My team and I developed a simulation platform to test out new machine learning models and logistics optimization approaches. In the process, we learned a ton about our marketplace, integrating batch and production systems, and how to accelerate innovation in the tech workplace. Hope you enjoy reading our new article with details of both why and how we did it and let us know your feedback and experiences in simulation.

https://doordash.engineering/2022/08/16/4-essential-steps-for-building-a-simulator/

I'm specifically interested in applying systems thinking to everyday life, and have trouble with math (which is why i'm not too interested in the harder/engineering side of systems science). What books should someone read to gain a basic understanding of the discipline?

I had an interesting conversation/interaction this morning while walking a different route than normal (from the mechanic back to my house). A homeless woman was trying to guide a shopping cart down a short, icy hill. She slid for a few feet and then the cart tipped over. Its contents spilled out down the hill except for some large pieces of cardboard. She had banged her leg on the cart during the fall, and was complaining loudly about it.

I went over to check on her, and told her I would right her cart and get the stuff back into it. What ensued, between "oh my leg!" complaints was me realizing there was a "best" way to pack the cart, and her telling me I wasn't doing it right.

a) Everything has to fit:

• sleeping bag, stuff sack, and pillow( uncovered stuffing)
• bag of clothes
• food ( twinkies,mandarin oranges, wine cooler)
• shelter ( large cardboard pieces
• heat supplies( plastic bag with large candle and lighter & smokes)
• small bag of bottles and cans (~ 6 total)
• covid mask

b) somethings need to be accessible(food, covid mask, smokes)

c) heavier items should be lower to make the cart more stable

d) cart should be balanced left to right

e) pile of stuff should be low enough to see over

f) sleeping bag was being aired out

g) My dog was trying to eat the twinkies so they had to off the ground first.

h) It was fucking cold out so I wasn't going to try many variations.

i) some of the items held more value and so she wanted them in a location she could see and control.( at least that's what I was assuming she wanted)

On one hand, she unpacked and packed the cart daily. On the other, I'm a bit of a know-it-all, so I wasn't going to listen to all her instructions.

We all know that the place where we hear about "sensitivity" the most is in the context of "specificity and sensitivity", e.g. used to evaluate how good statistical models are at predicting both classes of the response variable.

But recently, I came across the term "sensitivity" within the context of optimization.

Based on some reading, it seems that "sensitivity" in optimization refers to the following : if an optimization algorithm (e.g. gradient descent) settles on a final answer to an optimization problem (e.g x1= 5, x2 = 8, Loss = 18) ... then sensitivity analysis within optimization tries to determine "if x1 and x2 are slightly changed, how much would this impact the Loss?".

I think this seems intuitive - suppose when x1=5.1, x2 = 7.9 then Loss = 800 ... it would appear that the solution returned by the optimization algorithm is really 'sensitive' around that region. But imagine if x1=6, x2 = 4 then Loss = 18.01 ... it would appear that the solution is less sensitive. Using logic, you would want the solution to an optimization algorithm to be "less sensitive" in general.

Does anyone know how exactly to perform "Sensitivity analysis in optimization"? I tried to find an R tutorial, but I couldnt find anything. The best thing I could think of was to manually take the optimal solution and repeatedly add noise to the solution and see if the Loss changes - but I am not sure if this is good idea.

Does anyone if:

• my take on sensitivity analysis in optimization is correct?

• how exactly do you perform sensitivity analysis in optimization?

Thanks

Note: I assume "deterministic optimization" means that the optimization algorithm is "non-stochastic", i.e. returns the same solution every time you use it?

Hi all, I've come across the field of MDPs and I've been puzzled by question that I seem to find no straight forward answer to (even if going trough the handbook of MDPs).

Suppose I have a total expected cost problem (an UN-discounted problem where rewards are negative - it appears that there some subtle difference with positive problems ) where from the analytics I know that the optimal policy is monotone.

Is there any algorithm that I can employ to exploit the propriety of monotonicity of the optimal policy? The reason I ask is because from what I understand from Puterman, value iteration, policy and modified policy iteration may not converge to the optimal solution and hence I suppose it would be delicate modify such algorithms to only select monotone policies.

Would the route to follow simply consist of using some action elimination procedures?

Has anyone ever worked on problems in which you had to optimize multi-objective "blackbox" functions (i.e. functions where you can not take the derivatives, algorithms like gradient descent do not apply), e.g. using the genetic algorithm?

In the context of multi-objective optimization of non-blackbox functions, I read about some methods called "scalarization" which effectively transform multi-objective optimization problems into single-objective optimization problems.

For example: If you are trying to optimize three cost functions F1, F2, F3 ... you could combine these into a single problem using weighted coefficients, e.g. T = A * F1 + B* F2 + C *F3

A popular way to solve the above equation is to use methods like "epsilon-constraint": This is where you apply the desired constraints to F1, F2, F3 ... and then instruct the computer to loop through different values of A, B, C. Then, you see which combination of parameters (used in F1, F2, F3) result in the minimization of "T" - this is much easier to compare, since you can just rank all the candidate solutions. (source: https://www.youtube.com/watch?v=yc9NwvlpEpI)

This leads me to my question:

1. Do methods like "epsilon constraint" apply to "Blackbox" Functions? I.e. Can you use the "epsilon constraint" method along with the genetic algorithm?

