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Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End
Published 2009“…The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. …”
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Final Year Project -
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Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End
Published 2012“…The solution obtained from the LB–MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB–NLP. …”
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Final Year Project -
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Optimal timber transportation planning in tropical hill forest using bees algorithm
Published 2022“…Combining these two techniques allows TTP to be linked to timber harvesting area models. The planning depends on the legal restrictions, fixed and variable costs, landing locations, as well as the existing and proposed road network. …”
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Thesis -
4
Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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Process Planning Optimization In Reconfigurable Manufacturing Systems
Published 2008“…(i) what decision making models and (ii) what computational techniques, provide an optimal manufacturing process planning solution in a multidimensional decision variables space? …”
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6
A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system
Published 2011“…This research aims to apply this emerging optimisation algorithm to develop a mixed-integer programming model for designing cellular manufacturing systems (CMSs), and production planning in dynamic environments. …”
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Article -
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Automated time series forecasting
Published 2011“…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Monograph -
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Harmony Search algorithm-based gasoline consumption modeling for Indonesia
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Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH
Published 2021“…The load flow patterns will significantly have affected when uncertain PV generation – load models are considered into the power flow algorithm. …”
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11
Capacity Planning For Mixed-Load Tester Under Demand And Testing Time Uncertainty
Published 2018“…Currently,the company’s issue is low tester utilization of about 71%,well below the target of 96%.The objective of this research is to improve tester utilization while achieving the production target under uncertain demand and testing time and also to determine the break-even point on the testers required.A novel approach of integrating a mathematical model,robust optimization model,genetic algorithm,simulation model and cost–volume –profit analysis was developed.Firstly,a mathematical model of mixed-load tester was formulated.Next,a set of discrete scenarios was proposed to address uncertain demand and testing time.A robust optimization and genetic algorithm model was developed to optimize the number of testers under the described uncertainties.Next,these scenarios were simulated using the Pro Model simulation software to validate the proposed models and to evaluate throughput and tester utilization.Finally,the cost–volume–profit analysis was performed for scenarios that require additional testers at various levels of uncertainties.The results showed that the proposed solution improved tester utilization by 25% compared to the current system.This research has contribution by developing novel hybrid methodology and able to provide useful insights to assist company’s managers to plan and allocate resources according to variations in customers’ demands and testing time.…”
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Thesis -
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A hybrid prediction model for short-term load forecasting in power systems
Published 2024“…Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. …”
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Development of two matheuristics for production-inventory-distribution routing problem / Dicky Lim Teik Kyee
Published 2018“…The aim of solving the model is to construct a production plan and delivery schedule which minimizes the overall costs while fulfilling customers’ demand over the planning horizon. …”
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Optimizing timber transportation planning for timber harvesting using bees algorithm in Malaysia
Published 2023“…A Bees Algorithm (BA) was proposed to find an optimum TTP for timber extraction, forest road, and landing locations with grid cell-sized 10 m × 10 m and attributed with fixed and variable costs. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models
Published 2019“…The GA-ANN and GA-NARX models perform markedly better than the other models in the most training algorithms with different numbers of hidden layers.…”
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Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…The proposed strategy is dependent on modified Zimmermanns approach for handling all inexact operating costs, data capacities, and demand variables. The SD algorithm is employed to balance exploitation and exploration in MSA, thereby resulting in efficient and effective (speed and quality) solution for the APP model. …”
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A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST
Published 2014“…A long term distribution network planning consists of several complexity aspects due to the multiple decision variables in objective functions. …”
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Long-term optimal planning for renewable based distributed generators and plug-in electric vehicles parking lots toward higher penetration of green energy technology
Published 2025“…This paper introduces a long-term mixed-integer non-linear (MINL) optimization planning model designed to optimize the planning and operation of RESs, including wind and PV sources, alongside PEV-PLs infrastructure. …”
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