Design, modeling, and analysis of a feedstock logistics system

Judd, Jason; Sarin, Subhash C.; Cundiff, John S.

doi:10.1016/j.biortech.2011.09.111

Cited by 65 publications

(31 citation statements)

References 24 publications

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“…There are two most popular indices for measuring the economic performance of biofuel supply chain: the total cost and the total profit Judd et al, 2012;Sultana and Kumar, 2011;. The difference between these two indices is that the total profit of biofuel supply chain incorporates the revenue by selling the product (biofuels) and byproducts (i.e.…”

Section: Biofuel Supply Chainmentioning

confidence: 99%

“…An et al (2011) formulated a model to maximize the total profit of biofuel supply chain ranging from feedstock suppliers to biofuel customers. Judd et al (2012), Sultana and Kumar (2011), , and developed different models to minimize the total cost of biofuel supply chains. Besides the economic objective, some other models have been developed with the consideration of ecological performance of bioethanol supply chain, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming

Ren

Liang

Sun

et al. 2015

Bioresource Technology

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Section: Biofuel Supply Chainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming

Ren

Liang

Sun

et al. 2015

Bioresource Technology

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“…As a performance criterion, they examined the effect of the policies on the maximum number of trucks needed by the system. Judd et al (2012) extended the previous research in the sense that they constructed a MIP formulation to determine locations for SSLs and loading equipment routes, i.e. the order in which to empty the SSLs.…”

Section: Introductionmentioning

confidence: 96%

Mixed integer programming formulations for the Biomass Truck Scheduling problem

Torjai

Kruzslicz

2015

Cent Eur J Oper Res

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In this paper, we introduce the Biomass Truck Scheduling (BTS) problem that originated in a real-life herbaceous biomass supply chain (HBSC) around Pécs, Hungary. BTS can be considered as a Parallel Machine Scheduling with a Single Server problem, where identical trucks (parallel machines) deliver biomass from satellite storage locations to a central biorefinery operating a single unloader (single server). We make two particular assumptions regarding the server: the server operation has a unit time length for each trip and idle periods are not allowed for it (server no idle time constraint). We consider two objective functions associated with the revealed HBSC logistic cost structure. First, the number of trucks is minimized (resource availability cost) following which the total truck idle time is minimized. Three mixed integer programming formulations are constructed to solve BTS, and their efficiency is evaluated using a number of test cases. We found that, even if the number of trucks is locked at its minimum value, there is always a schedule with zero truck idle time-that is, there is no trade-off between these two objective functions.

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“…Ayoub and Yuji (2012) utilized a demand-driven approach for optimizing biomass utilization networks cost by applying genetic algorithm to solve the network problem. Judd et al (2012) proposed a mathematical programming to determine satellite storage locations and equipment routes to minimize the total cost of designing a feedstock logistics system. The feedstock logistics system includes transporting biomass from production fields to the bioenergy plant.…”

Section: Introductionmentioning

confidence: 99%

Biofuel supply chain considering depreciation cost of installed plants

et al. 2016

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Due to the depletion of the fossil fuels and major concerns about the security of energy in the future to produce fuels, the importance of utilizing the renewable energies is distinguished. Nowadays there has been a growing interest for biofuels. Thus, this paper reveals a general optimization model which enables the selection of preprocessing centers for the biomass, biofuel plants, and warehouses to store the biofuels. The objective of this model is to maximize the total benefits. Costs of the model consist of setup cost of preprocessing centers, plants and warehouses, transportation costs, production costs, emission cost and the depreciation cost. At first, the deprecation cost of the centers is calculated by means of three methods. The model chooses the best depreciation method in each period by switching between them. A numerical example is presented and solved by CPLEX solver in GAMS software and finally, sensitivity analyses are accomplished.

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Design, modeling, and analysis of a feedstock logistics system

Cited by 65 publications

References 24 publications

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming

Mixed integer programming formulations for the Biomass Truck Scheduling problem

Biofuel supply chain considering depreciation cost of installed plants

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