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A MILP model for planning at operative level in a meat packing plant - - PowerPoint PPT Presentation

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3 RD INTERNATIONAL WORKSHOP ON FOOD SUPPLY CHAIN A MILP model for planning at operative level in a meat packing plant By Sara Vernica Rodrguez Snchez University Autonomous of Nuevo Leon, Mexico Vctor Albornoz, Matas Gripe University

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By Sara Verónica Rodríguez Sánchez

University Autonomous of Nuevo Leon, Mexico

San Francisco, CA, November 4th, 2014

A MILP model for planning at operative level in a meat packing plant

3RD INTERNATIONAL WORKSHOP ON FOOD SUPPLY CHAIN

Víctor Albornoz, Matías Gripe

University Federico Santa María, Chile

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OUTLINE

  • Introduction about pork production
  • The pork supply chain structure
  • Planning production in a meat packing plant
  • Challenges and Opportunities
  • Conclusions
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IS MEAT PRODUCTION AN ISSUE?

In 2012, around 304 million tonnes of meat were produced worldwide. For 2014, FAO forecasts an increase to 311.8 million tonnes.

Year Population (Billions)

EXPECTATIONS OF WORLD POPULATION GROWTH ANUAL MEAT CONSUMPTION (PER CAPITA)

Kg Per capita USA Brazil China Word Developing countries Year

Nowadays we are approximate 7 thousand of millions of people

Source FAOSTAT

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WHAT WE NEED?

Mor More agricu ricultur ltural al lan land? d?

But the agricultural land is being devoted to build houses.

Bio-energy PR PRODU ODUCTIVITY CTIVITY

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TECHNOLOGY

Food and Agriculture Organization of the United Nations had point out TECHNOLOGY is the cornerstone to increase productivity.

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TECHNOLOGY

Here is where OR Researchers could play an important role

By developing Decision Support Systems

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TECHNOLOGY

My job is to provide tools to help you to get more money for your company with the resourses you already have.

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TECHNOLOGY

and moreover how to change your assets to be more productive

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PORK INDUSTRY EVOLUTION

FAMILIAR INDUSTRIAL

  • Pork is the most widely produced meat worldwide.
  • In many countries the number of pig farms is being reduced, while the herd size
  • f the remaining ones is increasing.
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CHANGES IN THE MANAGEMENT

2 to 5 animals per week 100 to 10000 animals per day Before Now

Size of Operations Decision Making is more complex

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  • Habits and practices are changing.

CONSUMER EVOLUTION

  • Quality.

EU REGULATIONS

  • Animal welfare.
  • Safety
  • Traceability
  • Sustainable
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ADOPTION OF CAPITAL INTENSIVE SYSTEMS

Climate control Automatic feeding systems Animal identification devices PDA Software

Pig production has been evolving towards a progressive concentration in larger and more specialized and efficient production units

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PORK SUPPLY CHAIN

The competition today is more between supply chains than individual firms.

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THE PSC MANAGEMENT

The chain manager now must make decisions on pig production agents considering the integration and coordination of the whole supply chain at different time horizons (Stadler, 2005).

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PORK PRODUCTION IN A MP PLANT

  • Kjærsgaard, N., 2008. Optimization of the Raw Material Use at Danish Slaughterhouses. Technical University of
  • Denmark. PhD Thesis.

FARMS SLAUGHTER AREA PACKING AREA

Lairage area Equalization room Processing Lines

A meat packing plant is the facility where the processing and packing

  • f the meat is done.
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DIFERENT PORK PRODUCTS

  • Multiproduct.
  • Process planning for product disassembly.
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DIFERRENT CUTTING PATTERNS

  • $ reward
  • $ cost
  • Set of products
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PROBLEM DESCRIPTION

Yield kg Cutting Patterns Demand kg The mayor difficulty is to balance the benefits of selling products from one part of the carcass when there are no demand from other parts or the carcass Carcass Demand

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MATHEMATICAL FORMULATION

  • OBJECTIVE FUNCTION is oriented to maximize the net profit.
  • The net profit is obtained through the difference between the incomes

from selling the products yielded by the cutting patterns, minus the

  • perational costs incurred. These operational costs involve inventory,

freezing costs, unsatisfied- demand penalties and labor costs to perform the cutting-patterns.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Bounds for carcasses to be processed. The number of carcasses to

process in each period needs to be bounded (upper and lower limits), given by the animal availability from suppliers according to different types of carcasses.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Cutting patterns balance. Carcasses are partitioned into sections and for

each section a different cutting pattern can be applied. This constraint ensures a balance between cutting patterns and the number

  • f carcasses processed. Equality is forced because the infeasibility to let

unprocessed raw material, due to perishability issues.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Cutting pattern yield. Different cutting patterns can be applied on the

carcass to make different products. A cutting pattern is therefore defined by a combination of a set of products and their respective yields. The following constraint calculates the total amount of product i, retrieved from all the cutting patterns applied in each period.

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MATHEMATICAL FORMULATION

CONSTRAINTS It is recognized that the pork industry works with perishable products subject to spoilage. In order to extend the life of the product, it undergoes to a freezing process. Thereby, a product can be sold in two presentations, fresh and frozen. A product is considered fresh if it is sold within 4 days after

  • elaboration. On the other hand, frozen products can be kept this way for

almost 2 years. However, the profit of selling frozen products decays considerably.

