System and method for automatic parameter tuning of campaign planning with hierarchical linear programming objectives
A system and method are disclosed for campaign planning and include modeling the use of campaign operations and campaignable resources of a supply chain network including a production line to produce products using campaign operations and campaignable resources as campaign planning problems, defining an evaluation function comprising a weighted sum of features evaluated from the campaign planning problem, initializing weights to build a consumption profile and evaluation function, determining fitness values that indicate a level of variability, evaluating reward values based on the fitness values, selecting a sub-sample of the top fitness values having the best associated objective function, repeating the generating, the evaluating and the selecting steps to adjust the weights until a stopping criterion is met indicating an optimal solution has been reached, and determining a campaign plan for the use of the campaign operations and campaignable resource.
1. A system of campaign planning, comprising:
a computer, comprising a processor and a memory, configured to:
model a use of the one or more campaign operations and one or more campaignable resources of a supply chain network comprising a production line configured to produce one or more products using one or more campaign operations and one or more campaignable resources as one or more campaign planning problems;
define an evaluation function comprising a weighted sum of features evaluated from the campaign planning problem;
initialize weights to build a consumption profile and evaluation function;
determine fitness values, wherein the fitness values indicate a level of variability;
evaluate reward values based on the fitness values;
select a sub-sample of the top fitness values having the best associated objective function;
repeat the determine, the evaluate and the select steps to adjust the weights until a stopping criterion is met indicating an optimal solution has been reached; and
determine a campaign plan for the use of the one or more campaign operations and one or more campaignable resource.
2. The system of claim 1 , wherein the one or more campaign planning problems are formulated as a sequential decision problem with sequential decision problem components including value function, reward, state space, and action set.
3. The system of claim 1 , wherein the one or more campaignable resources include resources for one or more manufacturing setups to produce the one or more products for the campaign plan.
4. The system of claim 3 , wherein the one or more manufacturing setups are sequence dependent.
5. The system of claim 1 , wherein the campaign plan balances KPI's comprising customer service level, resource utilization and inventory level.
6. The system of claim 5 , wherein the campaign plan determines when to change from one campaign to another and the duration of each campaign while balancing the KPI's.
7. The system of claim 1 , wherein the modeling of the use of the one or more campaign operations and the one or more campaignable resources of a supply chain network further comprise modelling a flow of materials through the supply chain network as a network of nodes and edges.
8. A computer-implemented method of campaign planning, comprising:
modeling, by a computer, the use of the one or more campaign operations and one or more campaignable resources of a supply chain network comprising a production line configured to produce one or more products using one or more campaign operations and one or more campaignable resources as one or more campaign planning problems;
defining an evaluation function comprising a weighted sum of features evaluated from the campaign planning problem;
initializing weights to build a consumption profile and evaluation function;
determining fitness values, wherein the fitness values indicate a level of variability;
evaluating reward values based on the fitness values;
selecting a sub-sample of the top fitness values having the best associated objective function;
repeating the generating, the evaluating and the selecting steps to adjust the weights until a stopping criterion is met indicating an optimal solution has been reached; and
determining, by the computer, a campaign plan for the use of the one or more campaign operations and one or more campaignable resource.
9. The computer-implemented method of claim 8 , wherein the one or more campaign planning problems are formulated as a sequential decision problem with sequential decision problem components including value function, reward, state space, and action set.
10. The computer-implemented method of claim 8 , wherein the one or more campaignable resources include resources for one or more manufacturing setups to produce the one or more products for the campaign plan.
11. The computer-implemented method of claim 10 , wherein the one or more manufacturing setups are sequence dependent.
12. The computer-implemented method of claim 8 , wherein the campaign plan balances KPI's comprising customer service level, resource utilization and inventory level.
13. The computer-implemented method of claim 12 , wherein the campaign plan determines when to change from one campaign to another and the duration of each campaign while balancing the KPI's.
14. The computer-implemented method of claim 8 , wherein the modeling of the use of the one or more campaign operations and the one or more campaignable resources of a supply chain network further comprise modelling a flow of materials through the supply chain network as a network of nodes and edges.
15. A non-transitory computer-readable medium embodied with software for campaign planning, the software when executed:
models the use of the one or more campaign operations and one or more campaignable resources of a supply chain network comprising a production line configured to produce one or more products using one or more campaign operations and one or more campaignable resources as one or more campaign planning problems;
defines an evaluation function comprising a weighted sum of features evaluated from the campaign planning problem;
initializes weights to build a consumption profile and evaluation function;
determine fitness values, wherein the fitness values indicate a level of variability;
evaluates reward values based on the fitness values;
selects a sub-sample of the top fitness values having the best associated objective function;
repeats the generates, the evaluates and the selects steps to adjust the weights until a stopping criterion is met indicating an optimal solution has been reached; and
determines a campaign plan for the use of the one or more campaign operations and one or more campaignable resource.
16. The non-transitory computer-readable medium of claim 15 , wherein the one or more campaign planning problems are formulated as a sequential decision problem with sequential decision problem components including value function, reward, state space, and action set.
17. The non-transitory computer-readable medium of claim 15 , wherein the one or more campaignable resources include resources for one or more manufacturing setups to produce the one or more products for the campaign plan.
18. The non-transitory computer-readable medium of claim 17 , wherein the one or more manufacturing setups are sequence dependent.
19. The non-transitory computer-readable medium of claim 15 , wherein the campaign plan balances KPI's comprising customer service level, resource utilization and inventory level.
20. The non-transitory computer-readable medium of claim 19 , wherein the campaign plan determines when to change from one campaign to another and the duration of each campaign while balancing the KPI's.