價格與交期多目標差異化服務模式之研究－以手工木造廠為例

差異化服務之應用非常廣泛，當顧客需求不一定的時候，差異化服務能夠滿足不同顧客的需求，本研究以一傳統手工木造廠為例，探討價格與交期之差異化服務，選擇此二變數主要原因是傳統木工經常因為製造時間的問題，而造成許多因為趕工以及生產時程不確定等問題，造成許多額外之成本，因此，如果配合交期訂定不同之價格，如此一來，便可以讓顧客在選擇交期的同時也得到相對之時間報酬，如此之差異化服務對其他傳統製造產業，更可提供非常有力之參考價值。本研究實驗環境架構以多目標粒子群演算法(MOPSO)為主，加入模擬系統生成之適應函式(fitness)求解訂單接受度與每日平均利潤之非凌駕解集合，粒子是15組乘以2維度之多維組合陣列，可同時模擬多種差異化服務之組合；為了跳脫MOPSO之區域最佳解問題，本研究亦納入最大鄰近解搜尋法(LNS)，結果顯示，結合後之MOPSOLNS方法比原來方法可以得到更好的探索解集合。而差異化服務的結果更探討出15組取7組的位置移動策略能夠比實際廠商一次衡量一種產品之差異化服務，有更好的訂單接受度與利潤，在微利的時代中，更可以把目標鎖定在如何擁有高訂單接受度也不失其利潤的最佳組合。

關鍵字

傳統製造產業；差異化服務；多目標粒子群演算法；最大鄰近解搜尋法；區域最佳解

並列摘要

Differential service plays an important role in enterprise event marketing. It satisfies different types of customers and meets their needs. This thesis takes the industry of handmade wood products for example, and discusses the differential service on pricing and due-date setting. This industry frequently bears additional costs because it faces the challenges of various productions scheduling due to the uncertain environment. Therefore, having a rule which the enterprise can refer to will provide direction, stability, and will also enhance the self-competitiveness. This study proposes a model of changing pricing and due-date, supported by multi-objective particle swarm optimization (MOPSO) which includes the average daily profit and acceptance rate of product order. In order to prevent particles from premature convergence and trap into local optimal solution, using the large neighborhood search (LNS) for particles beyond the optimal solution of the restricted region and speedy to obtain the most similar best set. 15 sets of two-dimensional arrays and a multi-objective task application are adopted to verify the proposed method, and simulate a variety of the company's portfolio of differential service. According to the results, the MOPSOLNS method is better than the traditional PSO method in solving the case of multi-objective problems. Furthermore, this thesis also further explores 7 sets of location shift strategies which have better profit and acceptance rate of product order. In the trend of micro profit, enterprise can enhance customer’s satisfaction without reducing profit.

並列關鍵字

Handmade wood products industry ； Differential service ； Multi-objective particle swarm optimization(MOPSO) ； Large neighborhood search(LNS) ； Local optimal solution

參考文獻

李建緯(2007)，以多目標粒子群最佳化演算法探勘分類法則，國立成功大學碩士論文，台南市。

陳建宇(2006)，以基因演算法結合層級分析法求解多廠區訂單分配，國立政治大學碩士論文，台北市。

Coello, C. A. C., Pulido, G. T. & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279.

Coello, C. A. C. & Lechuga, M. S. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. Paper presented at the Evolutionary Computation, 2002.

Eberhart, R. & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995.

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價格與交期多目標差異化服務模式之研究－以手工木造廠為例

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