Multistage Production Planning in the Dairy Industry: A Mixed-Integer Programming Approach

Bilgen, Bilge; Doğan, Koray

doi:10.1021/acs.iecr.5b02247

Cited by 23 publications

(16 citation statements)

References 32 publications

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“…The inclusion of the factor for intermediate production increases the efficiency of the model. This model utilizes a deterministic demand for production planning [17].…”

Section: Introductionmentioning

confidence: 99%

Developing a Multi-Stage Production Planning and Scheduling Model for a Small-Size Food and Beverage Company

Tirkes¹,

Çelebi²,

Güray³

2021

JESA

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A great deal of research has been undertaken in recent years related to facility capacity expansion and production planning problems under deterministic and stochastic constraints in the literature. However, only a small portion of this work directly addresses the issues faced by the food and beverage industry, especially in small-sized enterprises. In this study, a Mixed-Integer Linear Programming model (MILP) is developed for production planning and scheduling decisions for a small-size company producing syrup and jam products. The main constraint is that the multiple syrup and jam production lines in the model share the same limited-capacity module designed for inventory planning. To this end, the present model offers an efficient solution for executing a multi-product, multi-period production line by finding the most satisfactory strategy to match the right product with the useable capacity leading to profit maximization. The present approach is capable of coping with varying demands by offering a detailed costing procedure and implementing an effective inventory model.

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“…The inclusion of the factor for intermediate production increases the efficiency of the model. This model utilizes a deterministic demand for production planning [17].…”

Section: Introductionmentioning

confidence: 99%

Developing a Multi-Stage Production Planning and Scheduling Model for a Small-Size Food and Beverage Company

Tirkes¹,

Çelebi²,

Güray³

2021

JESA

View full text Add to dashboard Cite

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“…The associated complexities make the generation of production schedules neither an easy nor intuitive task. Compared with other industrial processes, the food industry and, in particular, the dairy industry exhibits specific characteristics such as divergent product structures, seasonal product demand, high demand variability, multiple intermediate products with limited storage capacity feeding many finished goods, high consumer-driven demand variability, long lead times for some packaging materials, and a high level of complexity in the production process (Bilgen and Dogan, 2015). The problem becomes even more complex when the production schedule also has to take into account other aspects such as guaranteeing the required equipment is cleaned at the right time or aiming at energy consumption reduction, among others.…”

Section: Introductionmentioning

confidence: 99%

Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach

Escobet¹,

Puig²,

Quevedo³

et al. 2019

Computers & Chemical Engineering

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This paper presents the optimal batch scheduling of a multi-product dairy process using an approach that combines optimization and constraint programming techniques. A suitable model describing the subprocesses and production rules is developed allowing to obtain scheduling constraints relating the production process and the machines available together with their relative efficiencies. After the scheduling problem has been formulated, the batch scheduling of a real powder milk/yogurt process is obtained in an optimal manner using the proposed approach with the objective of meeting customers' deadlines considering the efficiencies/costs of available alternative machines. Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided. This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications.

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“…Bilgen and Dogan () presents a novel MILP model for the production planning of a multistage process within the dairy industry, it encompases the main characteristics of many food production.…”

Section: Introductionmentioning

confidence: 99%

“…The computational results with a small instance is presented to test the proposed model. Bilgen and Dogan (2015) presents a novel MILP model for the production planning of a multistage process within the dairy industry, it encompases the main characteristics of many food production.…”

mentioning

confidence: 99%

Production optimization in sausage industry based on the demand of the products

Polon

Alves

Olivo

et al. 2017

J Food Process Engineering

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The sausage production industry has recently had an increase in consumption. This led to the search for the most efficient use of available resources in order to achieve increased productivity, reduced costs, reliability in service, and reduce delivery times. In the sausage industry a wide variety of products is obtained, most of these products do not has an exclusive line, and therefore there is a competition for equipment and intermediate states. The manufactured products are usually perishable and it is recommended that production should not exceed the demand under the disadvantage of stocks generating and exceed the shelf life of the product. The objective of this work was the adaptation and use of a mathematical model of production scheduling to optimize the production of a sausage industry, from demand values for each product. The proposed approach treats the problem as mixed integer linear programming to represent the decisions involved and was solved with the GAMS software (Generic Algebraic Modeling System). The developed model was applied to the problem of production scheduling with actual operational data from a regional industry. The results of the application of the model with data collected in the industry were an increase of the total production of the plant in the range of 20%. and has shown its capacity to generate better results than the production results presented by industry. Therefore, the result of the model presented here can assist in making the production scheduling decisions in the sausage industry. Practical applications The objective of the work was to maximize the production of the products taking into account the demand of each of them avoiding the formation of stocks due to the useful life of the products. For this, we use a mathematic model adapted to the sausage industry that operates in batch. The production scheduling was done in a single campaign mode for weekly production in the industry and short‐term production planning was done to produce the stocks of intermediate products needed at the beginning of the campaign. The results presented in the Gantt tables can be used on the factory floor. The results of the application of the model with data collected in the industry were an increase of the total production of the plant in the range of 20%.

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Multistage Production Planning in the Dairy Industry: A Mixed-Integer Programming Approach

Cited by 23 publications

References 32 publications

Developing a Multi-Stage Production Planning and Scheduling Model for a Small-Size Food and Beverage Company

Developing a Multi-Stage Production Planning and Scheduling Model for a Small-Size Food and Beverage Company

Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach

Production optimization in sausage industry based on the demand of the products

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