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Towards Integrated Production and Distribution Management

Towards Integrated Production and Distribution Management. Carlos A. Méndez www.intec.unl.edu.ar/capse. Center for Advanced Process Systems Engineering Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Universidad Nacional de Litoral (UNL) – CONICET

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Towards Integrated Production and Distribution Management

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  1. Towards Integrated Production and Distribution Management Carlos A. Méndez www.intec.unl.edu.ar/capse Center for AdvancedProcess Systems Engineering Institutode DesarrolloTecnológicopara la IndustriaQuímica (INTEC) Universidad Nacional de Litoral (UNL) – CONICET cmendez@intec.unl.edu.ar

  2. OUTLINE • Motivation • Problem Definition • The MILP Mathematical Model • Example • Conclusions

  3. MOTIVATION • In the current context of a global and very competitive economy, multipleproduction and distribution activities must be properly coordinated in order to satisfy strict market requirements at the minimum cost. • From the operational perspective, both problems have been traditionally faced separately and independently from any Supply Chain Environment. • The proper synchronization of these complex systems remains as an open and challenging area for research.

  4. MOTIVATION VEHICLE ROUTING AND SCHEDULING PROBLEMS

  5. Manufacturing plant Distribution center Retailer/Customer Product flow Cross-docking site MOTIVATION ALTERNATIVE DISTRIBUTION NETWORK DESIGN OPTIONS a) Manufacturer storage and direct shipping b) Distributor storage and shipping via central distribution centre c) Cross-docking d) Hybrid infrastructure

  6. MOTIVATION • In multi-site supply chains, products are usually manufactured in one or more factories, moved to warehouses for intermediate storage, and subsequently shipped to retailers or final consumers.

  7. MOTIVATION The effective operation of complex production and distribution networks involves the management of activities performed in multiple factories, distribution centers (DCs), retailers and end users, which are usually geographical distributed in many different cities, countries and/or continents. • MILP-based Framework Approach to the Integrated Operational Planning of Multi-Echelon Multiproduct Production and Transportation Networks in Supply Chains

  8. PROBLEM DEFINITION • The short-term operational planning of multi-echelon production and distribution networks comprises: • Pure” source nodes (IS), usually manufacturing plants, where several types of products are made. In these locations, vehicles carry out only pickup operations, • Mixed nodes (IM), like DCs, where visiting vehicles can accomplish pickup and/or unloading operations, • Destination nodes (ID), like consumer zones, where visiting trucks just perform delivery operations.

  9. PROBLEM DEFINITION V2 C 1 Multiproduct batch plants IS C 15 C 2 W1 C 13 C 7 C 3 C 4 C 14 C 11 C 12 C 6 Customer requests C 5 W2 C 8 C 10 V4 C 9 V1 Fleet of vehicles V3 Stock of finished goods IM

  10. INTEGRATED FRAMEWORK • Novel MILP-based continuous time formulation • It provides a very detailed set of coordinated production and distribution schedules.

  11. MODEL DECISIONS • Batches to be Produced • Batch to Unit Assignment • Batch Sequencing and Timing • Customer to Vehicle Assignment • Vehicle Routing and Timing • Batch to Vehicle Assignment • Inventory to Vehicle Assignment

  12. PROBLEM GOAL • The problem goal is to meet all products demands minimizing: • The Total ProductionCost • The Total TransportationCost • Production costs include: • Setup costs, • Fixed and variable production cost • Transportation costs include: • Fixed expenses incurred by used vehicles, • Distance-based variable costs, mainly fuel costs.

  13. INTEGRATION CONSTRAINTS Assigntheinitial stock tovehicles Amount of initial stock allocatedtovehicles Initial stock Split batchesbetweenvehicles Amount of products of batchassignedtoeachvehicle Batchsize Binary Variable. Itsvalueis 1 ifbatch b isassignedtovehicle v Amount of productsloadedbyeveryvehicle

  14. INTEGRATION CONSTRAINTS Maximum volumetric and weight capacity in every trip Load Time Production Time Earliest start time of each vehicle route

  15. OBJECTIVE FUNCTION ProductionCosts TransportationCosts

  16. EXAMPLE P3 P1 P1 P3 P2 P3 P3 P1 P1 P2 P2

  17. ALTERNATIVE SCENARIOS All the examples were solved to global optimality with a modest computational effort using GAMS 23.5.2 with solver CPLEX 12 in a PC Core 4 Quad (8 threads).

  18. CONCLUSIONS • An integrated MILP-based framework for production and distribution scheduling in supply chains has been proposed. • The formulation developed can be applied to solve complex problems in a reasonable computational time. • Each solution provides a very detailed set of coordinated production and distribution schedules to meet all products demands at minimum total production and transportation cost.

  19. THANKS FOR YOUR ATTENTIONQUESTIONS ? Center for AdvancedProcess Systems Engineering Institutode DesarrolloTecnológicopara la IndustriaQuímica (INTEC) Universidad Nacional de Litoral (UNL) – CONICET cmendez@intec.unl.edu.ar

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