An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company

Paiva, Rafael Piatti Oititica de; Morábito, Reinaldo

doi:10.1007/s10479-008-0428-9

Cited by 54 publications

(28 citation statements)

References 24 publications

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“…This phenomenon is frequently occurring in process industries as for example steel-, forestry-, chemical-and oil industry (Lasschuit and Thijssen, 2004, Viswanadham and Raghavan, 2000, D'Amours et al, 2008, Paiva and Morabito, 2009. A steel slab can be rolled into plates of different thicknesses which then can be cut into different lengths and widths.…”

Section: Raw Materials Properties and Divergent Bill Of Materialsmentioning

confidence: 99%

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Kylinger¹

2015

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Process industries has for long been important for the development of Swedish industry and society. All industries face different conditions that affect how to best run their operations. This thesis aims to describe some of the conditions that characterize process industries compared to other industries. Further one of these characteristics has been studied more closely.One of the traits of process industries is that they are positioned at the start of the transformation process close to the raw material mixing, separating or forming it into products often used for further transformation. Process industries hence become dependent on the properties of these materials. One of the most prominent characteristics inherent from the raw material properties is the divergent bill of material. The divergent bill of material originates from the fact that a given raw material is made up of different components that will yield several products with different characteristics when processed. When splitting the raw material into the desired products the yielded products from a certain raw material usually have different value to the producer, some more desired than others. These multiple products generated poses a challenge from a planning perspective raising questions like "How should we balance the supply and demand of all the products produced?", "What shall we do with the excess products produced?".The first paper in this thesis describe the production planning in four Swedish process industries with the ultimate aim to connect their planning to the supply chain characteristics they face as process industries. The study concludes that the industry specific conditions mainly affect planning at short time ranges when planning becomes more detailed. In general the use of planning or decision support systems is low, stemming from a, warranted or not, belief that general decision support systems do not fit process industries. Another finding is that the case companies mainly operate in niche markets. This study also highlighted that the planning complexity arising from characteristic of co-and by-product generation in combination with the lack of decision support systems requires further studies.The subsequent two papers focus on supply chain planning and coordination with a divergent bill of material. They present a mathematical model over the supply chain planning in a real case company in the specialty oils industry. The second paper investigates transfer pricing as a coordination tool by comparing decentralised supply chain planning with centralized planning in an integrated model. Transfer pricing is found to have a potential positive effect on supply chain planning while simultaneously creating problems in terms of uneven distribution of the contribution margin among supply chain partners.Finally the third paper more closely investigates different ways to set transfer prices and comparing them to the optimal transfer prices. Setting optimal transfer prices with a divergent bill of material has proven to be less str...

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Section: Raw Materials Properties and Divergent Bill Of Materialsmentioning

confidence: 99%

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Kylinger¹

2015

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“…According to Kallrath (2005), supply chain planning problems are commonly solved using mixed integer linear optimization. Paiva and Morabito (2009) also present a mixed integer optimization model, but for aggregated production planning in a sugar and ethanol milling company. Aiming to provide support for decision making, Paiva and Morabito (2009) as such involve the choice of production processes, quantities, inventory levels, suppliers, and transportation in the model.…”

Section: Mathematical Modelling In Process Industriesmentioning

confidence: 99%

“…Paiva and Morabito (2009) also present a mixed integer optimization model, but for aggregated production planning in a sugar and ethanol milling company. Aiming to provide support for decision making, Paiva and Morabito (2009) as such involve the choice of production processes, quantities, inventory levels, suppliers, and transportation in the model. Mathematical modelling can also be useful for different kinds of Decision Support Systems in process industry environments (e.g.…”

Section: Mathematical Modelling In Process Industriesmentioning

confidence: 99%

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Planning production and supply chain in energy intensive process industries

