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Multi-Mode Resource Constrained Project Scheduling Using Differential Evolution Algorithm

機譯：基于差分進化算法的多模式資源受限項目調(diào)度

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Project scheduling is a tool that manages the work and resources associated with delivering a project on time. Project scheduling is important to organize, keep track of the finished and in-progress tasks and manage the quality of work delivered. However, many problems arise during project scheduling. Minimizing project duration is the primary objective. Project cost is also a critical matter, but there will always be a trade off between project time and cost (Ghoddousiet et al., 2013), so scheduling activities can be challenging due to precedence activities, resources, and execution modes. Schedule reduction is heavily dependent on the availability of resources (Zhuo et al., 2013).;There have been several methods used to solve the project scheduling problem. This dissertation will focus on finding the optimal solution with minimum makespan at lowest possible cost. Schedules should help manage the project and not give a general estimate of the project duration. It is important to have realistic time estimates and resources to give accurate schedules. Generally, project scheduling problems are challenging from a computational point of view (Brucker et al., 1999).;This dissertation applies the differential evolution algorithm (DEA) to multi mode, multi resource constrained project scheduling problems. DEA was applied to a common 14- task network through different scenarios, which includes Multi Mode Single Non Renewable Resource Constrained Project Scheduling Problem (MMSNR) and Multi Mode Multiple Non Renewable Resource Constrained Project Scheduling Problem (MMMNR). DEA was also applied when each scenario was faced with a weekly constraint and when cost and time contingencies such as budget drops or change in expected project completion times interfere with the initial project scheduling plan. A benchmark problem was also presented to compare the DEA results with other optimization techniques such as a genetic algorithm (GA), a particle swarm optimization (PSO) and ant colony optimization (ACO). The results indicated that our DEA performs at least as good as these techniques as far as the project time is concerned and outperforms them in computational times and success rates. Finally, a pareto frontier was investigated, resulting in optimal solutions for a multi objective problem focusing on the tradeoff of the constrained set of parameters.
機譯：項目計劃是一種工具，用于管理與按時交付項目相關(guān)的工作和資源。項目安排對于組織，跟蹤已完成和正在進行的任務(wù)以及管理交付的工作質(zhì)量非常重要。但是，在項目計劃過程中會出現(xiàn)許多問題。最小化項目工期是主要目標。項目成本也是一個至關(guān)重要的問題，但是項目時間和成本之間總是存在折衷關(guān)系（Ghoddousiet等人，2013），因此，由于優(yōu)先活動，資源和執(zhí)行模式，調(diào)度活動可能會面臨挑戰(zhàn)。進度計劃的減少在很大程度上取決于資源的可用性（Zhuo等人，2013）。;已經(jīng)有幾種方法可以解決項目進度計劃的問題。本文將重點研究以最小的制造成本和最低的成本找到最佳的解決方案。進度表應(yīng)有助于管理項目，而不是對項目持續(xù)時間進行總體估計。重要的是要有切合實際的時間估計和資源來制定準確的時間表。通常，從計算的角度來看，項目調(diào)度問題具有挑戰(zhàn)性（Brucker等，1999）。本文將差分進化算法（DEA）應(yīng)用于多模式，多資源受限的項目調(diào)度問題。 DEA通過不同的場景應(yīng)用于常見的14任務(wù)網(wǎng)絡(luò)，其中包括多模式單一不可再生資源受限項目計劃問題（MMSNR）和多模式多個不可再生資源受限項目計劃問題（MMMNR）。當每種情況都面臨每周限制，并且成本和時間的意外情況（例如預(yù)算下降或預(yù)期項目完成時間的更改）干擾初始項目計劃時，也將應(yīng)用DEA。還提出了一個基準問題，以將DEA結(jié)果與其他優(yōu)化技術(shù)（例如遺傳算法（GA），粒子群優(yōu)化（PSO）和蟻群優(yōu)化（ACO））進行比較。結(jié)果表明，就項目時間而言，我們的DEA至少與這些技術(shù)一樣好，并且在計算時間和成功率方面均優(yōu)于它們。最后，研究了一個pareto前沿，得出了針對多目標問題的最佳解決方案，該解決方案關(guān)注于約束參數(shù)集的折衷。

著錄項

  • 作者

    Altarazi, Faisal Manour.;

  • 作者單位

    Old Dominion University.;

  • 授予單位 Old Dominion University.;
  • 學(xué)科 Mechanical engineering.
  • 學(xué)位 Ph.D.
  • 年度 2017
  • 頁碼 103 p.
  • 總頁數(shù) 103
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 古生物學(xué);
  • 關(guān)鍵詞

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