatives or crises grab corporate attention. This chapter introduces his frameworks and guidelines and selective examples of success and failure in implementation. This chapter assigns primary responsibility for supply chain strategy senior executives. It observes these areas as gradually becoming too operationalized, even commoditized, with local efficiencies emphasized at the cost of gradual overall ineffectiveness. It also dispels six myths that have taken root over time, highlighting their impact and substituting these with today's realities. To make this work more practical, this chapter shares first-hand examples of supply chain practices.

scholarly journals Optimizing Resource Utilization in Biomass Supply Chains by Creating Integrated Biomass Logistics Centers

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6153
Author(s):  
Xuezhen Guo ◽  
Juliën Voogt ◽  
Bert Annevelink ◽  
Joost Snels ◽  
Argyris Kanellopoulos
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Resource Utilization ◽  
Optimization Model ◽  
Biobased Products ◽  
Knowledge Gap ◽  
Supply Chain Optimization ◽  
Biomass Logistics ◽  
Idle Capacity

Bio-based supply chains are by nature complex to optimize. The new logistic concept of integrated biomass logistical center (IBLC) provides us the opportunity to make full use of the idle capacity for a food/feed plant to produce biobased products so that the entire chain efficiency can be improved. Although research has been conducted to analyze the IBLC concept, is yet to be an optimization model that can optimally arrange the activities in the supply chain where an IBLC stands in the middle. To fill the knowledge gap in the literature, this paper makes the first step to develop a MILP model that enables biobased supply chain optimization with the IBLC concept, which supports logistic and processing decisions in the chain. The model is applied in a case study for a feed and fodder plant in Spain where managerial insights have been derived for transferring the plant to a profitable IBLC.

scholarly journals Optimal Design of a Forest-Based Biomass Supply Chain Based on the Decision maker’s Viewpoint Towards Risk

2019 ◽  
Vol 66 (4) ◽  
pp. 509-519 ◽  
Author(s):  
Shaghaygh Akhtari ◽  
Taraneh Sowlati ◽  
Verena C Griess
Keyword(s):  
Supply Chain ◽  
Decision Maker ◽  
Decision Rules ◽  
Energy Prices ◽  
Biomass Supply ◽  
Management Literature ◽  
Future Events ◽  
Uncertain Future ◽  
Biomass Availability

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.

scholarly journals Cost of Oil and Biomass Supply Shocks under Different Biofuel Supply Chain Configurations

2018 ◽  
Vol 2672 (24) ◽  
pp. 31-40 ◽  
Author(s):  
Rocío Uría-Martínez ◽  
Paul N. Leiby ◽  
Maxwell L. Brown
Keyword(s):  
Supply Chain ◽  
Cellulosic Biomass ◽  
Renewable Fuel Standard ◽  
Fuel Blends ◽  
Supply Shocks ◽  
Biomass Supply ◽  
Biomass Logistics ◽  
Biofuel Supply Chain ◽  
Light Duty Vehicle ◽  
The Cost

This analysis estimates the cost of selected oil and biomass supply shocks for producers and consumers in the light-duty vehicle fuel market under various supply chain configurations using a mathematical programing model, BioTrans. The supply chain configurations differ by whether they include selected flexibility levers: multi-feedstock biorefineries; advanced biomass logistics; and the ability to adjust ethanol content of low-ethanol fuel blends, from E10 to E15 or E05. The simulated scenarios explore market responses to supply shocks including substitution between gasoline and ethanol, substitution between different sources of ethanol supply, biorefinery capacity additions or idling, and price adjustments. Welfare effects for the various market participants represented in BioTrans are summarized into a net shock cost measure. As oil accounts for a larger fraction of fuel by volume, its supply shocks are costlier than biomass supply shocks. Corn availability and the high cost of adding biorefinery capacity limit increases in ethanol use during gasoline price spikes. During shocks that imply sudden decreases in the price of gasoline, the renewable fuel standard (RFS) biofuel blending mandate limits the extent to which flexibility can be exercised to reduce ethanol use. The selected flexibility levers are most useful in response to cellulosic biomass supply shocks.

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