alberto m. lópez simulation-based optimization for determining the resources of a distribution...

Alberto M. LópezICS3012 Seminario de Ingeniería de Sistema

1

Simulation-based optimization for determining the resources of a

distribution center

Magister en IngenieríaÁrea de especialización: Ingeniería Industrial y de Sistemas

First Presentation - September 2013

Pontificia Universidad Católica de Chile

Alumno: Alberto Manuel López

Alberto M. López 2Alberto M. López

AGENDA

I. RESEARCH PROBLEM

II. LITERATURE REVIEW

III. RESEARCH METHODOLOGY

IV. EXPECTED RESULTS

V. ACADEMIC CONTRIBUTIONS

First Presentation - September 2013ICS3012 Seminario de Ingeniería de Sistema


RESEARCH PROBLEM

General problem:

Determine the optimal resource level (minimum cost) to operate a distribution center

Elements of the problem:

- 1 distribution center (particular operation)

- Several demand centers (stochastic demand with specific

characteristics)

- Performance measures (constraints)

- Resources (constant for the considered period)

- Time horizon T



RESEARCH PROBLEM

Determine the optimal truck fleet of a distribution center (minimize cost), to achieve a target level of a performance measure of the system (demand fulfillment percentage).

Characteristics of the problem:

– There are sources of uncertainty and variability

– Demand (stochastic and seasonal):

• For each month, we know the average and standard deviation of daily trips

• Correlation between the demand of a day and the following

• Backorders are allowed

• Divided into 3 zones

– The truck fleet interacts with other resources, which also influence the demand

fulfillment

– Trucks can do more than a daily trip

– Trucks are rented for each monthly period

Problem extension:- Evaluate if it is possible to improve the problem solution by making variable part

of the fleet during the period



RESEARCH PROBLEM

• Which are the steps to determine the amount of each resource to achieve specific target levels of the performance measures?

• What considerations should be taken into account to get a valid and applicable model of the system operation?

• Which is the optimal number of trucks (resource) required to achieve the target of demand fulfillment?

• Is it possible to improve the solution having the possibility of renting trucks per day? What policy should be applied?

• Which is the influence on the decision of the costs of renting trucks per day?

• How does the amount of other resources impacts the optimal fleet size?

• How sensitive are the performance measures to the amounts of each resource?

Research Questions



RESEARCH PROBLEM

• In practice, resources are allocated through deterministic estimates, generating inefficiencies and an increase in costs.

• There is a need to develop a method to efficiently determine the resources needed in a real logistic system.

Research motivation



RESEARCH PROBLEM

• General:– Develop a method that combines simulation and optimization to determine

the optimal amount of resources of a distribution center.

– Apply it in a real case.

• Specific:– Define how to build a simulation model to measure the performance of a

distribution center for different levels of resources.

– Build an optimization model to get the optimal levels of resources, given a set of performance constraints.

– Integrate both models.

– Apply the developed method in a case study (optimal truck fleet of a beverage distribution center).

– From the model, obtain policies that are applicable in practice.

Research objectives



LITERATURE REVIEW

Main topics

• Supply chain management

• Warehouse design, operation and performance evaluation

• Fleet sizing

• Simulation

• Optimization



SOME KEY BIBLIOGRAPHIC REFERENCES

Surveys

• Longo, F. (2011). Supply Chain Management Based on Modeling & Simulation: State of the Art and Application Examples in Inventory and Warehouse Management. InTech, Vienna, 93-144.

• Gu, J., Goetschalckx, M., & McGinnis, L. F. (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research, 203(3), 539-549.

• Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1-21.

• Rouwenhorst, B., Reuter, B., Stockrahm, V., Van Houtum, G., Mantel, R., & Zijm, W. (2000). Warehouse design and control: Framework and literature review. European Journal of Operational Research, 122(3), 515-533.

Supply Chain

• Goetschalckx, M. (2011). Supply Chain Engineering (Vol. 161): Springer.

• Simchi-Levi, D., Simchi-Levi, E., & Kaminsky, P. (2008). Designing and managing the supply chain: Concepts, strategies, and case studies: McGraw-Hill United-States.

• Bartholdi, J. & Hackman, S. (2011). Warehouse & Distribution Science. Release 0.95, The Logistics Institute, Georgia Tech, GA. http://www.warehouse--‐science.com/




Simulation and Optimization (Warehouse)

• Manivannan, M. S. (1998). Simulation of logistics and transportation systems. In J. Banks (Ed.), Handbook of simulation: Principles, methodology, advances, applications, and practice (pp. 571-604): John Wiley & Sons.

