餐飲業製餐排程研究

Scheduling Problem of Making Food for Catering Industry

鄭為中(Wei-Chung Cheng)

指導教授 ： 張政元

中原大學/工學院/電機工程研究所/碩士(2013年)

https://doi.org/10.6840/CYCU.2013.00340

摘要

本論文提出一種類神經製餐排程演算法(Neural Food-Making Scheduling algorithm, NFMS)，目的是加入排程機制在待做餐點清單上，使廚師能縮短製餐時間和顧客等待餐點時間，並降低顧客的抱怨。一般的點餐系統是將顧客點餐資料做整合處理後，再由螢幕顯示。但決定製餐順序是廚師。所以難免在尖峰時刻或廚師經驗不足下決策順序，反而會造成顧客餐點太晚送達，這些事都會影響顧客的評價與店家的服務品質。
除了提出NFMS演算法外，因為排程問題是NP-Complete的類型，所以在某些情況的排序下，可能導致顧客的餐點等待過久，因此增加了即時更新待做餐點清單機制和顧客等待時間提醒。在餐點的製餐順序上，廚師可以選擇系統排序或自己決定。當不依據系統排序製作時，將會更新待做顧客餐點。顧客提醒機制則是因為某些顧客點餐資料排序後，反而會造成某些顧客餐點超過可以忍受的等待時間，所以加入了保護措施。
在實驗結果中，我們利用三種方式互相比較與探討，分別是先進先(First In First Out, FIFO)演算法、製餐排程(Food-Making Scheduling, FMS)演算法和NFMS演算法，從顧客等待平均時間來看，發現類神經製餐排程演算法比起另外二種方式，的確縮短了更多顧客總平均等待時間，所以此方式的確是可以提高製餐效率和減少顧客等待餐點時間。

關鍵字

即時更新機制 ； 製餐排程演算法 ； 類神經製餐排程演算法

並列摘要

This paper presents the Neural Food-Making Scheduling algorithm (NFMS) and the purpose of scheduling mechanism added in the to-do list of meals. It can reduces the time of food preparation, waiting and the frequency of customers’ complain. The function of the general ordering system combines the customer ordering information, and shows the information by LCD; however, the food preparation orders still depends on the cook’s decision. It is not a good idea to prepare food in first-in-first-out order during the rush-hour or if the cook is lack of experience, because it will delay the food delivery and affect the quality of service.
In addition to NFMS, the scheduling problem is NP-Complete. When you use the NFMS, it may let few customers’ meals to be made later. In order to reduce the problems, the real-time updating mechanism for to-do list of meals and clock function is added. They help to remind customers’ waiting time as well. The cook can decide the either following the food order or not. When the cook decides not to follow the food order, the system will automatically refresh and update the customers’ food orders. The real-time updating mechanism solves the problems of using the FMS that has both pros and cons for all customers; therefore, it is necessary to be added.
During the experiment, the author is able to analysis the results by comparing the results from three different methods, which are first-in-first-out (FIFO), Food-Making Scheduling algorithm (FMS), and NFMS. Comparing to two other methods, system with NFMS certainly reduces the average customers’ waiting time frame. To conclude, FMS can improve the efficiency of food production and reduce customer’s waiting time. Thus, it would also reduce the frequency of customers’ complain and improve the quality of restaurant service.

並列關鍵字

real-time updating mechanism ； Food-Making Scheduling algorithm (FMS) ； Neural Food-Making Scheduling (NFMS)

參考文獻

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