Scheduling of Multistage Multiproduct Batch Plants Operating in a Campaign-Mode

Fumero, Yanina; Corsano, Gabriela; Montagna, Jorge Marcelo

doi:10.1021/ie201757t

Cited by 14 publications

(16 citation statements)

References 26 publications

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“…Also, according to the rule recently mentioned, in stage 1, slots 2 and 3 are only used on unit 1, slots 1, 5, and 6 are used on unit 2, and slots 4 and 7 are only used on unit 3. This preordering constraint in the assignment of batches to slots has been used by Fumero et al , where the authors have shown that this rule can be considered a good heuristic strategy for solving scheduling problems.…”

Section: Problem Descriptionmentioning

confidence: 99%

Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

Ackermann

Fumero

Montagna

2018

Ind. Eng. Chem. Res.

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In recent years, many companies have shifted from single-site production to more complex manufacturing environments involving multiple multiproduct facilities. At the operational level, the scheduling posed on each individual facility does not lead to efficient use of resources and consequently an optimal production management over the whole production system cannot be achieved. In this context, a multisite production scheduling framework is fundamental to respond appropriately to the market requirements. In this work, a mixed-integer linear programming (MILP) model for the simultaneous batching and scheduling of multisite multiproduct batch plants with nonidentical parallel units is proposed. Given a set of manufacturing sites with unit capacities and structure known and multiple orders with associated release and due dates, the proposed approach determines the plant where each customer order must be processed, the number and sizes of batches to satisfy each order, the assignment of these batches to units, as well as the sequencing and timing of batches on each unit of the plant that processes them, in order to minimize the global makespan related to the whole production system. The application and performance of the proposed model are highlighted through numerical examples.

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Section: Problem Descriptionmentioning

confidence: 99%

Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

Ackermann

Fumero

Montagna

2018

Ind. Eng. Chem. Res.

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“…In practice, it is common to select a set of production resources to define a production line that will operate during a certain time period, sufficient to supply a given demand. More recently, Fumero et al (2012) presented a solution approach for the scheduling of multistage multiproduct batch plants. They first solve a simplified slot-based continuous -time formulation that involves preordering constraints for the assignment of batches to slots in each stage.…”

Section: Scheduling Of Multipurpose Batch Plantsmentioning

confidence: 99%

Simultaneous regular and non-regular production scheduling of multipurpose batch plants: A real chemical–pharmaceutical case study

Moniz

Barbosa‐Póvoa

Sousa

2014

Computers & Chemical Engineering

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Regular and non-regular production can often be found in multipurpose batch plants, requiring two distinct operating strategies: campaign and short-term production. This paper proposes a solution approach for simultaneous scheduling of campaign and short-term products in multipurpose batch plants. Regular products follow a cyclic schedule and must cover several product deliveries during the scheduling horizon, while non-regular products have a non-cyclic schedule. The proposed approach explores the Resource-Task Network (RTN) discrete-time formulation. Moreover, a rolling horizon approach, and reformulation and branching strategies have been applied to deal with the computational complexity of the scheduling problem. Real case instances of a chemical-pharmaceutical industry are solved, showing the applicability of the solution approach.

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“…Fumero, Corsano, and Montagna () proposed models based on MILP for scheduling of a multistage multiproduct batch plant with identical parallel units, operating in a campaign mode.…”

Section: Introductionmentioning

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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Scheduling of Multistage Multiproduct Batch Plants Operating in a Campaign-Mode

Cited by 14 publications

References 26 publications

Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

Simultaneous regular and non-regular production scheduling of multipurpose batch plants: A real chemical–pharmaceutical case study

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

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