Currently I am modelling a high bay shelf in anylogic(personal learning edition) using Material handling library and process modelling library. Using jibcrane or overhead crane I am unable to store or retrieve pallets in all the racks. The pallet rack has 4 levels. Is there any other resource I can use instead of jibcrane or overhead crane to pick and drop the pallets in or from the racks?
you can actually store and retrieve pallets from racks using a crane using some tricks, even though it doesn't make sense in the real world
Nevertheless, in terms of resources, anything from a forklift to a person agents can retrieve and store in racks.. In the rackstore and rackpick properties you have a resource section that is quite easy to use.
If the problem is the 4 levels, there are forklifts that can lift pallets really high, and anylogic takes into consideration the level in order to calculate the storage time.
But anyways, your problem is not very clear.. do you want to actually use cranes to store? Or did I answer the question you had at least partially??
Related
I am currently working on a generalized warehouse model containing all processes that take place in warehouse operations. I just started to work with anylogic and I can not figure out how to implement order picking strategies. My current model is able to receive truckloads containing pallets, the pallets are checked, booked and stored in a racking system. For the outbound processes of picking, packing, shipping I created an order containing a single pallet that moves through all the processes. However, a picking process of only single pallets is not really representative of warehouse operations. Therefore, I want to know if it is possible to implement order picking strategies such as batch picking, wave picking, discrete picking, amongst others. I hope someone can help me out.
Kind regards,
Stefan
what is packaged inside standard Anylogic is just a possibility to easily simulate full pallet moves(receive, putaway,picking, shipping) without a lot of programming. To do that you will just use existing AL objects: RackStore, RackPick, maybe some MoveTo, Queue, etc.
But if you go beyond that and want to build some realistic warehouse with processing on a lower level of packing structure (pieces, and maybe even some more - layers, blisters, etc) that will require quite some coding. Depending on your chosen abstraction level you may even want to code everything exactly as its coded in your WMS in extreme case. Or maybe you simplify something but still its quite a modelling task for every new process (Pickwave, Disc Picking, etc) you want to implement. So the answer to your question - yes, its possible but beware of high effort.
Situation
I am trying to implement a warehouse system using a traditional database.
The tables are :
products (each row representing 1 sku)
warehouse_locations ( each row represents a particular shelf in a particular warehouse)
pallets (each row represents a particular pallet)
user_defined_categories (each row represents a particular user defined category: e.g. reserved, available, total_physical, etc)
products_in_pallets_by_categories (each row will have foreign keys of the pallets, products, and user_defined_categories table. will specify quantity of products in a particular pallet of a particular category.)
products_in_warehouse_locations_by_categories (each row will have foreign keys of the warehouse_locations, products table, and user_defined_categories. will specify quantity of products in a particular pallet of a particular category.)
What end users want to see/do
End users will update the system about what products are placed/removed on what pallet.
End users will also want to know any time (preferably in real-time) how many reserved or available products are in the warehouse.
So what's my initial plan?
Wanted to use a traditional RDBMS like PostgresQL and a message queue like RabbitMQ to provide real-time updates. By real-time updates, I mean the end users using either a single page application or mobile phone can observe changes in inventory in real-time.
So what's changed?
I came across rethinkdb FAQ and it said
RethinkDB is not a good choice if you need full ACID support or strong
schema enforcement—in this case you are better off using a relational
database such as MySQL or PostgreSQL.
Why you even considering rethinkdb?
Because if I can use it and it allows real-time updates, it will help tremendously as we expect the client's sales team placing reservations around the world on our system.
What's the most frequent updates/inserts?
The movement of the products from one place to another. I expect plenty of updates/inserts/deletes to the relation tables. Occasionally, I apologise I do not know how to explain this in the rethinkdb paradigm. I am a traditional RDBMS person.
Is the system built yet?
Not yet. Which is why I want to seek an answer regarding rethinkdb before actually proceeding.
Do you expect to use any transactions?
Well, I am not sure.
I can think of a real world case where a warehouse worker moves products (partially or completely) from one pallet to another pallet.
Another real world case will be where a warehouse worker moves the products from a pallet to a warehouse_location (or vice-versa).
