How do you schedule jobs effectively in a job shop? It’s a challenge with many potential solutions, but three problems often get in the way of a good schedule.
Job shops are notoriously challenging when it comes to scheduling. As we discussed in a previous article, high-mix, low-volume manufacturers often find it hard to achieve a consistent flow of jobs.
This can be troublesome when you’re considering whether to add robots, which have traditionally been best for consistent operations. Even though collaborative robots can reduce this challenge, they don’t remove it completely.
But as I like to say, forewarned is forearmed! When you’re aware of the potential problems of job shop environments, you can be prepared to tackle them if they arise.
Researchers have studied job shop scheduling for over 50 years. There are common issues that come up again and again. In this article, we discuss three scenarios which can cause problems in a job shop if you’re not prepared from them.
Why job shop scheduling is hard
The main scheduling challenge in job shops: inconsistent flow.
Jobs are often passed between operations on the shop floor as and when they are needed, without a well-defined manufacturing schedule. This makes job shops flexible, but it also means that you can’t easily tell how long a job will take to complete.
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This is more than just a manager’s pet peeve. It’s a significant scheduling problem that’s been researched for years in a few different fields. Researchers call it “The Job Shop Problem.” It also has diverse applications, including flexible manufacturing, railway scheduling, air traffic control, and robotic cell scheduling.
Researchers aim to answer this question: How can you schedule jobs effectively in a job shop?
The goal of the Job Shop Problem is to minimize “makespan,” which is the total duration of the schedule for all jobs. This is different from “cycle time” which is the duration (or average duration) of just one job. The problem is represented mathematically like this:
We have m machines (or manufacturing processes) and n jobs.
Each job consists of k operations, each with a processing time of Pk which is bigger than 0 (Pk > 0).
No job goes through the same process twice in a row (i.e. we assume there are no reworkings).
An operation is only feasible if its schedule (Sk) begins after the previous operation has finished (Sk+Pk <= Sk+1).
Find a feasible schedule (Sk) that minimizes makespan Ck.
Although it might seem simple when written out like this, the Job Shop Problem is one of the hardest scheduling problems and is classed as NP-Hard (which is a measure of the difficulty of solving a computationally complex problem).
Three common problems for job shops
Below are three of the common issues that arise in the mathematics of job shop scheduling. They are also an issue for real-world (i.e., non-theoretical) job shops.
1. Transportation or minimum delay
Transportation refers to the time taken to move products from one machine to the next. It’s a challenge because it introduces an extra constraint to the schedule. The next operation can’t start until the previous operation and transport time has passed. Transport times also vary depending on the distance between different machines, which makes them inconsistent.