Systems, methods and apparatus for implementing hybrid meta-heuristic inventory optimization based on production schedule and asset routing

• US 8,655,705 B2
• Filed: 01/13/2010
• Issued: 02/18/2014
• Est. Priority Date: 01/13/2010
• Status: Active Grant

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Riepshoff, Robert D., Klinefelter, Phillip
• Primary Examiner(s): Jasmin, Lynda
• Assistant Examiner(s): Rotaru, Octavian
• Application Number:
  US12/686,537
• Publication Number:
  US 20110173042A1
• Time in Patent Office: 1,497 Days
• Field of Search: 705/7.26, 705/7.11, 705/7.12, 705/7.22, 705/7.25, 705/348
• US Class Current: 705/7.26
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem into a production center schedule and an asset routing problem, which will be tackled one after the other. The decomposed supply plan problem is solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.

Claims

1. A computer implemented method for developing an optimized supply plan of scheduled jobs and assets in a mission critical supply chain network of an operations theater site comprising:

inputting, with a processor, operational demands and a priority level for each of the operational demands, the input operational demands being selected from a group consisting of;

mission demand, supply demand, maintenance demand, event exception demand and user defined demand of the operations'"'"' theater site that includes at least one of;

squadrons, bases, repair facilities, supply warehouses, and factories;

dividing, with the processor, the input operational demands into jobs;

performing, with the processor, an initial jobs assessment and demand prioritization based on an assignment performed by a genetic algorithm that optimizes supply plans based on dynamic changes of the input operational demands and the priority level for each of the input operational demands;

scheduling, with the processor, jobs with a same demand prioritization based on the performed initial jobs assessment and demand prioritization;

forwarding, with the processor, the scheduled jobs to a jobs schedule variance processing;

determining, by the processor, and redirecting unscheduled jobs from the scheduled jobs with same demand;

forwarding, with the processor, the determined and redirected unscheduled jobs to the jobs schedule variance processing;

assigning, by the processor, the scheduled jobs with same demand and the determined and redirected unscheduled jobs to the assets;

forwarding, with the processor, the assigned scheduled jobs to an assets schedule variance and feasibility constraints processing, the assigning using a workflow for scheduling the assets comprising a job start load time sorting, an incremental assigning of the assets until a last asset availability, a determination of job assignment achievability after previous task completion by the asset, a decision constraint, and an overall number of other jobs to be assigned;

determining, by the processor, undelivered jobs from the determined and redirected unscheduled jobs to the assets;

assigning, by the processor, the undelivered determined and redirected unscheduled jobs to other assets based on a fitness evaluation;

forwarding, with the processor, the assigned undelivered determined and redirected unscheduled jobs to the assets schedule variance and feasibility constraints processing,

wherein the fitness evaluation comprises;

generating chromosomes populations of possible solutions for each of the assigned undelivered determined and redirected unscheduled jobs,

selecting fittest chromosomes based on a prevailing ranking of the chromosomes according to an objective selected from a group consisting of;

maximizing an operational availability and minimizing a logistic footprint,

selecting a predetermined number of the fittest chromosomes,

pairing the selected predetermined number of the fittest chromosomes with each other,

interchanging genes in the pairs of the selected predetermined number of the fittest chromosomes beyond a crossover point of a first paired fittest chromosome and before the crossover point of a second paired fittest chromosome,

mutating the interchanged predetermined number of chromosomes to create new chromosomes, and

forwarding the new chromosomes to a simulator for performing feasibility and impact analysis on the mutated interchanges; and

repeating, with the processor, the fitness evaluation until the optimized supply plan of the scheduled jobs and assets is developed in the mission critical supply chain network of the operations'"'"' theater site.

2. A system for developing an optimized supply plan of scheduled jobs and assets in a mission critical supply chain network of an operations'"'"' theater site, the system comprising:

one or more processor components programmed to;

input operational demands and a priority level for each of the input operational demands, the input operational demands being selected from a group consisting of;

mission demand, supply demand, maintenance demand, event exception demand and user defined demand of the operations'"'"' theater site that includes at least one of;

squadrons, bases, repair facilities, supply warehouses, and factories;

divide the input operational demands into jobs;

perform an initial jobs assessment and demand prioritization based on an assignment performed by a genetic algorithm that optimizes supply plans based on dynamic changes of the input operational demands and the priority level for each of the input operational demands;

schedule jobs with same demand based on the performed initial jobs assessment and demand prioritization;

forward the scheduled jobs to a jobs schedule variance processing;

determine and redirect unscheduled jobs from the scheduled jobs with same demand;