Do I definitely need to use transactions? Again, I am not sure.
Cause I expect the workers to update the system AFTER they have physically finished the moving.
I will provide a screen for them to choose
move from <some dropdown> to <another dropdown>
So what's the question?
Do I need to have full ACID support or strong schema enforcement for my warehouse system based on my user requirements at the moment? And is it implementable using rethinkdb?
I also expect to implement activity streams once the system is implemented which will show events such as Worker A moved 100 units of product A from warehouse shelf 1A to pallet 25.
When you are dealing with things where information must always be accurate and consistent, ACID matters. From what you say, it sounds like it is important.
It sounds to me like you want to just allow real-time updates and that the key problem is in seeing rabbit-mq as the non-real-time component, correct? Why were you even considering RabbitMQ? (If it is to allow the db to go down for maintenance, maybe implement a backup private caching store in sqlite?)
In general you should assume you need ACID compliance until you have a model where eventual consistency is ok. Additionally real-time accurate reporting rules out eventual consistency.
Imagine a large organisation with many applications. The applications are not currently integrated to any great extent. There is a new and empty enterprise data warehouse, and it would store all data in a canonical format. The first step is to set up the warehouse and seed it with data from the applications.
I am looking for pros and cons between the following two enterprise integration patterns:
1) Using a combination of integration tools, setup batching to extract transform and load data on a periodic interval into the warehouse. Then, as part of the process, integrate the data from the warehouse to the required applications.
2) Using a combination of integration tools, detect changes real-time, or in batch and publish them to a service bus (in canonical format). Then, for each required application, subscribe to the messages to integrate them. The data warehouse is another subscriber to the same messages.
Thanks in advance.
One aspect that is hard to get right with integration-via-messages is periodic datasets.
Say you have a table in your data warehouse (DW) that contains data partitioned by day. If an ETL job loads that table, you can be sure that if the load job is finished, the respective dataset is complete (unless there's a bug in the job).
Messaging systems, on the other hand, usually don't provide guarantees of timely delivery. So you might get 90% of messages for a particular day by midnight, 8% within the next hour, and the remaining 2% within the next 6 hours (and a few messages might never arrive). In this situation, if you have a job that depends on this data, how can you know that the dataset is ready? You can set an arbitrary cutoff time (e.g. 1 hour past midnight) based on previous experience, SLAs, or some other criteria, when you consider the dataset complete, but that will by design be an approximation. You will also need some means to detect missing data (because of lost messages) and re-request it from the source.
This answer talks about similar problems.
Another issue is backfills. Imagine your source sends a backdated message, for example to correct some previously-sent one that belongs to a dataset in the past. Presumably, any consumers of that dataset need to be notified of the change and recompute their results. However, without some additional logic in the DW they might not know about it. With the ETL approach, since you already have dependencies between jobs, if you rerun some job with a backfill date, its dependencies will run automatically, or at least it'll be explicitly known that some consumers are affected.
With these caveats in mind, the messaging approach has some great advantages:
all your systems will be integrated using a uniform approach
the propagation time for your data will potentially be much lower
you won't have to fix ETL jobs that exploded because the data volume has grown past their ability to scale
you won't get SLA violations because your ETL jobs timed out
I guess you are talking about both ETL Systems and Mediation (intra-communication) design pattern. I don't know why have to choose between them, in my current project we combine them.
The ETL solution is implemented as Layer responsible for management of the Data integration (via Orchestrator module). It a single entry point and part of the Pipes and filters design pattern
concept that we rely on. It's able to perform a variety of tasks of varying complexity on the information that it processes.
On the other hand the Mediation as EAI system acts as "broker" between multiple applications. Whenever an interesting event occurs in an application (for instance, new information is created or a new transaction completed) an integration module in the EAI system is notified. The module then propagates the changes to other relevant applications.
So as bottom line I can't give you pros & cons for both, since to me they are a good solution together and their use is dependent on your goals, design etc.. But from your description it's seems to me that is similar to what I've suggested.
I often hear about eventual consistency in different speeches about NoSQL, data grids etc.
It seems that definition of eventual consistency varies in many sources (and maybe even depends on a concrete data storage).
