An MILP Model for Scheduling Multistage, Multiproducts Milk Processing

Touil, Achraf; Echchatbi, Abdelwahed; Charkaoui, Abdelkabir

doi:10.1016/j.ifacol.2016.07.884

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…Examples of case studies in processing industries are those described in Refs. [82,86,84,59,41,34,54,33,14,21,81]. The assembling industries are those companies or industrial sectors that carry out the final assembly of a set of parts supplied by other factories, thus concluding a manufacturing process that covers several production units, as is the case in the production of aircraft, automobiles, electronic devices, computers, amongst others, and these were studied in Refs.…”

Section: Interpretation and Analysis Of Resultsmentioning

confidence: 99%

“…Particularly interesting, for example, is the explicit treatment of setup times, i.e. they are treated separately from the job processing times in quite a number of studies [82,46,86,70,84,59,78,9,41,26,16,45,27,81].…”

Section: Interpretation and Analysis Of Resultsmentioning

confidence: 99%

“…A MILP model was proposed in Ref. [81] to represent an optimization problem in a dairy industry, including sequence-dependent setup times, machine speed and capacity constraints.…”

Section: Description Of the Reviewed Papersmentioning

confidence: 99%

See 2 more Smart Citations

A survey of case studies in production scheduling: Analysis and perspectives

Fuchigami

Rangel

2018

Journal of Computational Science

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The problems of production scheduling and sequencing refer to decision making regarding the designation of jobs to available resources and their subsequent order to optimize pre-defined performance measures. From the early days of research in this area until this last decade, the publication of case studies has been scarce, with their frequency only increasing very recently. This survey aims to highlight practical research and case studies published in the literature in the scheduling area, identifying the main characteristics of the problems treated, trends in this research and also gaps showing potential areas for future study.

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Section: Interpretation and Analysis Of Resultsmentioning

confidence: 99%

Section: Interpretation and Analysis Of Resultsmentioning

confidence: 99%

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A survey of case studies in production scheduling: Analysis and perspectives

Fuchigami

Rangel

2018

Journal of Computational Science

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“…Thirty-two different scenarios were considered and a significant decrease in the total waste and makespan was achieved in approximately 1 h of CPU time. Touil et al [103] deployed an MILP model for a small multiproduct milk industry, located in Morocco, aiming at the minimization of makespan. The stages of homogenization, pasteurization and packaging were scheduled for four final products, seven packing lines, two pasteurization units and one homogenizer.…”

Section: Food Industriesmentioning

confidence: 99%

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

2019

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Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.

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“…Touil, Echchatbi, and Charkaoui () developed a MILP to address production scheduling problems in multistage, multiproduct milk processing. The computational results with a small instance is presented to test the proposed model.…”

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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An MILP Model for Scheduling Multistage, Multiproducts Milk Processing

Cited by 14 publications

References 6 publications

A survey of case studies in production scheduling: Analysis and perspectives

A survey of case studies in production scheduling: Analysis and perspectives

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

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

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