Optimal job scheduling to minimize total tardiness by dispatching rules and community evaluation chromosomes

Bari, Prasad; Karande, Prasad

doi:10.31181/dmame622023700

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…If the null hypothesis is not accepted, ANOVA simply expresses that there is a substantial dissimilarity among the methods employed without providing any further details. Post hoc tests are thus carried out to identify the discrepancies between techniques [48]. In Jeffreys's Amazing Statistics Programme (JASP), an open source statistical programme, the ANOVA test is run on the R 2 , RMSE, and MAE of ML techniques applied to labour, seed, fertilizers and manure, insecticides, and irrigation costs, which are the components of the operational costs of crop cultivation.…”

Section: Anova Statistical Test Validation Of ML Methodsmentioning

confidence: 99%

Machine Learning-based Extrapolation of Crop Cultivation Cost

Bari,

Ragha

2024

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It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.

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Section: Anova Statistical Test Validation Of ML Methodsmentioning

confidence: 99%

Machine Learning-based Extrapolation of Crop Cultivation Cost

Bari,

Ragha

2024

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“…One approach to creating schedules in a parallel machine environment is through the utilization of priority sequencing rules, allowing schedules to evolve. When a workstation is ready for further processing, the selection of the next job to be processed is made based on these priority sequencing rules [6,8] .…”

Section: Parallel Machine Schedulingmentioning

confidence: 99%

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

Badi,

Bouraima,

Qiu

et al. 2024

Decis. Mak. Adv.

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Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

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A green project scheduling with material ordering problem considering cost constraints and quality of materials

Kebriyaii,

Heidari,

Khalilzadeh

et al. 2024

ECAM

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PurposeIntegrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.Design/methodology/approachIn this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.FindingsBased on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.Originality/valueThe primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

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Optimal job scheduling to minimize total tardiness by dispatching rules and community evaluation chromosomes

Cited by 3 publications

References 32 publications

Machine Learning-based Extrapolation of Crop Cultivation Cost

Machine Learning-based Extrapolation of Crop Cultivation Cost

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

A green project scheduling with material ordering problem considering cost constraints and quality of materials

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