Waldemarsson

2014

Linköping Studies in Science and Technology. Dissertations

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To make a difference among the energy intensive process industries, this dissertation addresses production planning and supply chain planning problems related to industrial energy management issues. The energy issue is turning more and more important from different angles, involving price as well as environmental problems due to climate change leading to political pressure on all energy users. The process industry sector is one of the largest users of energy, and thus important to analyse. Process industries are also capital intensive and operate on large and expensive process equipment, making it imperative to plan their production well in order to reach preferable capacity utilisation. Therefore this dissertation strives to locate the most important energy management issues for the long term profitability of process industries, and investigates the symbiotic effects of including energy issues in production and supply chain planning.Three different studies at three case companies are carried out, analysed, and presented in five papers. The cases represent the process industry sectors: chemicals, pulp, and steel. Both qualitative case study methodologies as well as quantitative mathematical modelling and optimisation approaches have been practiced. The research questions are analysed from both an energy system and from a production process point of view, separately as well as combined. Energy is somewhat considered to be the main workforce for process industries and this dissertation exemplifies some of its most important dimensions in this context. Several prerequisites for putting energy management on the strategic agenda are located in a specialty chemical industry where the importance of introducing a strategic perspective on energy, the way energy is used, and the possibilities of increasing alternative revenue from utilising by-and/or co-products differently are pinpointed. Approaches for including energy issues in planning processes are also suggested in terms of a MILP model for the entire supply chain of a pulp company, including decisions on purchase and transportation of raw materials, production allocation, energy mix, and distribution. Another example is presented based on the perspectives of economics of scale and lot sizing through economic order quantity principles in a steel company. By using real company data, energy smart approaches in planning and scheduling are developed with respect to the most important intersections between the production processes and their supporting energy system. The accumulated resource intensity and embedded energy could, and probably should, hence be more fairly reflected in the product price. The research finally shows some possible impact with including energy issues in a production and supply chain planning model. By planning differently, production prioritisations can be done, and it is not only possible without any large investments, but also prosperous with savings on both energy and money within reach.To conclude, planning of production and supply ch...

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“…The MCMIGP model, based on the production system of sugar, alcohol, molasses and derivatives, is solved using the modelling language GAMs 23.6.2 and the optimisation solver CPLEX 12.2.1 and has helped to make better decisions on production planning, distribution, and energy cogeneration for the mill. A mixed integer programming model is also proposed in Paiva and Morabito (2009) so as to help decision makers to plan industrial processes of sugar, ethanol and molasses production in a Brazilian mill. In Kawamura et al (2006), the authors have applied a multi-period linear programming (LP) model to reduce total transportation and storage costs in Sugarcane and Ethanol Producers' Cooperative, which consists of 34 sugar mills, in Brazil.…”

Section: Introductionmentioning

confidence: 99%

A two-stage stochastic programming optimisation for sugar-ethanol-electricity production from sugarcane: a case study of Mauritius

Adam

Dauhoo

Khoodaruth

et al. 2016

IJMMNO

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Abstract:Sugarcane is an economically essential plant from which juice is extracted to produce raw sugar and molasses. The raw sugar can further be refined while the molasses can be fermented to manufacture ethanol. Other than cane juice, bagasse is another by-product of sugarcane which can be used to generate electricity and as a raw material to manufacture paper and chemicals. In this paper, we present a two-stage stochastic programming model to quantify the optimal amount of sugar, ethanol and electricity to be made from sugarcane so as to minimise total production cost. The model developed is based on variation of prices of sugar, ethanol and electricity and energy required to produce them. We establish the boundedness, convexity and existence of solution of the model. The latter is then applied to data available for Mauritius and solved using a genetic algorithm approach. We estimate the optimal amount of sugar, ethanol and electricity to be produced up to the year 2027. Finally, we analyse three different scenarios, namely decrease in land under sugarcane, increase/decrease in price of sugar and increase/decrease in price of ethanol.

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An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company

Cited by 54 publications

References 24 publications

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Planning production and supply chain in energy intensive process industries

A two-stage stochastic programming optimisation for sugar-ethanol-electricity production from sugarcane: a case study of Mauritius

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