Can anyone give a simple explanation what Eventual Consistency is in general terms, not related to any concrete data storage?
Eventual consistency:
I watch the weather report and learn that it's going to rain tomorrow.
I tell you that it's going to rain tomorrow.
Your neighbor tells his wife that it's going to be sunny tomorrow.
You tell your neighbor that it is going to rain tomorrow.
Eventually, all of the servers (you, me, your neighbor) know the truth (that it's going to rain tomorrow), but in the meantime the client (his wife) came away thinking it is going to be sunny, even though she asked after one or more of the servers (you and me) had a more up-to-date value.
As opposed to Strict Consistency / ACID compliance:
Your bank balance is $50.
You deposit $100.
Your bank balance, queried from any ATM anywhere, is $150.
Your daughter withdraws $40 with your ATM card.
Your bank balance, queried from any ATM anywhere, is $110.
At no time can your balance reflect anything other than the actual sum of all of the transactions made on your account to that exact moment.
The reason why so many NoSQL systems have eventual consistency is that virtually all of them are designed to be distributed, and with fully distributed systems there is super-linear overhead to maintaining strict consistency (meaning you can only scale so far before things start to slow down, and when they do you need to throw exponentially more hardware at the problem to keep scaling).
Eventual consistency:
Your data is replicated on multiple servers
Your clients can access any of the servers to retrieve the data
Someone writes a piece of data to one of the servers, but it wasn't yet copied to the rest
A client accesses the server with the data, and gets the most up-to-date copy
A different client (or even the same client) accesses a different server (one which didn't get the new copy yet), and gets the old copy
Basically, because it takes time to replicate the data across multiple servers, requests to read the data might go to a server with a new copy, and then go to a server with an old copy. The term "eventual" means that eventually the data will be replicated to all the servers, and thus they will all have the up-to-date copy.
Eventual consistency is a must if you want low latency reads, since the responding server must return its own copy of the data, and doesn't have time to consult other servers and reach a mutual agreement on the content of the data. I wrote a blog post explaining this in more detail.
Think you have an application and its replica. Then you have to add new data item to the application.
Then application synchronises the data to other replica show in below
Meanwhile new client going to get data from one replica that not update yet. In that case he cant get correct up date data. Because synchronisation get some time. In that case it haven't eventually consistency
Problem is how can we eventually consistency?
For that we use mediator application to update / create / delete data and use direct querying to read data. that help to make eventually consistency
When an application makes a change to a data item on one machine, that change has to be propagated to the other replicas. Since the change propagation is not instantaneous, there’s an interval of time during which some of the copies will have the most recent change, but others won’t. In other words, the copies will be mutually inconsistent. However, the change will eventually be propagated to all the copies, and hence the term “eventual consistency”. The term eventual consistency is simply an acknowledgement that there is an unbounded delay in propagating a change made on one machine to all the other copies. Eventual consistency is not meaningful or relevant in centralized (single copy) systems since there’s no need for propagation.
source: http://www.oracle.com/technetwork/products/nosqldb/documentation/consistency-explained-1659908.pdf
Eventual consistency means changes take time to propagate and the data might not be in the same state after every action, even for identical actions or transformations of the data. This can cause very bad things to happen when people don’t know what they are doing when interacting with such a system.
Please don’t implement business critical document data stores until you understand this concept well. Screwing up a document data store implementation is much harder to fix than a relational model because the fundamental things that are going to be screwed up simply cannot be fixed as the things that are required to fix it are just not present in the ecosystem. Refactoring the data of an inflight store is also much harder than the simple ETL transformations of a RDBMS.
Not all document stores are created equal. Some these days (MongoDB) do support transactions of a sort, but migrating datastores is likely comparable to the expense of re-implementation.
