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Hi Aimms Community, I’m having trouble using the Sort function in order to obtain the values of a mulit-dimensional array: For example:multi-dimensional array = foo(size,car,color,location) size, car, color, location = some float value [ ‘small’, ‘toyota’, ‘white’, ‘usa’] = 1.25 [ ‘small’, ‘toyota’, ‘green’, ‘greenland’] = .5 [ ‘small’, ‘nissan’, ‘blue’, ‘france’’] = 1.0[ ‘small’, ‘nissan’, ‘green’, ‘france’’] = .3 In this case we are looking to sort foo by the values for all colors and locations where the size and car are the same, meaning that the result would be put into another variable bar(size,car,row)[ ‘small’, ‘toyota’, 1] = .5[ ‘small’, ‘toyota’, 2] = 1.25[ ‘small’, ‘nissan’, 1] = .3[ ‘small’, ‘nissan’, 2] = 1.0 How do I approach this problem? I looked through the documentation but I see that the Sort operator is really trying to sort by an index of an array and not the values for given indexes. Thanks for any help
In this how-to article a hello world style introduction to leveraging a REST API with an OpenAPI specification is presented. The complete example can be found here.That article discusses creating the attached application. This application can be opened using the AIMMS Community edition. After obtaining an API key from ipTwist, and filling in my own IP address it shows me a location in the Netherlands:
Herewith we would like provide some information on behalf of our partners from the OPTIMAL project introduced in one of our previous posts. More specifically, this post is about the OptiCL package developed by the researchers at OPTIMAL for Mixed-Integer Optimization with Constraint Learning. At AIMMS, we are exploring capabilities such as those provided by the OptiCL package and are looking forward to feedback on this matter from the AIMMS Community. We are especially interested in potential use cases and interests for conducting pilot studies in this area.The increasing availability of data has led to the rise in data-driven decision making. One area of data-driven decision making that is becoming increasingly popular is constraint learning . Many real-life optimization problems contain one or more objectives and/or constraints for which there are no explicit formulae. In such cases, machine learning models, which are good at determining complex decision boundaries, can be used to
Example:For all t, H(t) = Z(t+1) + Z(t).Z(t) is the variable. And H(t) is the parameter calculated with the adjacent elements in the variable. How can I do this?BTW, in this instance, what’s the ‘index domain’ of the parameter H, as the index t=1,2,……,24, i.e. the maximum element in Z is Z(24), if I define the ‘index domain’ of H as t directly, which means that there exists H(24) = Z(25)+Z(24), which should not be existed. Would this cause error or how can I limit the index domain of H to t = 1,2,…..,23?
Gabriela Servidone, AIMMS modeling expert at UniSoma, explains why she started studying usability and how it has changed her approach to projects with customers. She also shares how she and her colleagues transitioned customers into WebUI apps, and why she is excited about using WebUI to its full potential. A lot of developers struggle with making their model easy to use for a typical end user. How did you start learning about usability concepts? I read a few books about usability. I was able to understand the main concepts, and I use them every day in my work. When you read these, at first it seems obvious. You wonder why it needs to be written. But, in your daily work you forget these simple things. For example, if you have buttons Cancel and OK, and they are in the "wrong" or unusual position. This little detail makes it really hard for your users! So, it's worthwhile to review these fundamentals and really try to follow them. And it doesn't just mean the UI. I presented recently a
Good day, I am facing difficulties as below when I building the refinery logistics model:Sum up the total cost that includes charter rate and ship consumption cost as per the formula below. Note that the calculation can be found in the “Data.xlsx” Make scenario like one of the ship is not working and total ship will be 3 instead of 4. The model should be able to run the total cost based on diff scenarioThe project is about planning the ship route by optimizing the demand fulfilment and total cost from one location to another. I would like to seek some opinion and guidance for me to move on with this project as I am very new to AIMMS. Attached is the project model file for your reference and testing. Do comment if you need more clarification. Thank you and hope to get some response.
