COVER PAGE
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Program Name:
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Title of Idea:
MTConnect Challenge
Automated predictive analytics for production scheduling by a novel
simulation optimization
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Respondent Name: George Mejtsky
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Organization (if applicable): n/a
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Phone Number: (610) 594-0558
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Email address: george.mejtsky@yahoo.com
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Date of Proposal: April 28, 2013
SECTION I: ABSTRACT
In manufacturing intelligence, predictive analytics is used for planning and operations
management, particularly for production scheduling.
In production scheduling, simulation with heuristic rules and what-if analysis are used to run
several scenarios generating several results (schedules). This process is highly labor-intensive
and time-consuming, and therefore, a prime candidate for automation. Our idea is to automate
this process by merging simulation with an approximate branch-and-bound optimization
algorithm.
Such metaheuristic simulation optimization, more described and tested in a referenced paper of
this proposal, can be used for solving combinatorial problems like production scheduling, supply
chain optimization, resource allocation, routing of war robots, scheduling military operations, or
air traffic management.
In production scheduling, direct benefits of the proposed automated predictive analytics are:
higher quality of schedules (schedules are shorter), the schedules are generated faster, and
savings of labor-intensive and time-consuming scheduling activities. These direct benefits lead to
other manufacturing benefits like increased throughput, maximized utilization of resources
(machines, people, tools, etc.), shorter lead times, improved customer satisfaction (meeting due
dates, on-time delivery), improved lean manufacturing by reducing waste (waiting time, setup
time, resource idleness). Therefore, production cost is reduced, profitability is increased without
any capital investment, and sustainable manufacturing is supported.
SECTION II: PROPOSED IDEA
The goal
The goal of this innovative idea is to improve the predictive capability of analytics in
manufacturing intelligence. The proposed software will automate currently “manual” what-if
analysis in production scheduling. This automation is based on merging simulation with an
approximate branch-and-bound optimization method at the expense of heuristic rules.
The idea description
Currently in manufacturing intelligence, predictive analytics is used in several places, such as
production scheduling/optimization or verification/simulation. In such places, usually simulation
with heuristic rules and what-if analysis/scenarios are used to develop production schedules or to
evaluate/verify plans, processes, or procedures.
For example in production scheduling, a typical what-if analysis is carried out by a person,
scheduler, in several steps. In the first step, the scheduler decides on a set of heuristic rules used
in a simulation model (the first scenario). Simulation is then run, and a result (schedule) is
produced. In the next step, the scheduler analyzes the schedule and decides on the second set of
heuristic rules (the second scenario). Simulation is run again, and a result (schedule) is produced.
This process is repeated until a satisfactory schedule is found. The goal of the what-if analysis is
to find such a set of heuristic rules producing a satisfactory schedule. For an example of this type
of scheduling software, see Proficy Scheduler (ROB-EX) from GE Intelligent Platforms, a
leading user of MTConnect data in aerospace manufacturing.
Such what-if analysis is a very labor-intensive and time-consuming process. It requires a
scheduler to understand heuristic rules (differences among rules and when a rule should be
applied). There are virtually hundreds of heuristic rules to choose from. If the shop floor has
dozens of machines and other resources (like tools, operators, automated guided vehicles, and
automated storage and retrieval systems) which need to have a rule assigned, then such “manual”
what-if analysis is tedious and can last for hours.
Let’s look at an example of how heuristic rules work and how they are used. Zoom in on one
machine in a job shop. Suppose there are 6 jobs waiting for processing in a queue in front of the
machine. In which sequence should we process these jobs on the machine? Each sequence has
significant and different impact on a production schedule. One sequence can result in a schedule
not meeting job due dates, and another sequence can meet the due dates. We would like to find
such a sequence which would produce as good schedule as possible. There are 720 possible
sequences (6! = 6x5x4x3x2x1=720) to choose from. In what-if analysis, we would have to carry
out 720 scenarios to find the best sequence for just one machine. With “manual” what-if
analysis, this process would take hours, so it is not practical to search through all 720 scenarios.
Therefore, we use heuristic rules to help us find a satisfactory sequence fast.
How does a heuristic rule work? In our example, there is a decision point with 6 options (which
job to select from the 6 jobs). The machine can processed one job at a time. When deciding
which option to take, a heuristic rule orders the 6 jobs according to a rule of thumb and selects
the first (“best”) job in the ordered list to be processed on the machine first. For example, the
FIFO (First In First Out) rule orders jobs based on the order as they entered the waiting queue.
Then FIFO selects the first job in the ordered list (the oldest job in the waiting queue). The EDD
(Earliest Due Date) rule orders jobs based on their due dates, and then EDD selects the job with
the earliest due date to be processed on the machine first.
