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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:
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?
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[
OnLocation is an energy consulting company based in the Washington, D.C. area. They have extensive experience using integrated energy modeling to assess the costs and benefits of energy policies and technologies and provide insights into potential implementation challenges. OnLocation’s primary integrated modeling tool is The National Energy Modeling System (NEMS), which is the most trusted integrated energy model used for projections and policy analysis in the United States. Recently, we hosted a webinar to hear about OnLocation’s REStore model, a submodule of NEMS built in AIMMS. REStore represents electricity storage and the temporal pattern of renewable generation. Get the inside story below!Renewable energy on the riseAccording to the U.S. Department of Energy, solar installations in the U.S. have grown 35-fold since 2008. The average cost of solar photovoltaic (PV) panels has dropped nearly 50% since 2014, as well. This growth in renewables is posing new challenges for the electr
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]
Adding leading time, phasing in capital and economic life to a generation capacity expansion planning model
Good day, I have an electricity capacity expansion planning model which was developed in AIMMS and is working fine. However, now I need to add the following:Lead times, which differ for each technology. Currently, the model assumes that if a technology is selected by the model, it can produce electricity as soon as the model makes it available for capacity allocation. I need this to change and the model should make the technology’s capacity available after the lead time has expired. Phasing in capital, which also differ for each technology. The current model assumes that the capital for a chosen technology is payable immediately. The phasing in capital are connected to lead times. For example if a technology has a lead time of 2 years, the phasing in capital is also over 2 years, say 90% of capital is payable in year 1 and 10% in year 2. I need the capital for each technology to be phased in according to the phasing in capital percentages. Life of plant (Economic life), which also dif
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
Study 5Knowledge Base
Study 5: Refine Capacity Smoothing This study can help you ensure resource requirements in your supply chain. Impact – As certain resources are forced to close, other resources will experience an increased load. Companies may not be able to adequately ramp up capacity and lose sales. Action – Inventory must be kept in anticipation of resource unavailability in the future. At the same time, over-utilization must be reduced to not exhaust the resources. How –AIMMS S&OP Navigator enables users to perform capacity smoothing. This incurs a penalty on over-utilization and tries to minimize it by ensuring that inventory has been built up in anticipation of future shortages. It also suggests alternative distribution options for obtaining the product in case the resource in question is unavailable. Demo –Capacity Smoothing in S&OP Navigator This article is part of the series Immediate Supply Chain Actions to Take Amid COVID-19.
Dear all, I write on behalf of baobab soluciones, an AIMMS partner based in Madrid. Since the onset of the pandemics we have decided to support organizations and public authorities and offer our solution development services pro-bono for crisis relief projects. We were thinking route optimisation, workforce optimisation, workload management, load balancing between centres, for healthcare, quick response and critical supply chains. Quick, technically simple projects that will help take the most out of our exhausted common resources. So if you encounter a situation where we can be of assistance, please do not hesitate to contact us. Thank you very much, Julio
Supporting the Dutch Healthcare System with AIMMS-based Optimization During the COVID-19 PandemicBlog
This article is based on an interview with Erwin Hans, Professor of Operations Management in Healthcare at the University of Twente and Co-Founder of CHOIR and Rhythm B.V. Erwin is helping care givers address the challenge of critical care capacity and is looking for motivated students with AIMMS know-how to join the effort! AIMMS and I have a long-standing relationship. As a former student of applied mathematics at the University of Twente, an institution where I now teach, I completed my graduation project under the supervision of AIMMS’ founder, Johannes Bisschop. Jan, as we call him, was my thesis supervisor. My path in OR started at the Energy Research Foundation in the late 90s. During my time here, I conducted my master’s research on the liberalization of the European electricity market. Specifically, I developed an AIMMS-based model to understand how electricity transport, post liberalization, would impact prices. After completing my research, I took on a PhD position in logis
Came across a nice set of (3) Linear Programming exercises from the Erasmus University of Rotterdam (academic year 2016/2017) that might interest you. https://www.studeersnel.nl/nl/document/erasmus-universiteit-rotterdam/lineair-programmeren/verplichte-opgaven/exercise-set-aimms-16-17/999425/view
Join the next Robust Optimization Webinar - ROW Talk! Date: WEDNESDAY, May 20, 2020 Time: 9.00-10.00 Boston time, 15.00-16.00 Dutch time, 21.00-22.00 Singapore time. Featured speaker: Dick den Hertog (Tilburg University, Tilburg, The Netherlands) Panelists: Aharon Ben-Tal (Technion, Haifa, Israel) Dimitris Bertsimas (MIT, Cambridge, USA) Frans de Ruiter (CQM, Eindhoven, The Netherlands) The three panelists will react on Dick den Hertog's topic, How to improve the adoption of Robust Optimization by practitioners, followed by a plenary discussion. Zoom link: https://ethz.zoom.us/j/451585177 (for more joining options, see below) Join Zoom Meetinghttps://ethz.zoom.us/j/451585177 Meeting ID: 451 585 177 Dial by your location +1 786 635 1003 US (Miami) +1 213 338 8477 US (Los Angeles) +1 253 215 8782 US +1 312 626 6799 US (Chicago) +1 470 250 9358 US (Atlanta) +1 646 518 9805 US (New York) +1 669 219 2599 US (San Jose) Meet
[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
Some of you may find this paper interesting - published 2013 but still relevant! “Developing Constraint Programming Applications with AIMMS” by Prof. Willem-Jan van Hoeve of Carnegie Mellon University Download the full paper in PDF from https://www.andrew.cmu.edu/user/vanhoeve/papers/aimms_cp_app.pdf Abstract. We describe the constraint programming interface of the optimization modeling systems AIMMS. First, we present the modeling language for basic constraint programming and advanced scheduling constructs, and specify how search can be controlled. Then we provide three example applications that illustrate how AIMMS can be used for developing constraint programming applications.
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,
1. The next Analytics for a Better World Webinar (ABW-W) is on Wednesday, October 28, 2020, EDT 12:00 PM (Boston time), CET 5:00 PM (Amsterdam time). Speaker: Jean-Philippe Vial (Geneva, Switzerland)Title: Analytics for the Design of Gravity-Driven Water Distribution Systems for Poor Rural Communities: the NeatWork Tool.Abstract: NeatWork is an optimization and simulation tool for the design of water-distribution systems with the aim of providing clean water to poor rural communities. The management of such systems must be cheap and simple, a requirement which prohibits power-driven equipment, such as pumps, and man-operated devices, such as regulators. Therefore, the physical system must be driven by gravity only. It must also be endowed with self-regulation to ensure flow invariance despite stochastic intermittent water withdrawals by independent users. The formulation as a least-cost design problem belongs to the realm of two-stage stochastic optimization, in which the second stage
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