Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design

Khishtandar, Soheila

doi:10.1016/j.apenergy.2018.11.092

Cited by 57 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy method is based on strong mathematical concepts and due to the lack of need for accurate and sufficient information, this approach provides a more efficient model than other approaches including the probability approach that requires sufficient knowledge of the distribution of nonlinear parameters (Falcone & De Rosa, 2020 ; Khishtandar, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

A multi-objective location-routing model for dental waste considering environmental factors

et al. 2022

View full text Add to dashboard Cite

Nowadays, the amounts of infectious medical waste (IMW) have surged considerably so waste management has become a critical emergency in many developing countries. However, most large medical waste generation centers (MWGC) are equipped with treatment facilities, small MWGC faces the waste management problem. It reveals the significance of having a proper program for small health centers. This is an indisputable difficulty that governments bordered because it imposes great costs on societies, also the environmental problems caused by improper treatment are irreparable. To attend to all the essential aspects of the problem, this paper recommended a location-routing model with four objective functions to minimize the total costs, environmental pollution, the risk imposed on the population around disposal sites, and the total violation from the expected arrival time. Considering a multi-period problem with a maximum acceptable delay plays a key role to connect the assumptions to the real-world problem. In addition, for solving mathematical models based on case studies, the role of uncertainty is undeniable. The demand for dental waste treatment is not definite and is changed based on the different conditions thus fuzzy chance-constrained programming is proposed for this problem to tackle the uncertainty. The revised multi-choice goal programming method is considered to solve the model and a real case study for dental clinics in Babol city of Iran is investigated to illustrate the validation of the proposed model. The results indicate that the solution method can create a balance between four objective functions. Finally, sensitivity analyses are performed for some parameters to analyze the behavior of the objective functions.

show abstract

Section: Methodsmentioning

confidence: 99%

A multi-objective location-routing model for dental waste considering environmental factors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…After this transformation, (8) becomes a conceptual formula, which can represent fuzzy market demand but cannot be solved. Therefore, this chapter introduces the concept of fuzzy chance constrained programming; that is, the decision can be made before the constraints with random variables are realized, and the decision results are allowed to fail to meet the constraints to a certain extent, but the probability of the establishment of the constraints should not be less than the confidence level [39]. The specific form and performance are as follows:…”

Section: ) the Clarity Of Uncertain Market Demandmentioning

confidence: 99%

Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

Tang

Wang

2022

IEEE Access

View full text Add to dashboard Cite

With the gradual diversification of customer demand, to improve the rapid response ability of enterprises, this paper fully considers uncertain market information under the background of mass customization and establishes a new process industry multi-product switching production lot sizing and scheduling model with the goal of minimizing the maximum completion time and total switching cost. Fuzzy chance-constrained programming is used to explicitly incorporate market demand with uncertain quantities into the model. Starting from reality, this paper considers the switching cost of equipment when processing multiple varieties of products and skillfully integrates the conversion rate of materials during processing into the novel model, making the entire production system closer to the real state. It provides a new concept to consider cost reduction for actual workshop scheduling management. In addition, this paper proposes an improved multi-objective genetic particle swarm optimization (SMOPSO-IIs) algorithm to simulate and solve the model. The operation results show that the Pareto solution obtained by the SMOPSO-IIs algorithm is better overall. Finally, the model is solved by example simulation, and the operation results are analyzed along with a scheduling Gantt chart to verify its applicability and effectiveness. The model presented in this paper can be used to further shorten the gap between production theory and practical application and improve the current workshop scheduling management system of the process industry.INDEX TERMS Mass customization, process industry lot sizing and scheduling; uncertain market information; product switching

show abstract

“…Several reviewed studies utilized FL‐based methods to address BSC uncertainties related to biomass availability (Arabi et al, 2019; Khishtandar, 2019; Tong et al, 2014), biofuel demand (Tong et al, 2014), land use (Balaman & Selim, 2014, 2015, 2016), and biomass prices (Balaman et al, 2018; Khishtandar, 2019). In addition, two studies used ABM to simulate the individual behavior across BSC and then implemented the simulation‐based optimization (Kim et al, 2018; Shu et al, 2017).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

View full text Add to dashboard Cite

Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

show abstract

Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design

Cited by 57 publications

References 27 publications

A multi-objective location-routing model for dental waste considering environmental factors

A multi-objective location-routing model for dental waste considering environmental factors

Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Contact Info

Product

Resources

About