Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study

Babor, Majharulislam; Pedersen, Line Bjørnskov; Kidmose, Ulla; Emire, Shimelis Admassu; Hitzmann, Bernd

doi:10.3390/pr10081623

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Therefore, wherever possible, it is suggested to keep the processing route for a product separate from other products. Six bakery production datasets from Denmark were used by Babor et al 55 to increase the production efficiency. The results revealed that NSGA-II performed efficiently to reduce makespan by up to 12% and oven idle time by up to 61%.…”

Section: Resultsmentioning

confidence: 99%

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Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Babor

Emire

Kohlus

et al. 2023

Sci Rep

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Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO2 emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens’ idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows.

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Section: Resultsmentioning

confidence: 99%

“…Figure15. An evaluation procedure of the NSGA-II55 . Non-dominated sorting divides the population into different ranks (F 0 , F 1 , F 2 , …).…”

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confidence: 99%

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Babor

Emire

Kohlus

et al. 2023

Sci Rep

Self Cite

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“…A few of the most well-known posteriori approaches have been based upon Evolutionary Algorithms (EA) [41], which includes NSGA-II [42] and Non-dominated Sorting Genetic Algorithm (NSGA) [43], [44], [45], MO Particle Swarm Optimization (MOPSO) [28], [46], and Pareto-frontier Differential Evolution (PDE) [47]. While the recently developed nature-inspired EAs have been utilized in real-life MOPs as well, the algorithms that have been mentioned above were first introduced more than ten years ago.…”

Section: Fig 3 Illustrates a Multimodal Moo Problemmentioning

confidence: 99%

Analytical and Comparative Study for Optimization Problems

2023

IJCCCE

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Finding an optimal solution to some problem, like minimizing and maximizing the objective function, is the goal of Single-Objective Optimization (SOP). Real-world problems, on the other hand, are more complicated and involve a wider range of objectives, several objectives should be maximized in such problems. No single solution could be enhanced in all objectives without deteriorating at least one other goal, which is the definition of Pareto-optimality. Understanding the idea of Multi-Objective Optimization (MOP) is thus necessary to find the optimum solution. Multi-objective evolutionary algorithm (MOEA) are made to simultaneously assess many objectives and find Pareto-optimal solutions, MOEA can resolve multi-objective and single-objective optimization problems. This paper aims to introduce a survey study for optimization problem solutions by comparing techniques, advantages, and disadvantages of SOP and MOP with metaheuristics and evolutionary algorithms. From this study, we conduct that the efficiency of MOP lies in the present more than one SOP, but it takes a longer time to process and train and is not suitable for all applications, While SOP is faster and more useful in stock and profit maximization applications. And the posterior techniques are considered the dominant approach to solving multi-objective problems by the use of the field of metaheuristics. Index Terms— Multi-Objective Evolutionary Algorithm, Multi-Objective Optimization, Optimization problem, Objective Function, Single-Objective Optimization.

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“…In the process of solving, parameters such as the population size and the number of iterations of the algorithm need to be determined. Reasonable parameters will improve the accuracy and speed of the algorithm (44). In NSGA-II, the quality of the solution is affected by the algorithm parameters and reasonable value of parameters.…”

Section: Selection Of Parameters Of Algorithmmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

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Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study

Cited by 8 publications

References 38 publications

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Analytical and Comparative Study for Optimization Problems

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

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