2. Intuitively, when dealing with a multi-objective optimization problem: is there any way to deal with all the solutions along the "Pareto Front"? Using the concept of the "Pareto Front" - suppose the optimization algorithm identifies a set of solutions that "can not be made better in some criteria without worsening some other criteria" ... how exactly can you rank and compare all the solutions along the Pareto Front? The concept of scalarization seemed useful, seeing how it converts a multi-objective optimization problem into a single-objective optimization problem, and therefore you can rank all the candidate solutions according to the ones that result in the minimum cost of the single objective .... but otherwise, how are you supposed to pick a solution among the set of solutions along the Pareto Front?

Thanks

Can someone help me with writing the OPL CPLEX model for the attached problem. I'm having difficulties writing up the constraints onto CPLEX (I'm a beginner).

THANKS

https://preview.redd.it/1co75g8sk8071.png?width=1256&format=png&auto=webp&s=3cf52c622644175cc8e0db0402e9dc560e1f264e

I have downloaded the new version of Arena (16.1) and realized it doesn't have an "Alter block" the previous one used to have.

Does anyone know what I can replace it with? If there is a better subreddit to ask I would be grateful to know.

I am planning to develop a VRP tools and I have choices for below solvers Google Or Tools, Optaplanner, Jsprit, Vroom.

Any one have any experience or suggestion about these?

Thanks

Hello!

Recently I deployed MiniZinc Playground at https://play.disopt.com/. It's a simple editor where you can put MiniZinc model and run it. Also it has option to share your model.

I developed this Playground for some discrete optimization courses to simplify initial "aha" moment and as a nice way to share code. I hope it could be useful for community too.

It has a few limitation: 20 seconds maximum run time and solver is only gecode. Syntax highlighting is also limited. I put only most used keywords. In a future I'll add more.

If you catch any problems, email me at play@disopt.com

Thanks, be safe!

https://preview.redd.it/wyiaypy23sa61.png?width=1282&format=png&auto=webp&s=af12c1aad009273a3b04a7b8b0986e2e4e200a50

Hi All,

I am writing a thesis entitled " Industry 4.0 based manufacturing implementation: Analysis and prioritization of risks in industry (empirical work),". Essentially this involves surveying industry leaders to rank a number of possible risks (from most to least significant) faced by SME's in implementing industry 4.0 technologies.

The nine risks I have chosen to focus on (the risks that will be included in my questionnaire) are as follows:

1. Financial constraints, 2. Employee opposition, 3. Lack of skills among workforce, 4. Data security issues (due to weak IT infrastructure), 5. Current lack of a certification & standards, 6. Reliability of new systems (and consequences of system failure) 7. Legal issues/lack of clear government policies, 8. Lack of implementation methodology, 9. Risk of overdependency on external partners

However, before constructing my questionnaire, I must first select an appropriate multi-criteria decision making model with which to rank the risks once I have gathered the "inputs," from the survey. I am having some trouble getting my head around various MCDM's well enough to make a selection as to which would be best to construct the questionnaire around.

I am looking to find an MCDM which requires the fewest inputs possible (so as not to inconvenience those completing the questionnaire more than absolutely necessary) whilst still remaining consistent, and can be used with both qualitative and quantitative inputs. Based on some research, I think I can narrow my choice down to three:

1. The Analytical Hierarchy Process (AHP),
2. The Best-Worst method (BWM),
3. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

At the moment, I am thinking the BWM may be the best option to go with, as it requires relatively few inputs (2n-3), while also being consistent.

However, I still don't feel as though I understand the methods well enough to be sure. I'd be grateful to hear from anyone who's experienced in using these methods for similar problems, or anyone with a second opinion who may be able to point out anything I may be missing.

My search is also not constraint to the three methods listed above, so I am open to suggestions on other methods or "hybrid," methods if they are seen to be more suitable for a problem like this.

I have linked some articles of problems with somewhat similar structure to mine and how they have implemented these MCDM's

https://www.emerald.com/insight/content/doi/10.1108/IJPPM-05-2019-0221/full/html

http://ehemj.com/article-1-559-en.html

https://www.sciencedirect.com/science/article/pii/S0959652617306200

https://www.worldscientific.com/doi/abs/10.1142/S2196888819500167

Thanks all!

I’m trying to solve a VRPMT problem with Or-tools.

I have a fleet of dump trucks that need to go to leave the shop then travel to sites and pick up full loads. Then there’s a handful of landfills they can visit to unload. They can then leave that landfill and get another load if they have enough time (max 12hr driving time) before returning to the shop.

Any idea on how to use OR Tools to solve this Multi Trip problem?

I am a pure math major but looking to get into something more applied for grad school. My current interests are in graph theory ( I do graph theory research and have taken graduate and undergraduate classes in it) I am also really interested in optimization (continuous and discrete) and computer science (algorithms and complexity). Is OR a good field to look into? I originally got interested in OR/IE by looking at the book Scheduling Theory by Michael Pinedo. I haven’t read much but it looks really interesting.