本研究針對流程型生產系統，探討多代理人排程。多代理人排程是指將工件分成多個子集合，子集合分別屬於不同的代理人，代理人有各自的目標。本研究探討二代理人於多機台流程型生產系統，欲決定待加工工件進入系統之順序排程。考量到待加工工件分別屬於兩個代理人，代理人1、2的目標函數分別設定為最大完工時間和總延遲時間，兩個代理人之間會競爭生產資源的使用權。因此，在排程時需綜合考量所有代理人的目標，使得排程方案為所有代理人接受。
本研究採用Pareto最優的觀念同時考量兩個代理人的目標，目的是找出一組Pareto最優的排程方案。研究方法為利用模擬軟體在考量所有機台、工件、代理人等資訊後，建構相應的模擬模式，以評估排程績效。接著，在面臨可行解空間過大時，設計多目標粒子群演算法(Multi-Objective Particle Swarm Optimization，MOPSO)進行最佳方案的搜尋；為了改善MOPSO收斂性差的不足，本研究從問題的二代理人特性出發，在粒子群的設定、粒子位置更新機制、Pbest更新機制和初始解的產生等方面進行改進，提出基於代理人的多目標粒子群演算法(Agent-based Multi-Objective Particle Swarm Optimization，AMOPSO)。本研究對兩種演算法進行參數分析，以提高搜尋效率和求解品質。
最後，比較兩種算法和變動鄰域搜尋(Variable Neighborhood Search)在此問題上的求解性能，研究結果顯示，基於代理人的多目標粒子群演算法可以求得在距離、範圍和多樣性指標上表現較佳的解。

In this research, we study the multi-agent scheduling problem in flow shop environment. In a multi-agent scheduling problem, the set of jobs is divided into several subsets. There are several agents, each interested in a subset of jobs and has its own objective. In this paper flow shop scheduling problem with two agents is considered, in which the processing sequence of all jobs is to be determined. Each job belongs to either of the agents, the objective function of agent 1 is the makespan of its jobs and agent 2 is the total tardiness of its jobs. Two agents have to compete on the use of common processing resources. Every agent’s objective must be considered in the scheduling of jobs and the final schedule must be acceptable to all agents.
Two agents’ objectives are measured in Pareto optimality and final result is a set of Pareto optimal scheduling solutions. A simulation model including information of machines, jobs and agents is constructed to evaluate performances. The search of optimal solutions in problems with large feasible solution space is through Multi-Objective Particle Swarm Optimization. Due to the low convergence of MOPSO, we propose new swarm setting, position updating, personal best position updating and initialization operations based on two-agent features. The improved new algorithm is called Agent-based MOPSO. The parameter analysis is conducted to determine the best parameter combination in searching efficiency and solution quality.
In the end, two MOPSO algorithms are compared with Variable Neighborhood Search in two-agent flow shop scheduling problem. Results show that Agent-based MOPSO performs better than the other two algorithms in distance metric, space metric and diversity metric in this problem.

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