WARNING: Developers and even architects who do not know or understand the technology of a document data store and are afraid to admit that for fear of losing their jobs but have been classically trained in RDBMS and who only know ACID systems (how different can it be?) and who don’t know the technology or take the time to learn it, will miss design a document data store. They may also try and use it as a RDBMS or for things like caching. They will break down what should be atomic transactions which should operate on an entire document into “relational” pieces forgetting that replication and latency are things, or worse yet, dragging third party systems into a “transaction”. They’ll do this so their RDBMS can mirror their data lake, without regard to if it will work or not, and with no testing, because they know what they are doing. Then they will act surprised when complex objects stored in separate documents like “orders” have less “order items” than expected, or maybe none at all. But it won’t happen often, or often enough so they’ll just march forward. They may not even hit the problem in development. Then, rather than redesign things, they will throw “delays” and “retries” and “checks” in to fake a relational data model, which won’t work, but will add additional complexity for no benefit. But its too late now - the thing has been deployed and now the business is running on it. Eventually, the entire system will be thrown out and the department will be outsourced and someone else will maintain it. It still won’t work correctly, but they can fail less expensively than the current failure.
In simple English, we can say: Although your system may be in inconsistent states, the aim is always to reach consistency at some point for each piece of data.
Eventual consistency is more like a spectrum. On one end you have strong consistency and on other you have eventual consistency. In between there are levels like Snapshot, read my writes, bounded staleness. Doug Terry has a beautiful explanation in his paper on eventual consistency thru baseball
.
As per me eventual consistency is basically toleration to random data in random order every time you read from a data store. Anything better than that is a stronger consistency model. For example, a snapshot has stale data but will return same data if read again so it is predictable. Sometimes application can tolerate data which is stale for a given amount of time beyond which it demands consistent data.
If you look at meaning of consistency it relates more to uniformity or lack of deviation. So in non computer system terms it could mean toleration for unexpected variations. It could be very well explained thru ATM. An ATM could be offline hence divergent from account balance from core systems. However there is a toleration for showing different balances for a window of time. Once the ATM comes online, it can sync with core systems and reflect same balance. So an ATM could be said to be eventually consistent.
Eventual consistency guarantees consistency throughout the system, but not at all times. There is an inconsistency window, where a node might not have the latest value, but will still return a valid response when queried, even if that response will not be accurate. Cassandra has a ring system where your data is split up into different nodes:
Any of those nodes can act as the primary interface point for your application. So there is no single point of failure because any of those nodes can serve as your primary API point. But there is a trade-off here. Because any node can be primary, that data needs to be replicated amongst all of these nodes in order to stay up to date. So all of the other nodes needs to know what is where at all times and that means that as a trade-off for this architecture, we have eventual consistency. Because it takes time for that data to propagate throughout the ring, through every node in your system. So, as the data is written, it might be a little bit of time before you can actually read that data back you just wrote. Maybe data is written to one node, but you are reading it from a different node and that written data have not made it to that other node yet.
Let's say you back up your photos on your phone to the cloud every Sunday. If you check your photos on Friday on your cloud, you are not going to see the photos that were taken between Monday-Friday. You are still getting a response but not an updated response but if you check your cloud on Sunday night you will see all of your photos. So your data across phone and cloud services eventually reach consistency.
I am new to Scala and Akka and am considering using it to solve a problem. Suppose I have a calculation engine (that searches for a solution). I'd like to parallelize that search both across cpus and across nodes by giving each cpu on each node its own engine instance.
The engine inputs consist of a small number of scalar inputs and a very large hash table. Each engine instance would use its scalar inputs to make some small local change to the hash table, calculate a goodness, then discard its changes (they do not need to be committed/seen by any other engine instance). The goodness value would be returned to some coordinator that would choose among the results.
I was reading some about the STM TransactionalMap as a vehicle for shared state. This seems ideal, but I don't really see any complete examples using it as shared state.
Questions:
Does the actor/stm model seem right for this problem?
Can you show a specific example of how to distribute the shared state? (is it Ref[TransactionalMap[,]] as a message?
Is there anything different about distributing the shared state within a node as opposed to across different nodes?
Inquiring Minds Want to Know,
Allan
In terms of handling shared memory it doesn't sound like STM would be the right fit here because you don't want the changes made in engine instances to commit to the shared copy of the hash table.
Instead, an immutable HashMap might be a better fit. The elements that do not change in the map can be shared by the engine instances with only the differences in each map taking additional memory space.
The actor model would fit very well what you want to do. Set up one actor for each engine instance you want and pass it a message with the scalar values and the hashmap. Then have it return the results to the coordinator.