• 沒有找到結果。

在 全溫層物流設施規劃研究部分,本研究首先將全溫層物流技術 與各溫層貨物納入考量,所以本研究所建構之模式主要考量為成本最 小化、滿足需求與容量限制等,並未將目前民眾日趨重視之時效與品 質等議題納入考量,且本研究為測試該模式之可行性,僅以小型案例 作測試,未來建議可朝向大型案例作測試,並將民眾日趨重視之時效 與品質等議題納入考量。另因本研究同時考量到全溫層物流技術、各 溫層貨物、車輛型式、車輛大小與服務頻次等,導致構建本研究模式 之複雜度高,因此本研究另外建構車輛成本演算法與其求解架構,其 中車輛成本演算法主要利用貪婪原則,此屬於最單純之演算法,未來 可朝向建構更複雜且更周密之演算法;該求解架構則採用分 支 定 限 法 中 評 選 法 則 的 越優解越先搜尋方法,其達到終止條件前所計算的 子問題個數,平均而言是最低的,有較低的時間複雜度,且因本研究 的範例規模較小,對於陷入區域解(local optimal)則有較低的風險,且 樹狀的結構子問題分解,屬於啟發式的分解方式,未來可嘗試使用不 同的起始解與其他已發展之巨集演算法,應可避免執行演算法時落入 區域解(local optimal)的問題。

在 低溫物流自動倉儲系統動態儲位指派模式研究部分,本研究能 根 據 未 來 趨 勢 在 需 求 型 態 轉 變 、 技 術 相 關 參 數 改 變 下 等 不 同 情 境 環 境 下 , 對 於 已 跨 足 多 溫 層 商 品 市 場 業 者 在 營 運 上 之 影 響 , 以 及 就 有 意 跨 足 多 溫 層 商 品 市 場 之 業 者 擬 定 本 研 究 認 為 適 切 之 營 運 規 劃 內 容 以 供 參 考 。 由於真實範例取得不易,故本研 究在此僅針對小型的範例進行測試,未來仍可嘗試以實例的方式與不 同之自動倉儲系統來進行分析,如此分析出來的結果將更具信賴度;

而在需求量的預測方面,本研究以灰預測 GM(1,1),且僅以四筆資料 做需求量預測,係為簡化求解上的複雜度,未來仍可以較多筆的資料 或較複雜的模式來進行預測,可能會有更佳之效果。此外,本研究係 以低溫食品為研究對象,未來亦可針對物流環境要求較為嚴峻之電子

科技與生化科技等特殊商品,針對各種環境與技術加入腐敗與存貨等 成本進行深入的探討。

綜上所述,本論文有關全溫層物流設施與營運規劃研究之具體建 議彙整如下:

1. 未來可朝向大型案例與真實案例作測試。

2. 未來可針對民眾日趨重視之時效與品質等議題納入模式中一 併考量。

3. 本研究所建構之車輛成本演算法主要利用貪婪 原則,此屬於 最 單 純 之 演 算 法 , 未 來 可 朝 向 建 構 更 複 雜 且 更 周 密 之 演 算 法,並搭配適合之求解架構,以使求解結果更接近最佳解。

4. 本研究求解架 構 則採 用分 支 定 限 法 中 評 選 法 則 的 越優解 越先搜尋方法,其達到終止條件前所計算的子問題個數,平 均而言是最低的,有較低的時間複雜度,且因本研究的範例 規模較小,對於陷入區域解(local optimal)則有較低的風險,

且樹狀的結構子問題分解,屬於啟發式的求解方式,未來可 嘗試使用不同的起始解與其他已發展之巨集演算法,應可避 免執行演算法時落入區域解(local optimal)的問題。

5. 本研究以灰預測 GM(1,1),且僅以四筆資料做需求量預測,係 為簡化求解上的複雜度,未來仍可以較多筆的資料或較複雜 的模式來進行預測,可能會有更佳之效果。

6. 本研究係以低溫食品為研究對象,未來可針對需求較為嚴峻 的物流環境,如電子科技與生化科技等特殊商品,針對各種 環境與技術加入腐敗與存貨等成本來進行深入的探討。

99

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