I am a novice. I'm working on a vehicle routing problem that considers soft time windows and fuel costs. I need to set a very large penalty value (b) so that it becomes a hard time window because I don't want to violate it. But the solution time will exceed 24 hours. Wondering how to reduce the solution time?The version used is 22.214.171.124. Other versions cannot be used due to research requirements.Thank you for your reply
Hi all,I’m sorry of the question is too basic, but I recently had to re-run an old code (originally developed in AIMMS 4.47) and I’m having lots of trouble correcting errors.I have a variable ( Ireal(ld) ) that uses another variable ( Vreal(bus,ph) ) in its calculation.I have an string parameter ( load_bus(ld) ) that has the “bus” value for each “ld” and a parameter ( load_ph(ld) ) that has the “ph” value for each “ld”.In the old code I used Vreal(load_bus(ld), load_ph(ld)) to calculate the Ireal(ld), but in the new AIMMS version (4.88) I keep getting “The scope of string parameter “load_bus” has not been specified”.Can anybody please help me in what I’m missing?Thank you in advance.
We solved the MIP optimization model. The characteristics of the models and solution times are given in the table below. We used SQLServer Express for data ETL purposes. Input and output data published in two datasets (Dataset: Podporozhye, Dataset: Estonia) in the MendeleyData repository. We will present the results at the IECF2021 conference in September. Estonia Podporozhye Constraints 2904 3203 Variables 2760 3112 Binary 2759 3111 Nonzeros 36890 20174 CBC, s 1.78 3.75 What is of interest to us as AIMMS users in this case study? The use of AIMMS Community Edition with open solver CBC to find solutions for scientific problems in the first approximation. In other words, AIMMS Community Edition allows us to test different models of the problem on a small data set. @bacherikov_iv , @anastasiiasimonenkova Thanks to AIMMS B.V. for providing the lic
Hello all together,Unfortunately I am a complete AIMMS beginner and can't get any further on my own.Can someone please help me to build this model (I have only the Academic License):Four products can be produced in a total of 10 locations (but not every product can be produced in every location).All products should then be transported to a fixed location.3 objective functions are to be considered simultaneously with different weights (minimize production costs, minimize CO2 emissions during production, maximize social impact due to production at each location).A few constraints should be taken into account, e.g. if 2 products come from the same location, the transport costs should be reduced, etc. This model should answer the question: how much of which product should be produced in which location? Thanks for your help!
Here's a post sourced from the old Tech Blog that points you to some handy tricks for integer and linear modeling! Modeling problems with an (integer) linear program sometimes requires some experience to recognize certain structures in the problem description that can be formulated in a linear way. On the website of the [url=http://faculty.nps.edu/vitae/cgi-bin/vita.cgi]Naval Postgraduate School[/url], you can find the document [url=http://faculty.nps.edu/dell/docs/Brown_Dell_INFORMS_Transactions_on_Education_January2007.pdf]Formulating Integer Linear Programs: A Rogues’ Gallery[/url] that tries to demystify the art of formulating linear and integer linear programs. They do this by introducing formulettes, which consist of a verbal description and the constraints and variables that model this verbal description. The first simple example of a formulette they provide is the following: [i]For each unit of [b]X[sub]1[/sub][/b] , there must be at least 5 units of [b]X[sub]2[
When the going gets tough ... we at AIMMS use this paper a lot! Even with state-of-the-art hardware and software, mixed integer programs can require hours, or even days, of run time and are not guaranteed to yield an optimal (or near-optimal, or any!) solution. In this paper, we present suggestions for appropriate use of state-of-the-art optimizers and guidelines for careful formulation, both of which can vastly improve performance. http://inside.mines.edu/~anewman/MIP_practice120212.pdf [b][i]Are you using this as well? You can add 'your bible' as a reply.[/i][/b]
Study 6Knowledge Base
Study 6: Accelerate Adoption of Relevant Customer Channels This study can help you ensure resource requirements in your supply chain. Impact – A very likely scenario is an accelerated adoption of certain customer channels, for instance online grocery. These channels might not be able to bear the increased shift in demand. Action – Relevant customer channels must be prioritized and the resource requirements to fulfill their demand must be studied. How –AIMMS S&OP Navigator allows users to prioritize their customers to increase the fulfillment in that channel. Although a forced higher fulfillment of one channel might lead to an overall lower margin, understanding the impact on your resources of putting a higher burden on that channel could prove to be invaluable in the long run. Demo –Customer Channel Priority in S&OP Navigator This article is part of the series Immediate Supply Chain Actions to Take Amid COVID-19.
