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  1. 1

    Path planning algorithm for a car like robot based on MILP method by Mohd Sadri, Mohd Pawzi

    Published 2013
    “…This project is presents an algorithm for path planning optimal routes mobile robot “like a car” to a target in unknown environment. …”
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  2. 2

    Development of dynamic programming algorithm for maintenance scheduling problem by Zafira Adlia, Mohd Fauzi

    Published 2020
    “…The objectives of this research are to develop a dynamic programming algorithm for the maintenance scheduling problem that can deal with the uncertainty and to determine the optimum maintenance schedule that will change according to the uncertainty that happened. …”
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  3. 3

    A genetic algorithm for solving single level lotsizing problems by Zenon, Nasaruddin, Ahmad, Ab. Rahman, Ali, Rosmah

    Published 2003
    “…In this paper a genetic algorithm for solving single level lot-sizing problems is proposed and the results of applying the algorithm toexample problems are discussed. …”
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    Article
  4. 4

    Application of Shuffled Frog Leaping Algorithm (SFLA) to long term generation expansion planning / Nurul Ulya Ibrahim by Ibrahim, Nurul Ulya

    Published 2016
    “…This thesis presents a solution to generation expansion planning problem based on Shuffled Frog Leaping Algorithm (SFLA). …”
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  5. 5

    Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming by Che Ku, Nor Azie Hailma, Omar, Rosli, Sabudin Elia Nadira, Sabudin Elia Nadira

    Published 2016
    “…This paper will review and compare the performances of those existing methods that can find solution without graph search algorithm such as Mixed-Integer Linear Programming (MILP) techniques which exactly solves the problem and then propose four alternative MILP formulations which are computationally less intensive and suited for real-time purposes, but yield a theoretically guaranteed suboptimal solution.…”
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    Article
  6. 6

    Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment by Kalaf, Kalaf, Bayda Atiya

    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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    Development of two matheuristics for production-inventory-distribution routing problem / Dicky Lim Teik Kyee by 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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    Optimize class time tabling by using genetic algorithm technique in UTHM by Ahmad, Izah Rafidah

    Published 2019
    “…A new algorithm was produced to handle the timetabling problem in the university. …”
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    Chaotic mutation immune evolutionary programming for photovoltaic planning in power system / Sharifah Azma Syed Mustaffa by Syed Mustaffa, Sharifah Azma

    Published 2020
    “…The second objective is to determine the location and sizing of multi-unit DGPV for single-objective problem using CMIEP. The element of chaotic local search is also integrated into the algorithm for better performance. …”
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  14. 14

    Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration by Ba-swaimi S., Verayiah R., Ramachandaramurthy V.K., ALAhmad A.K., Padmanaban S.

    Published 2025
    “…The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. …”
    Article
  15. 15

    An iterative procedure for production-inventory-distribution routing problem by Dicky Lim, T.K., Moin, N.H.

    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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    Development of a Production Planning and Control Software in Total Productivity Maintenance Environment by Lee, Pang Seng

    Published 2004
    “…A TPM based software program has been developed to ease the process of production planning and control in TPM environment by executing sequential methodology such as problem identification, literature review, problem formulation, data coliection, company operation review, flow chart system and program algorithm before test run in the company. …”
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  18. 18

    Minimizing makespan of a resource-constrained scheduling problem: a hybrid greedy and genetic algorithms by Mohd Ariffin, Mohd Khairol Anuar, Baharudin, B. T. Hang Tuah, Leman, Zulkiflle, Delgoshaei, Aidin

    Published 2015
    “…To solve this problem, many methods have been proposed before. In this article, an effective backward-forward search method (BFSM) is proposed using Greedy algorithm that is employed as a part of a hybrid with a two-stage genetic algorithm (BFSM-GA). …”
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  19. 19

    Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations by Goheannee

    Published 2014
    “…In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. …”
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  20. 20

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the scanning tasks need to be segregated and assigned for each scanner head, and path planning where the best combinatorial paths for each scanner are determined in order to minimize the total motion of scanning time. …”
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