  • Fresh and frozen balance. This constraint determines the amount of

product to be frozen and the ones to keep fresh to be sold in the next periods.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Fresh product to be sold. As mentioned, fresh products are not allowed

to be kept for more than 4 days. Constraint 7 calculates the total amount of fresh products that can be sold in a period t, but were produced in previous periods.

  • Frozen product to be sold. Fresh products need to stay at least 2 days

in the freezing tunnel, to be considered frozen. The following constraint balance the inventory of frozen products at each period.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Demand of frozen products. Ensures that the requested level of each

frozen product is addressed, allowing the existence of unsatisfied-demand in the case the raw materials are insufficient.

  • Demand of fresh products. Ensures that the requested level of each

fresh product is addressed, allowing the existence of unsatisfied-demand in the case the raw materials are insufficient.

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MATHEMATICAL FORMULATION

CONSTRAINTS

  • Labor capacity.
  • Wharehouse capacity for fresh products.
  • Wharehouse capacity for frozen products.
  • Freezing tunnel capacity.
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CASE OF STUDY

We are working with data from different companies

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SUCCESS CASE

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OUTPUT

  • Moreover. The model gives the ability to ask ‘what if? questions such as
  • What is the effect on profit as further cutting patterns and extra products

become available?

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OPTIMAL MARKETING OF PIGS

  • The fattened pig ready for slaughtering is the output from several productive

and reproductive biological processes .

Fattening farm

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OPTIMAL MARKETING OF PIGS

  • The fattening is the last biological process before the pig is marketed as a live

animal, and sent it to the slaughterhouse to be processed as a meat.

Fattening farm

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OPTIMAL MARKETING OF PIGS

  • As pigs reach marketable weights near the end of the finishing phase, a pork

producer must devise a marketing strategy to determine when to sell pigs, which and how many pigs to sell.

Sl Slaughterhouse se

Rearing farms Fattening farm

11 a a 17 17 wee eeks ks Marketable le weigh ight 15 15 to to 35 kg kg

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OPTIMAL MARKETING OF PIGS

  • The major complexity faced in this problem is in managing the biological

variance; owing to it, pigs reach optimal conditions for slaughter at different times of the fattening period.

Sl Slaughterhouse se

Rearing farms Fattening farm

11 a a 17 17 wee eeks ks Marketable le weigh ight 15 15 to to 35 kg kg

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CHALLENGES AND OPPORTUNITIES

FATTENING FARM 6 FATTENING FARM 1 FATTENING FARM 2 FATTENING FARM 3 MEAT PACKING PLANT FATTENING FARM 4 FATTENING FARM 5

SLAUGHTERING/PROCESSING

Figure 1. The structure of the pork supply chain considering two levels, fattening farms and the meat packing plant.

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CONCLUSIONS

  • Adoption of OR methods in the PSC progress slowly like in other industries of

the primary sector. It may benefit of the development of user-friendly DSSs in a narrow collaboration with the industry.

  • Traditionally, judgement based on experience had been the basis for the

production planning. However recent changes have driven to a more complex business planning enviroment, and thereby made the development of more formal planning methos necessary.

  • The new competitive strategy in pig farming is no longer based on individual

farms units, but rather integrated into a supply chain.

  • Analitical

tools has the potential to increase profits through a better undertanding and new insights for their marketing strategy and production

  • peration.
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THANK YOU!

If you have any comment, suggestion, …please write to: srodriguez090444@gmail.com

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REFERENCES

Rodríguez, S.V., Plà, L.M., Faulin, J., New Oportunities in Operations Research to improve pork supply chain efficiency, 2014. Annals of Operations Research, 219, 5- 13. Pla, L.M., Rodriguez, S.V., Rebillas, V. 2013. A mixed integer linear programming model for optimal delivery of fattened pigs to the abattoir. Journal of Applied Operations Research, 5(4), 164-175.

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REFERENCES

  • Plà, L.M., (1997) Review of mathematical models for sow

herd management. Livestock Science, 106, 107-119. Rodriguez, S. V., Albornoz, V., & Plà, L. M. (2009). A two stage stochastic programming model for scheduling replacements in sow farms. TOP, 17(1), 171–179.

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THE SOW REPLACEMENT PROBLEM

  • The piglet production capacity
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THE SOW REPLACEMENT PROBLEM

  • The piglet production capacity
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SET AND INDEXES

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SET AND INDEXES

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REFERENCE

  • Whitaker, D. and Cammell, S. 1990. A partitioned Cutting-Stock Problem

Applied in the meat industry. Journal of Operations Research Society, 41(9), 801-807.

  • Bixby, A., Dows, B., and Self, Mike. 2006. A Schedule and Capable to

promise application for Swift & Company. Interfaces. 36(1), 69-86.

  • Reynisdóttir, K., 2012. Linear optimization model that maximizes the value of

pork products. MSc Thesis. Reykjavík University.

  • Kjærsgaard, N., 2008. Optimization of the Raw Material Use at Danish
  • Slaughterhouses. Technical University of Denmark. PhD Thesis.
  • Wikborg, U., N., 2008. Online meat cutting operations. MSc Thesis. Norwegian

University of Science and Technology.

  • Rodriguez, S., Albornoz, V., Gripe, M., Gonzalez, M., 2014. A mixed integer

linear program for planning and scheduling the meat production in a pork supply chain, ISCO_2014.