forwarding the determined and redirected unscheduled jobs to the jobs schedule variance processing;

assign the scheduled jobs with same demand and the determined and redirected unscheduled jobs to the assets;

forward the assigned scheduled jobs to an assets schedule variance and feasibility constraints processing, the assigning using a workflow for scheduling the assets comprising a job start load time sorting, an incremental assigning of the assets until a last asset availability, a determination of job assignment achievability after previous task completion by the asset, a decision constraint, and an overall number of other jobs to be assigned;

determine undelivered jobs from the determined and redirected unscheduled jobs to the assets;

assign the undelivered determined and redirected unscheduled jobs to other assets based on a fitness evaluation;

forward the assigned undelivered determined and redirected unscheduled jobs to the assets schedule variance and feasibility constraints processing,

wherein the fitness evaluation comprises;

generating chromosomes populations of possible solutions for each of the assigned undelivered determined and redirected unscheduled jobs,

selecting fittest chromosomes based on a prevailing ranking of the chromosomes according to an objective selected from a group consisting of;

maximizing an operational availability and minimizing a logistic footprint,

selecting a predetermined number of the fittest chromosomes,

pairing the selected predetermined number of the fittest chromosomes with each other,

interchanging genes in the pairs of the selected predetermined number of the fittest chromosomes beyond a crossover point of a first paired fittest chromosome and before the crossover point of a second paired fittest chromosome,

mutating the interchanged predetermined number of chromosomes to create new chromosomes, and

forwarding the new chromosomes to a simulator for performing feasibility and impact analysis on the mutated interchanges; and

repeat the fitness evaluation until the optimized supply plan of the scheduled jobs and assets is developed in the mission critical supply chain network of the operations'"'"' theater site.

3. A non-transitory computer readable storage medium for developing an optimized supply plan of scheduled jobs and assets in a mission critical supply chain network of an operations'"'"' theater site, on which is recorded computer executable instructions that, when executed by a processor, cause the processor to execute the steps of a method comprising:

inputting operational demands and a priority level for each of the input operational demands, the input operational demands being selected from a group consisting of;

mission demand, supply demand, maintenance demand, event exception demand and user defined demand of the operations'"'"' theater site that includes at least one of;

squadrons, bases, repair facilities, supply warehouses, and factories;

dividing the input operational demands into jobs;

performing an initial jobs assessment and demand prioritization based on an assignment performed by a genetic algorithm that optimizes supply plans based on dynamic changes of the input operational demands and the priority level for each of the input operational demands;

scheduling jobs with a same demand prioritization based on the performed initial jobs assessment and demand prioritization;

forwarding the scheduled jobs to a jobs schedule variance processing;

determining and redirecting unscheduled jobs from the scheduled jobs with same demand;

forwarding the determined and redirected unscheduled jobs to the jobs schedule variance processing;

assigning the scheduled jobs with same demand and the determined and redirected unscheduled jobs to the assets;

forwarding the assigned scheduled jobs to an assets schedule variance and feasibility constraints processing, the assigning using a workflow for scheduling the assets comprising a job start load time sorting, an incremental assigning of the assets until a last asset availability, a determination of job assignment achievability after previous task completion by the asset, a decision constraint, and an overall number of other jobs to be assigned;

determining undelivered jobs from the determined and redirected unscheduled jobs to the assets;

assigning the undelivered determined and redirected unscheduled jobs to other assets based on a fitness evaluation;

forwarding the assigned undelivered determined and redirected unscheduled jobs to the assets schedule variance and feasibility constraints processing,

wherein the fitness evaluation comprises;

generating chromosomes populations of possible solutions for each of the assigned undelivered determined and redirected unscheduled jobs,

selecting fittest chromosomes based on a prevailing ranking of the chromosomes according to an objective selected from a group consisting of;

maximizing an operational availability and minimizing a logistic footprint,

selecting a predetermined number of the fittest chromosomes,

pairing the selected predetermined number of the fittest chromosomes with each other,

interchanging genes in the pairs of the selected predetermined number of the fittest chromosomes beyond a crossover point of a first paired fittest chromosome and before the crossover point of a second paired fittest chromosome,

mutating the interchanged predetermined number of chromosomes to create new chromosomes, and

forwarding the new chromosomes to a simulator for performing feasibility and impact analysis on the mutated interchanges; and

repeating the fitness evaluation until the optimized supply plan of the scheduled jobs and assets is developed in the mission critical supply chain network of the operations'"'"' theater site including at least one of;

squadrons, bases, repair facilities, supply warehouses, and factories.