Immediate Supply Chain Actions to Take Amid COVID-19Knowledge Base
There are many impacts of COVID-19 that will be felt by Supply Chains in the coming weeks and months. As advised by McKinsey, in the next 2-4 weeks it is critical for companies to understand the exposure throughout their value chain, take actions to address anticipated shortages and ensure resources requirements to restart. Supply chain planning tools like AIMMS SC Navigator can be used to directly address many of these impacts and help businesses recover quickly from losses. We hope that this series of articles gives you some direction - and we welcome your comments and advice from experiences relating to these challenges. Understand Exposure Study 1: Anticipate Best- and Worst-Case Operations Scenarios Study 2: Inspect Demand Trends Anticipate Shortages Study 3: Identify Optimal Sourcing Locations Ensure Resource Requirements Study 4: Create Multiple Plans for Resource Utilization Study 5: Refine Capacity Smoothing Study 6: Accelerate Adoption of Relevant Customer Channels
Study 1Knowledge Base
Study 1: Anticipate Best- and Worst-Case Operations Scenarios This study can help you understand exposure in your supply chain. Impact – Prolonged shutdowns will have both financial and operation implications, which are often very difficult to realize due to the complexity and far-reaching nature of supply chains. Action – Scenario planning is the key to understanding the many repercussions of sudden changes in demand and operations. Multiple best/worst case scenarios should be run under different demand conditions to study its impact on your supply chain. This can help expose any vulnerabilities in the network and take business a step closer towards preparedness. How – AIMMS Network Design Navigator allows users to adjust demand for unique customers, locations and products. This means that you can target very specific parts of your demand data and dynamically run different scenarios in those conditions to measure its effect. On top of this, after demand has been adjusted, demand si
Study 2Knowledge Base
Study 2: Inspect Demand Trends This study can help you understand exposure in your supply chain. Impact –Since demand will significantly fluctuate and spike for a period of time before reverting to the mean, accurate demand signaling for the future becomes a challenge. Misreading these signals often leads to the bullwhip effect. When shoppers decide to hoard all the toilet paper from the stores, it sends a signal through the supply chain to produce more. If the toilet paper companies decide to produce more, extra inventory is pushed into the distribution channel. Inevitably, demand will stabilize and when it happens, the stores will find themselves overstocked since customers are still exhausting their previous purchases. Action – Demand trends must be studied closely and any outliers must be treated to avoid inaccurate resource planning for the future. How – AIMMS Demand Forecasting Navigator can be used to perform time-series analysis for individual product groups. Machine learning
Study 3Knowledge Base
Study 3: Identify Optimal Sourcing Locations This study can help you anticipate shortages in your supply chain. Impact – As transportation lanes become unavailable, the availability of suppliers and components will decrease. Action – There is a strategic incentive in understanding the optimal sourcing location for specific customers, and in understanding the optimal sourcing location for the different resources. This is not to say that there is no value in having multiple supplier options, but understanding who your ideal supplier is can enable you to form the right relationship and expectations with them for the coming weeks. How – Network Design Navigator enables forcing a single source as a constraint. Once the constraint is relaxed, customers are free to choose from a multitude of suppliers. However, with the constraint enforced, the optimal source of the product can be studied while minimizing both production and distribution costs. Network Design can also be used to make certai
Study 4Knowledge Base
