本研究開發一個以區域最佳解為基礎的群體式 (population-based) 啟發式演算法(簡稱HLBS)，來求解流程式排程（flow shop）之最大流程時間的最小化問題。其中，HLBS會先建置一個跟隨模型來導引搜尋機制，然後，運用過濾策略來預防重複搜尋相同解空間而陷入區域最佳解的困境；但搜尋仍有可能會陷入區域最佳解，這時，HLBS則會啟動跳脫策略來協助跳出區域最佳解，以進入新的區域之搜尋；為驗證HLBS演算法的績效，本研究利用著名的Taillard 測試題庫來進行評估，除證明跟隨模型、過濾策略和跳脫策略的效用外，也提出實驗結果證明HLBS較其他知名群體式啟發式演算法(如基因演算法、蟻群演算法以及粒子群最佳化演算法)之效能為優。 / This research proposes population-based metaheuristic based on the local best solution (HLBS) for the permutation flow shop scheduling problem (PFSP-makespan). The proposed metaheuristic operates through three mechanisms: (i) it introduces a new method to produce a trace-model for guiding the search, (ii) it applies a new filter strategy to filter the solution regions that have been reviewed and guides the search to new solution regions in order to keep the search from trapping into local optima, and (iii) it initiates a new jump strategy to help the search escape if the search does become trapped at a local optimum. Computational experiments on the well-known Taillard's benchmark data sets will be performed to evaluate the effects of the trace-model generating rule, the filter strategy, and the jump strategy on the performance of HLBS, and to compare the performance of HLBS with all the promising population-based metaheuristics related to Genetic Algorithms (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

[pt] Esta dissertação de mestrado analisa um problema de programação de máquinas em série com datas de entrega e ganhos cumulativos sob incerteza. Em particular, este trabalho considera situações reais na quais os tempos de processamento e datas de liberação são estocásticos. O objetivo principal deste trabalho é a resolução deste problema de programação de máquinas em série em um ambiente estocástico buscando analisar a relação entre diferentes niveis de incerteza e o benefício esperado. Visando atingir este objetivo, primeiramente uma heurística é proposta utilizando-se da técnica de biased-randomization para a versão determinística do problema. Então, esta heurística é extendida para uma metaheurística a partir do encapsulamento dentro da estrutura de um variable neighborhood descend. Finalmente, a metaheurística é extendida para uma simheurística a partir da incorporação da simulação de Monte Carlo. De acordo com os experimentos computacionais, o nível de incerteza tem um impacto direto nas soluções geradas pela simheurística. Além disso, análise de risco foram desenvolvidas utilizando as conhecidas métricas de risco: value at risk e conditional value at risk. / [en] This master s thesis analyzes the Permutation Flow-shop Scheduling Problem with Delivery Dates and Cumulative Payoffs under uncertainty conditions. In particular, the work considers the realistic situation in which processing times and release dates are stochastics. The main goal is to solve this Permutation Flow-shop problem in the stochastic environment and analyze the relationship between different levels of uncertainty and the expected payoff. In order to achieve this goal, first a biased-randomized heuristic is proposed for the deterministic version of the problem. Then, this heuristic is extended into a metaheuristic by encapsulating it into a variable neighborhood descent framework. Finally, the metaheuristic is extended into a simheuristic by incorporating Monte Carlo simulation. According to the computational experiments, the level of uncertainty has a direct impact on the solutions provided by the simheuristic. Moreover, a risk analysis is performed using two well-known metrics: the value at risk and the conditional value at risk.