Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration

Duc, Duy Nguyen; Meejaroen, Pasakorn; Nananukul, Narameth

doi:10.1016/j.egyr.2021.10.071

Cited by 17 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on fuzzy theory, fuzzy mathematical programming allows imprecise data to be modeled with tolerance values, which is adequate and effective for dealing with fuzzy energy demand and resources, and fuzzy goals. [33][34][35] For example, Duc et al 36 developed a stochastic and fuzzy optimization model for strategic and tactical management decisions of biomass plants in Thailand, exploring the impacts of uncertain demand on optimal strategies. Chen et al 37 determined a co-firing power plant-siting strategy for Heilongjiang Province, China, using a robust fuzzy optimization model taking into account supply price and quality uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Strategic planning and optimal tactical management of regional bioethanol supply‐chain system with high spatial resolution and uncertainties

Wang,

Ji,

Xie

2024

Biofuels Bioprod Bioref

View full text Add to dashboard Cite

Although bioethanol is viewed as a promising alternative to traditional fossil fuels, its commercialization is hindered by high production costs and uncertain policies. This study proposes a novel integrated framework to support decision making in the bioethanol supply‐chain system under complex uncertainties. By considering various geographical, economic, and environmental factors, potentially suitable sites are identified using a geographic information systems‐based (GIS‐based) multi‐criteria decision‐making approach. Then, a two‐stage stochastic, fuzzy, chance‐constrained programming model is developed to determine investment decisions (i.e. site and size of biorefineries) and operational strategies (i.e. production schedule, biomass supply network, etc.) in the face of uncertainties. The proposed research framework is tailored for a case study in Henan Province, China, which suggests the installation of six biorefineries with a total capacity of 679.11 million liters to reduce local carbon emissions by 1.54 million tons. Decision makers with different risk preferences can use this method to find optimal tradeoff strategies between system cost and risk under uncertainties. Sensitivity analysis also reveals that increasing the proportion of bioethanol blending targets is the key exogenous variable to promote the use of local biomass resources.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strategic planning and optimal tactical management of regional bioethanol supply‐chain system with high spatial resolution and uncertainties

Wang,

Ji,

Xie

2024

Biofuels Bioprod Bioref

View full text Add to dashboard Cite

show abstract

“…14 The ε-constraint is the most well-known method because it is rigorous for both convex and nonconvex problems. 15 As an advancement of the traditional ε-constraint method, an augmented ε-constraint method (AUGMECON) was developed. 16,17 Aboytes-Ojeda et al 18 proposed a MOO biofuel supply chain model using AUGMECON to evaluate the trade-off between expected cost and risk management.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Energy efficiency in supply chain operations contributes to environmental sustainability and significantly affects economic growth 3 . Moreover, greenhouse gas emission in the transportation operations in the supply chain is considered one of the main environmental concerns worldwide 4,5 . In this regard, the green supply chain is becoming an important strategy for most industries and enterprises.…”

Section: Introductionmentioning

confidence: 99%

“…3 Moreover, greenhouse gas emission in the transportation operations in the supply chain is considered one of the main environmental concerns worldwide. 4,5 In this regard, the green supply chain is becoming an important strategy for most industries and enterprises. The green supply chain promotes low energy consumption as well as low environmental and air pollution.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient and sustainable supply chain in the manufacturing industry

Beheshtinia

Fathi

2022

Energy Science & Engineering

View full text Add to dashboard Cite

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

show abstract

Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration

Cited by 17 publications

References 19 publications

Strategic planning and optimal tactical management of regional bioethanol supply‐chain system with high spatial resolution and uncertainties

Strategic planning and optimal tactical management of regional bioethanol supply‐chain system with high spatial resolution and uncertainties

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

Energy‐efficient and sustainable supply chain in the manufacturing industry

Contact Info

Product

Resources

About