Developers

The Magic of notebooks

Traditionally applications were developed and released in long development cycles. Any changes were compiled and prioritized for the next release. With the advent of cloud computing the cycle was shortened as it alleviated the need to install a new release on many end machines but was still a similar process. In the last few years, emerging Read More »

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Subway Card Loading Optimization

No one likes to waste time having to stop by the vending machine to reload their subway card and risk being stuck in line when the next train arrives. Have you ever wondered what would be the best strategy to reload your subway card? How do you minimize the number of times you have to do Read More »

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Data Science Revolution

We recently recruited a highly talented and accomplished young graduate. Other than summer internships, Opalytics will be his first job. We had some concerns during the recruitment process, as the graduate had the sort of background that provided a lot of choices – solid Operations Research education, programming experience, and bilingual. Between relocating, company size, Read More »

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Rapid Deployment of Optimization developed with OPL

The Opalytics Cloud Platform was developed in order to create applications that use advanced analytics solvers. The solvers cover different capabilities including simulation, machine learning and optimization. Our goal is to enable fast and effective deployment of analytics so that business users can participate in the prototyping stage and use it in their work as Read More »

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Combining Machine Learning and Optimization for Better Decisions

Most analytics applications in supply chain and operations involve some form of forecasting and optimization. These are also called more generally predictive and prescriptive analytics. One such example is price optimization. This process analyzes historical data to determine how price influences demand and then develops an effective pricing strategy. Price optimization is often performed with Read More »

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Treat Your Analytics Practice to Instant Applications

In our interactions with many companies and their analytics teams, we note that they need to solve the unique problem they have and, therefore, need a specific analysis for an industry-specific, company-specific, and/or situation-specific question. This requires a combination of services and software to support them. Many analytics tools reside on the analyst’s machine or Read More »

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Three Steps towards Rapid Custom Analytics Deployment

Most analytics applications in supply chain and operations involve some form of forecasting and optimization. These are also called more generally predictive and prescriptive analytics. One example is inventory optimization that relies on forecast of future demand and its variability to calculate safety stock. Similarly, network design requires forecast of future demand (sometime as far Read More »

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Avoid the tower of Babel: Optimization with Python

In The Py in Opalytics we discussed  why we decided to adopt Python for the solver interface.  In a recent blog post Why Python for MIP?, our CSO,  Peter Cacioppi,  elaborates  on why Operations Research (OR) practitioners need to move beyond proprietary modeling languages, and use Python for Mixed Integer Programming (MIP). He mentions four Read More »

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The Py in Opalytics

A few years ago we saw the need for new more flexible supply chain analytics on a modern platform. We started developing our Network Risk application as well as Multi Echelon Inventory Optimization, Network Design and Routing. Along the way, we created the Opalytics cloud platform that enables fast deployment of solvers and applications.  The platform provides Read More »

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An easy-to-use data library for developing mathematical engines

We recently released ticdat 0.2.0 on pypi.  ticdat is an easy-to-use, lightweight, relational, data library. It provides a simple interface for defining a data schema, and a factory class for creating ticdat data objects that confirm to this schema. It is primarily intended to simplify the process of developing proof-of-concept mathematical engines that read from one Read More »

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