Usually heuristic rules are simple, taking into account only one factor, like EDD cares only about
job due dates, and FIFO cares only about how long jobs are waiting in a queue. There are many
factors on a shop floor impacting quality of a schedule, such as sequence dependent setup times
or bottlenecks. Even if a rule appears reasonable and makes sense, like EDD, it can produce a
very poor schedule. This can happen when a combination of other factors creates a situation on
the shop floor which is not suitable for a certain rule to be applied at that time on that machine.
In a later time when the situation changes then the same rule can produce a very good schedule.
In what-if analysis, when a scheduler designs scenarios, it is very difficult to imagine all possible
future situations on the shop floor, particularly, when the shop floor has many machines and
other resources. For example, bottlenecks could be unpredictably shifting over time, so to find
the right set of heuristic rules for such fogy future situations is difficult. The scheduler has a
daunting task to assign the right rule for each machine and other resources which need it. Before
each scenario, the scheduler analyzes schedules from previous scenarios to figure out a better
combination of rules for the next scenario in order to find a better schedule.
Can we help the scheduler?
This labor-intensive and time-consuming “manual” what-if process is a prime candidate for
automation. This is our basic idea: Let’s automate the “manual” what-if analysis in simulation
scheduling/optimization, and let the computer do the tedious work. But how?
The idea proposes to exclude heuristic rules and to merge simulation with an approximate
branch-and-bound optimization algorithm. The resulting novel simulation-based optimization,
called sweep algorithm or Sweeper, is suitable for solving combinatorial problems, such as
production scheduling, vehicle routing, staff scheduling in hospitals or airports, resource
allocation, supply chain optimization, air traffic management, project management with limited
resources, scheduling military operations, and routing of war robots.
For example in production scheduling, Sweeper (1) first creates a simulation model of a shop
floor with all needed production constraints (finite capacity scheduling), based on input
MTConnect data from a real or near-real time snapshot of the shop floor, (2) then runs
automatically many scenarios with the model, (3) analyzes scenarios without any human
involvement, (4) searches intelligently for as good solution (schedule) as possible by branch-andprune optimization, and finely (5) outputs the best schedule found.
Branching is the key concept of the idea. In the above example with one machine and 6 jobs,
there is the decision point with 6 options. A heuristic rule selects only one option. The idea
proposes to select more than one option, preferably all options, so the right option leading to a
very good schedule or even the best (optimal) schedule cannot be missed.
In the real world, only one option can be selected. However in the computer world, we can select
more than one option. In such a simulation run, (1) this branching with several selected options is
carried out in every decision point; (2) the algorithm continuously analyzes situations on the
simulated shop floor; and (3) focuses on the most promising options to continue in the simulation
run. In this way, many promising scenarios are run and analyzed automatically, and finally, the
best found schedule is presented.
For a detailed description of Sweeper and its testing on the standard benchmark problems for job
shop scheduling, see our latest conference paper: (Mejtsky, G. J. 2008. “The improved sweep
metaheuristic for simulation optimization and applications to job shop scheduling”. In
Proceedings of the 2008 Winter Simulation Conference, 731-739. Piscataway, New Jersey:
Institute of Electrical and Electronics Engineers, Inc.).
In Sweeper, it is possible to use heuristic rules if needed. The rules can be freely mixed with the
novel optimization on machines and other resources. For example, on some machines only rules
can be applied, and on others the novel optimization can be applied. Even it is possible to mix a
rule and the novel optimization on one machine, effectively creating a hybrid (Super rule) which
outperforms the rule. In this way, we can create Super rules outperforming any heuristic rule.
Areas of benefit and impact
In Operations Management, the main impact of this innovation is in shop floor operations and in
the Production Scheduling area of Process Management. The direct benefits are: (1) higher
quality of schedules (schedules are shorter), (2) the schedules are generated faster since human
time-consuming tedious analysis is minimized, and (3) savings of labor-intensive and timeconsuming scheduling activities.
SECTION III: TECHNICAL REQUIREMENTS
The new scheduling module (Sweeper) can be deployed as an individual software application or
can be combined into an integrated application like ERP. In both cases, some
integration/interface work needs to be added to bring input MTConnect data into the module and
send output data (schedule) to a destination.
MTConnect is utilized heavily and often by any scheduling module, such as Proficy Scheduler
(ROB-EX) from GE Intelligent Platforms. Our new module needs the same large amount of
input MTConnect data like Proficy Scheduler or others. MTConnect data is provided by
capturing a snapshot of the whole shop floor. The frequency of taking snapshots depends on the
frequency of scheduling/rescheduling.
No additional scientific or technical breakthroughs are required to achieve the goal.
SECTION IV: BENEFITS
Because of the higher quality of schedules (schedules are shorter), manufacturing benefits are:
increased throughput, maximized utilization of resources (machines, people, tools, etc.), shorter
lead times, improved customer satisfaction (meeting due dates, on-time delivery), improved lean
manufacturing by reducing waste (waiting time, setup time, resource idleness). Therefore,
production cost is reduced, profitability is increased without any capital investment, and
sustainable manufacturing is supported.