The beverage market has become very dynamic and competitive, and wine marketing strategies involve, besides the quality aspect, the aspect of precise order delivery. Moreover, bottling plant planning is difficult, as consumer demand is highly influenced by seasonality and many different types of packaging are requested. This article presents a new scheduling method for planning bottling activities in modern wineries. The bottling plant has been assumed as a single-machine job shop where wine orders of different amounts and due dates must be processed. In order to limit the complexity introduced by the large number of variables and constraints, the sequencing has been obtained by means of a two-step procedure based on mixed-integer linear programming (MILP) algorithms. The optimization takes into account, for each wine type, data on production rates, storage levels, minimum batch size, labor and storage costs, and risk due to not having the minimum storage and having lost sales. The method operates on a finite time horizon, typically four weeks, with recursive rescheduling each week. The effectiveness of the proposed method is shown by an example using data collected in a large winery in the Piedmont region of Italy.
Wine bottling scheduling optimization
BERRUTO, Remigio;TORTIA, Cristina;GAY, Paolo
2006-01-01
Abstract
The beverage market has become very dynamic and competitive, and wine marketing strategies involve, besides the quality aspect, the aspect of precise order delivery. Moreover, bottling plant planning is difficult, as consumer demand is highly influenced by seasonality and many different types of packaging are requested. This article presents a new scheduling method for planning bottling activities in modern wineries. The bottling plant has been assumed as a single-machine job shop where wine orders of different amounts and due dates must be processed. In order to limit the complexity introduced by the large number of variables and constraints, the sequencing has been obtained by means of a two-step procedure based on mixed-integer linear programming (MILP) algorithms. The optimization takes into account, for each wine type, data on production rates, storage levels, minimum batch size, labor and storage costs, and risk due to not having the minimum storage and having lost sales. The method operates on a finite time horizon, typically four weeks, with recursive rescheduling each week. The effectiveness of the proposed method is shown by an example using data collected in a large winery in the Piedmont region of Italy.File | Dimensione | Formato | |
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