Study 4: Create Multiple Plans for Resource Utilization This study can help you ensure resource requirements in your supply chain. Impact –Manufacturing plants and warehouses will experience a sudden shift in resource utilization. There will most likely be a reduction in labor hours as fewer people will work in a closed, crowded environments. This could even lead to total shutdown of certain plants. Action – Study multiple resource utilization what-if scenarios and accommodate your production plan accordingly. How – S&OP Navigator allows users to simulate changes in resource capacity, labor hours, utilization, production lines and throughput rate dynamically within the application. The output is a detailed production and distribution plan that maximizes your margin. Note:If using your own bespoke app, another option is to re-adjust your objective function. We have seen this work very well in the past where a customer, during a crisis, moved from Production Maximization to Cost Min
[i]This article, originally published in 2013, comes from our Tech Blog archives. As such, some cited data is a bit dated, but we think that the story's value illustrating practical application of OR still holds up.[/i] [b]The mathematical story behind North Star Alliance’s POLARIS[/b] More than 35 million people worldwide are infected with HIV or are living with AIDS, and approximately 70% live in Sub-Saharan Africa. Mobile populations, such as long distance truck drivers, are particularly at risk of contracting and transmitting the virus. In 2007, TNT Express and the United Nations World Food Programme joined forces to form North Star Alliance (North Star) - a public-private partnership that is working to increase access to health services along major transport corridors in sub-Saharan Africa. ORTEC, a longstanding AIMMS partner, joined North Star in 2008 to design their award-winning Corridor Medical Transfer System (COMETS), which enables North Star staff to access and mo
[i]This interview, originally published in 2014, comes from our Tech Blog archives. Strategic Forest Management Model (SFMM) was among the first applications of AIMMS and is still going strong.[/i] [i]Please note, some cited data is a bit dated, but we think that the story is still worth sharing. [/i] [h3][b]Ontario’s Ministry of Natural Resources takes us through decades of effective forest management[/b][/h3][img]https://uploads-eu-west-1.insided.com/aimms-en/attachment/727ac3a2-7e96-4729-9e3a-ad6481560c2a.jpg[/img] Forest ecosystems are highly complex and influenced by a diversity of factors. Sustainable forest management is therefore an ongoing and constantly evolving process which requires an integrated approach. Government bodies, such as The [url=http://www.mnr.gov.on.ca/en/]Ontario Ministry of Natural Resources (OMNR)[/url], must conform to provincial policies and standards, while taking economical and ecological considerations into account to arrive at optimal forest manage
We spoke with Russ Philbrick, founder of Polaris Optimization Systems, about optimization solutions for energy sector customers. Find out how the AIMMS-based engine PSO (Power System Optimizer) helps the power industry make the most of renewable energy. Can you tell me a little about your background in the energy sector? I started working with Alstom back in 1999 and was responsible for the engines used to plan day-ahead unit commitment and reliability unit commitment. That project was very successful, and even today a number of power markets still run on AIMMS as the core engine, based on that work. What led you to start Polaris? In the mid-2000s, I was asked to support efforts to represent what Alstom was doing to support wind integration. I began to understand that, because of the variability and uncertainty of wind and solar, integration into the power grid presented new planning and operational challenges. With wind and solar, it’s a new kind of uncertainty and variability
[img]https://uploads-eu-west-1.insided.com/aimms-en/attachment/6a96e5f5-545f-4e13-a92f-6993665e3860.jpg[/img] Dear AIMMS communians:wink:, We started some years ago at AIMMS an investigation on how machine learning and optimization can work together, or how machine learning algorithms could help optimization modeling. During the AIMMS Summit, in addition to [url=https://community.aimms.com/post-event-materials-46/machine-learning-optimization-presentation-218]the very interesting presentation from Ger Koole[/url], we presented a 45 min workshop demoing an interesting use case on this matter - [b]Optimizing a power production plan with renewable resources[/b]. This example showcases a combination between machine learning and [url=https://download.aimms.com/aimms/download/manuals/AIMMS3LR_RobustOptimization.pdf]robust optimization[/url]. I'm sharing now our slides, as well as the model itself, written in AIMMS. This example was greatly inspired and helped by Gianmaria Leo,
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