Coordination Strategy for Production Planning in Supply Chain Based on Lagrange Relaxation Algorithm

2006 ◽  
Author(s):  
Cuihua Zhang ◽  
Guangshu Chang ◽  
Yan Fan ◽  
Haibin Yu
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Lagrange Relaxation ◽  
Relaxation Algorithm ◽  
Coordination Strategy

scholarly journals A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1257
Author(s):  
Xiaoyong Gao ◽  
Yue Zhao ◽  
Yuhong Wang ◽  
Xin Zuo ◽  
Tao Chen
Keyword(s):  
Production Planning ◽  
Large Scale ◽  
Oil Production ◽  
Original Model ◽  
Decomposition Algorithm ◽  
Scale Model ◽  
Lagrange Relaxation ◽  
Planning Optimization ◽  
Offshore Oil Production ◽  
Offshore Oil

In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale model is decomposed into several single-batch subproblems and solved simultaneously by commercial solvers. Computational results show that the proposed method can reduce the solving time up to 43% or even more. Meanwhile, the planning results are close to those obtained by the original model. Moreover, the larger the problem size, the better the proposed LR algorithm is than the original model.

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

Modeling Closed Loop Supply Chain Systems

2012 ◽  
pp. 313-342
Author(s):  
Roberto Poles
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Supply Chains ◽  
Inventory Control ◽  
Production Planning ◽  
Closed Loop ◽  
Economic Benefits ◽  
Reverse Supply Chain ◽  
Closed Loop Supply Chains ◽  
Operational Processes

In the past, many companies were concerned with managing activities primarily along the traditional supply chain to optimize operational processes and thereby economic benefits, without considering new economic or environmental opportunities in relation to the reverse supply chain and the use of used or reclaimed products. In contrast, companies are now showing increased interest in reverse logistics and closed loop supply chains (CLSCs) and their economic benefits and environmental impacts. In this chapter, our focus is the study of remanufacturing activity, which is one of the main recovery methods applied to closed loop supply chains. Specifically, the authors investigate and evaluate strategies for effective management of inventory control and production planning of a remanufacturing system. To pursue this objective, they model a production and inventory system for remanufacturing using the System Dynamics (SD) simulation modeling approach. The authors primary interest is in the returns process of such a system. Case studies will be referred to in this chapter to support some of the findings and to further validate the developed model.

Application of Triplet Notation and Dynamic Programming to Single-Line, Multi-Product Dairy Production Scheduling

2012 ◽  
pp. 816-827
Author(s):  
Virginia M. Miori ◽  
Brian Segulin
Keyword(s):  
Dynamic Programming ◽  
Supply Chain ◽  
Production Scheduling ◽  
Production Line ◽  
Dairy Industry ◽  
Dairy Production ◽  
Final Solution ◽  
Lagrange Relaxation ◽  
Optimal Methods ◽  
Single Production

The application of optimal methods for production scheduling in the dairy industry has been limited. Within supply chain terminology, dairy production was generally considered a push process but with advancements in automation, the industry is slowly transforming to a pull process. In this paper, the authors present triplet notation applied to the production scheduling of a single production line used for milk, juice, and carnival drinks. Once production and cleaning cycles are characterized as triplets, the problem is formulated. Lagrange relaxation is applied and the final solution is generated using dynamic programming.

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