• Castro, M., Faundes, J., & Halcartegaray, P. (2013). Informe final. Simulación del Centro de Distribución Modelo. Transportes CCU Ltda.: Simula UC. Pontificia Universidad Católica de Chile.

• Hou, S. (2013). Distribution Center Logistics Optimization Based on Simulation.

• Correa Espinal, A. A. (2011). WMS y Simulación discreta en la operación de un centro de distribución. Paper presented at the Décima Conferencia Iberoamericana en Sistemas, Cibernética e Informática, Estados Unidos.

• Gómez, R. A., & Correa, A. A. (2010). Métodos cuantitativos utilizados en el diseño de la gestión de almacenes y centros de distribución. Revista Avances en Sistemas e Informática, 7(3), 109-117.

• Gagliardi, J. P., Renaud, J., & Ruiz, A. (2007). A simulation model to improve warehouse operations. Paper presented at the Proceedings of the 39th conference on Winter simulation.

• Narciso Farias, M. E., Piera i Eroles, M. A., & Figueras, J. (2005). Optimización de sistemas logísticos mediante simulación: una metodología basada en redes de petri coloreadas: Revista Iberoamericana de Automática e Informática Industrial.

• Zhou, M., Setavoraphan, K., & Chen, Z. (2005). Conceptual simulation modeling of warehousing operations. Paper presented at the Proceedings of the 37th conference on Winter simulation.

• Takakuwa, S., Takizawa, H., Ito, K., & Hiraoka, S. (2000). Simulation of distribution systems: simulation and analysis of non-automated distribution warehouses. Paper presented at the Proceedings of the 32nd conference on Winter simulation.




Fleet Sizing

• Stojanović, Nikoličić, S., & Miličić, M. (2011). Transport fleet sizing by using make and buy decision-making (Vol. 56): Economic Annals.

• Wu, P., Hartman, J. C., & Wilson, G. R. (2005). An integrated model and solution approach for fleet sizing with heterogeneous assets. Transportation Science, 39(1), 87-103.

• M. Singer, P. D. a. S. J. (2002). Fleet Configuration Subject to Stochastic Demand: An Application in the Distribution ofLiquefied Petroleum Gas (Vol. 53, pp. 961-971): The Journal of the Operational Research Society.

Simulation and Optimization (Methodology)

• Kelton, W. D., & Law, A. M. (2000). Simulation modeling and analysis: McGraw Hill Boston, MA.

• Law, A. M. (2008). How to build valid and credible simulation models. Paper presented at the Proceedings of the 40th Conference on Winter Simulation.

• Law, A. M., & McComas, M. G. (2002). Simulation-based optimization. Paper presented at the Proceedings of the 34th conference on Winter simulation.

• Joines, J. A., & Roberts, S. D. (2012). Simulation modeling with SIMIO: a workbook (2nd ed.): Simio LLC.



RESEARCH METHODOLOGY

Quantitative methods

• Discrete event simulation:– Complex real problems with uncertainty

– Answer what-if questions

– Evaluate policies

– Flexible modeling

• Optimization:– Optimal solutions




Classification (Operations research models)

• Symbolic or mathematical

• Quantitative

• Customized

• Probabilistic

• Descriptive - Normative

• Static - Dynamic

• Simulation-based




• Problem formulation

• Data and information collection

• Definition of the conceptual model

• Validation of the conceptual model

• Development of simulation computational model

• Verification and validation of the computational model

• Optimization model development

• Experiments design, implementation and analysis

• Results documentation and presentation



EXPECTED RESULTS

In general terms:

• Establish a methodology to determine the optimal amount for a given resource in the operation of a distribution center.

(R.Q. 1 & 2)

For the case study:

• Obtain policies that can be applied in practice and reduce operational costs.

(R.Q. 3 & 4)

• Understand the system behavior.(R.Q. 5, 6 & 7)



ACADEMIC CONTRIBUTIONS

Bringing practical tools to companies to improve decision-making

“... it is our belief that more case studies and computational tools for warehouse design and operation will help to bridge the significant gap between academic research and practical application, and therefore, represent a key need for the future.”

(Gu, Goetschalckx & McGinnis, 2010)

“... gap between the assumption-restricted models in research publications and the complex reality of most warehouses. There is a significant need for more industrial case studies, which will assist the warehouse research community in better understanding the real issues in warehouse design. In turn, research results that have been tested on more realistic data sets will have a more substantial impact on practice.”

(Gu, Goetschalckx & McGinnis, 2010)



Thank you for your attention

Questions? Comments?


alberto m. lópez simulation-based optimization for determining the resources of a distribution...

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