Search Results - (( program planning model algorithm ) OR ( java implication based algorithm ))
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Development of dynamic programming algorithm for maintenance scheduling problem
Published 2020“…Using the dynamic programming algorithm developed, the model was also able to recalculate alternative schedules by replacing unavailable teams with other teams to avoid delays. …”
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Long-term optimal planning of distributed generations and battery energy storage systems towards high integration of green energy considering uncertainty and demand response progra...
Published 2025“…Motivated by these goals, this paper introduces a long-term Mixed-Integer Nonlinear Programming (MINLP) multi-objective stochastic optimization planning model to increase the penetration of green energy in the distribution system (DS). …”
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Urban connected vehicle lane planning based on improved Frank Wolfe algorithm
Published 2025“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. …”
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Urban connected vehicle lane planning based on improved Frank Wolfe algorithm
Published 2025“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. …”
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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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A Constraint Programming-based Genetic Algorithm (CPGA) for Capacity Output Optimization
Published 2014“…A genetic algorithm model was created in the second stage to optimize capacity output. …”
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Development of two matheuristics for production-inventory-distribution routing problem / Dicky Lim Teik Kyee
Published 2018“…We propose an optimization algorithm designed by the interpolation of metaheuristics and mathematical programming techniques, known as MatHeuristics algorithm, to solve the model. …”
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Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…For these proposed approaches, this study adopted a hybridization of a fuzzy programming, modify simulated annealing, and simplex downhill (SD) algorithm called Fuzzy-MSASD to resolve multiple objective linear programming APP problems in a fuzzy environment. …”
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Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration
Published 2025“…This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. …”
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Small Farmers' Decisions: Utility Versus Profit Maximization
Published 1982“…The farmers' perception of the riskiness ofaltemative crops are also measured and a quadratic programming algorithm is used to derive the most efficient expected meanvariance (E- V) frontier of each farmer. …”
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An iterative procedure for production-inventory-distribution routing problem
Published 2015“…The aim of PIDRP is to minimize the overall cost of coordinating the production, inventory and transportation over a finite planning horizon. We propose a MatHeuristic algorithm, an optimization algorithm made by the interpolation of metaheuristics and mathematical programming techniques, to solve the model. …”
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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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Optimal timber transportation planning in tropical hill forest using bees algorithm
Published 2022“…This study proposed a multi-objective linear programming model with Bees algorithm (BA) to find an optimal cost TTP for extraction, forest road, and landing locations. …”
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Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations
Published 2014“…For each case study, a capacity model was constructed in Microsoft Excel spreadsheet, as an input to the above mentioned metaheuristic algorithms which programmed in Matlab coding. …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail
Published 2024“…The first two proposed algorithms are validated on 2 reliability test systems (RTS) to represent a small and medium power transmission model namely the IEEE 30-Bus RTS and IEEE 57-Bus RTS. …”
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