Location allocation of solid biomass power plants: Case study of Vojvodina

Bojic, Sanja; Đatkov, Đorđe; Brcanov, Dejan; Georgijević, Milosav; Martinov, Milan

doi:10.1016/j.rser.2013.06.039

Cited by 44 publications

(22 citation statements)

References 7 publications

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“…1. GIS provides visualization of each sectors with corresponding resource potential which allows the selection of both potential plant locations and primary storage locations and calculation of transportation distance between them [8,13,22].…”

Section: Mapping Of Biomass Potentialmentioning

confidence: 99%

“…Based on previous researches presented in [27][28][29][30] and references [for example: [3,6,13,22]], technologies for the biomass valorisation have been classified into four groups: biofuel (liquid, solid and gaseous) production plants, thermal power plants, heating plants and cogeneration plants.…”

Section: Types Of Plants and Their Possible Locationsmentioning

confidence: 99%

“…The model also includes determination of optimal number, locations and sizes of plants and amounts of biomass, intermediate products and final products to be transported between selected locations over selected period. In order to improve the utilization of biomass potential Bojic et al [13] are carried out the research which was aimed at defining optimal location of biomass fuelled thermal power plants. The research has been based on minimization of electricity generation cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization and GIS-based combined approach for the determination of the most cost-effective investments in biomass sector

Vukašinović

Gordić

2016

Applied Energy

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Section: Mapping Of Biomass Potentialmentioning

confidence: 99%

Section: Types Of Plants and Their Possible Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization and GIS-based combined approach for the determination of the most cost-effective investments in biomass sector

Vukašinović

Gordić

2016

Applied Energy

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“…Bojic et al (2013) suggest an allocation model to solve biomass power plant locations. Their model was created from nodes and arcs (network structure).…”

Section: Agricultural Servicesmentioning

confidence: 99%

A Solution Approach for Agricultural Service Center Location Problem Using TOPSIS, DEA and SAW Techniques

et al. 2015

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In this research, agricultural service centers have been introduced as a strategic decision to improve efficiency of agricultural supply chain. Since the location problems in agriculture exhibit several features, such as their large scope and size, and demonstrate increased levels of complexity, so the main aim of this study is developing an integrated Multi-Attribute Decision Making (MADM) approach considering all aspects of the Agricultural Service Center Location Problem (ASCLP) including: customers, service suppliers, and also technical suitability of candidate location. In this regard two main MADM approaches are developed. The first approach was the combination of Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Data Envelopment Analysis (DEA). The second approach is developed to improve the solutions for some cases which the first approach was unable to reach to the non-dominated solutions. Before completing the MADM approaches, initially a Delphi fuzzy-analytical hierarchy process survey has been completed to extract the objectives and attributes of ASCLP and their local weight, which is published in previous work by the authors. The main contribution of current chapter is to consider more comprehensively the attributes in solving a service facility location problem such as: distances to demand points and also suppliers, cultivated area of demand points, population of demand points, number of cultivated crops, and the ration of irrigated cultivated lands to dried cultivated lands. The developed approach was able to use all mentioned decision methods, simultaneously. In the second approach, the developed attributes are used, two scores (maxi-min and maxi-max) for each candidate location is computed and nondominated solutions are identified. To prove the capability of the selected attributes one case study is addressed. The results reveal that the chosen attributes to a very large degree can lead to the non-dominated solutions.

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“…For example, Brazil was one of the leading countries that grew sugarcane to produce ethanol fuel in 2012 and 2013 (Bargos et al 2016). In addition, a case study investigating growing energy plants was performed by Bojić et al (2013), and the optimal siting and size of bioenergy facilities using geographically dependent renewable resources was studied by Sultana and Kumar (2012).…”

Section: Introductionmentioning

confidence: 99%

An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

Chomchalao

Kaewman

Pitakaso

et al. 2018

Administrative Sciences

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This paper presents an algorithm to solve the multilevel location-allocation problem when sabotage risk is considered (MLLAP-SB). Sabotage risk is the risk that a deliberate act of sabotage will happen in a living area or during the transportation of a vehicle. This can change the way decisions are made about the transportation problem when it is considered. The mathematical model of the MLLAP-SB is first presented and solved to optimality by using Lingo v. 11 optimization software, but it can solve only small numbers of test instances. Second, two heuristics are presented to solve large numbers of test instances that Lingo cannot solve to optimality within a reasonable time. The original differential evolution (DE) algorithm and the extended version of DE-the modified differential evolution (MDE) algorithm-are presented to solve the MLLAP-SB. From the computational result, when solving small numbers of test instances in which Lingo is able to find the optimality, DE and MDE are able to find a 100% optimal solution while requiring much lower computational time. Lingo uses an average 96,156.67 s to solve the problem, while DE and MDE use only 104 and 90 s, respectively. Solving large numbers of test instances where Lingo cannot solve the problem, MDE outperformed DE, as it found a 100% better solution than DE. MDE has an average 0.404% lower cost than DE when using a computational time of 90 min. The difference in cost between MDE and DE changes from 0.08% when using 10 min to 0.54% when using 100 min computational time. The computational result also explicitly shows that when sabotage risk is integrated into the method of solving the problem, it can reduce the average total cost from 32,772,361 baht to 30,652,360 baht, corresponding to a 9.61% reduction.

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Location allocation of solid biomass power plants: Case study of Vojvodina

Cited by 44 publications

References 7 publications

Optimization and GIS-based combined approach for the determination of the most cost-effective investments in biomass sector

Optimization and GIS-based combined approach for the determination of the most cost-effective investments in biomass sector

A Solution Approach for Agricultural Service Center Location Problem Using TOPSIS, DEA and SAW Techniques

An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

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