Week 2

The number of similar jobs processed together, either sequentially or simultaneously, is known variously as the batch size or lot size of the operation.

• Due to capacity

• In many instances, it is more efficient to process a batch of entities than to process them one at a time.

• There are three basic reasons for the increase in efficiency due to batching

  • Setup Avoidance

  • Pacing Improvement (getting into a rhythm)

  • Simultaneous Processing

    • Some operations are intrinsically batch in nature because they can process a batch of entities as quickly as they can process a single entity (a forklift takes the same amount of time with one part or a batch)

• Simultaneous batches

  • where entities are processed together

• Sequential batches

  • Where entities are processed one-at-a-time between setups

Number of entities processed together at a station

number of entities moved together between stations

• Regardless of the application, the purpose of simultaneous batching is to make effective use of the capacity of the process.

• Since cycle time increases in utilization, we would expect increasing batch size to decrease cycle time. And this is exactly what happens as long as larger batch sizes do not cause entities to wait while forming a batch. For instance, if the entire batch arrives together, then none of the entities will have to wait and hence cycle time will unambiguously decrease with batch size.

• When can cycle times increase

  • If parts arrive one at a time to a simultaneous batch operation, then it is possible for cycle time to increase in the batch size.

  • If arrivals to the operation are slow and the batch size is fixed and large, then the first entities to arrive will wait a long time for a full batch to form.

A more effective way to avoid excessive wait-for-batch time is to abandon the fixed batch size policy altogether.

• How to determine cycle time with simultaneous batching?

  • PT/2 is the average waiting time

  • PT/2+PT=3PT

ARATE x 3PT/2

A sequential batch operation is one that processes entities sequentially (one at a time) but requires time to setup or change over before moving to a different type of entity

• The smallest batch size that yields a stable system may be greater than one

  • If the wait-for-batch and process times become large enough to offset the utilization (and hence queueing) reduction due to large batch sizes

• Delay due to batching (eventually) increases proportionally in the batch size

• Reduce setup times

• Start moving finished entities while the entire batch is not yet finished (Move batch splitting)

Economic order quantity (EOQ) model

• There is no guarantee that the batch sizes produced by the EOQ model will even be feasible

• it may be very difficult to construct an actual production schedule from them

• Even if the batch sizes are feasible and lead to a schedule, the schedule might be such that a customer has to wait a long time for a particular entity type to “come around” on the schedule

• Approach the multi-product sequential batching problem is in terms of allocating setups to product types

• Look at what the production run times are if all products have only one run, and then minimize run time by adding setups

• First determine total process time

• Determine UTIL (without setups)

  • Total process time / Total available hours

• Determine UTIL where every product is made in one batch (1 setup per product type)

• Determine ideal UTIL

• Determine what the ultimate UTIL is in production hours

  • sqrt(UTIL zero setups) x total available time

• Determine how much time can be spent on setup

  • Ultimate UTIL - total process time (sum of all production times without setups)

• Determine run length per product type

• Determine which product has the longest run time

• Add (if possible) an extra setup for that particular product

• Check the total sum of setups times again and do the process again till