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Примеры имитационных моделей производств и складских комплексов на сайте компании "Информ-Стандарт"

В статье приводятся примеры применения математического программирования в управлении цепью поставок в газодобыче и газопереработке. Приводятся математические постановки задач, в основном нелинейных.

«Проблема сопряжения членов экипажа авиакомпании состоит в том, что необходимо создать оптимальные пары, которые бы удовлетворяли требованиям к комплектованию рейса. Точный состав экипажа, необходимый для полета, часто варьируется в зависимости от пар, которые его формируют. В отличие от обычных подходов, Saber Long Haul Pairing Optimizer моделирует эту зависимость явно и использует сложные методы ветвей и цен. Мы используем реальные примеры авиакомпаний, чтобы показать, что наше решение приносит значительную экономию для авиационной отрасли ». "The airline crew pairing problem is to generate optimized legal anonymous pairings that cover the flight complement requirements. The exact crew composition needed on a flight often varies depending on of the pairings that cover it. Different from the conventional approaches, Sabre Long Haul Pairing Optimizer models this dependency explicitly and uses sophisticated Branch and Price techniques. We use real airline examples to show our solution brings considerable savings to the airline industry."

Corporate Finance Models Investment Models Production Models Distribution Models Purchasing Models HR and Scheduling Models

Описание и примеры использования эффективной эвристики для решения задачи коммивояжера "LKH is an effective implementation of the Lin-Kernighan heuristic for solving the traveling salesman problem." Computational experiments have shown that LKH is highly effective. Even though the algorithm is approximate, optimal solutions are produced with an impressively high frequency. LKH has produced optimal solutions for all solved problems we have been able to obtain; including a 85,900-city instance (at the time of writing, the largest nontrivial instance solved to optimality). Furthermore, the algorithm has improved the best known solutions for a series of large-scale instances with unknown optima, among these a 1,904,711-city instance (World TSP)."

Применение имитационного моделирования в Anylogic для оптимизации складских процессов фармацевтической компании. Видео с конференции Anylogic 2013

"Ingram Micro, the world's largest technology distributor operates in a high volume low margin environment. The company started its Business Intelligence & Analytics practice in North America (NA) about 6 years ago. Since then the group has built and deployed a scalable highly innovative price optimization engine, imprime™, for NA's $8B spot business, a set of analytics apps, imsmart™ for its internal sales organization and an integrated digital marketing platform, intelligence.ingrammicro.com, to run data-driven marketing campaigns to its customers and end customers. Since 2011, these products and analytical programs have yielded a cumulative benefit of $1.12 Billion of incremental product revenue and $28M of incremental gross profit. These solutions have been effective in driving profitable growth and are scalable. Our next steps are to continue to drive these best practices within the other regions outside NA and continue our sales enablement activities."

"We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand."

"The planning of spare engines and engine parts is a challenging and important task for airlines to seamlessly support flying and engine repair operations. Engines are expensive and critical assets that make this problem important from the financial and operational perspectives. In this talk, we present an application of a simulation-based approach utilized for planning the ownership level of spare engines and engine parts required to achieve a target service level or out-of-service (OTS) aircraft performance metrics. Our application considers both single and multi-location settings as well as two levels of modeling: a higher level where the repairable components are the engines as a whole, and a lower level that focuses on the repair of the individual engine parts. "

"The Mayo Clinic Department of Orthopedic Surgery was facing low utilization of their operating rooms (ORs) for spine surgical procedures, combined with fluctuating empty days and days with overtime to complete scheduled surgeries. Extremely long days ended up being unsafe days with increased provider fatigue and higher likelihood of errors. Investigation revealed the cause to be inaccurate estimation of surgical and non-surgical duration and scheduling of surgeries rather than limited surgery demand. Existing scheduling optimization research in the literature was inadequate, as they provided a single "optimal" solution. Often, the single optimal solution was not satisfactory to the patients and the providers. The team 1) conducted descriptive research using historical data to identify clinical and operational factors; 2) developed and implemented predictive models for the duration of surgical and nonsurgical times in the OR based on these factors; and 3) developed and implemented a prescriptive scheduling search algorithm that suggests multiple slots for a given surgery thereby providing flexibility while ensuring high probability of completion of surgical day without much overtime (completion closer to 5 pm)."

"We describe a software application that enables owners of generation output from a virtualized Federal Columbia River Power System to safely operate the system while also shaping the generation to meet their energy and economic needs. The application, known colloquially as The Optimizer, employs modern Operations Research techniques to convert a highly non-linear problem into a linear one so as to create a robust solution for the entire, six-dam system on an hourly basis; over a ten-day time horizon; within seconds to minutes. The tool permits two operators to manage and optimize the entire generation portfolios for nine utilities simultaneously in a very stringent time frame around the clock, and enables planners to ensure that the operation of the river meets all of the requirements for flood control, fish management, electrical reliability, safe dam operation, and recreation under high degrees of uncertainty. As a result, the Optimizer allows utilities to integrate renewable, environmentally friendly wind and solar generation into their resource portfolio with hydro generation and empowers fast decision making and adaptation to rapidly changing conditions."

"We present the problem of scheduling crash tests for new vehicle programs at Ford. We developed a completely custom-made scheduling system that transforms a labor-intensive scheduling process relying on high levels of expertise, to a more automated one that utilizes optimization and institutionalizes expert knowledge. Our system enables engineers and managers to consider multiple scheduling scenarios, using efficient interfaces to specify problem instances and efficient methods to solve them."

"Long term infrastructure planning of locomotive service facilities is vital to the efficiency of the railroad. We developed a large-scale optimization model that integrates decisions on (i) location, capability, and capacity of fixed facilities, (ii) home location and routing plan of movable facilities, and (iii) assignment of a variety of service demands. A decomposition-based solution framework was developed and shown to bring significant economic benefits in full-scale implementations."

Пример имитационной модели для анализа вариантов устранения заторов транспортных средств в порту Chennai

Имитационное моделирование как инструмент проектирования инфраструктуры терминально-логистического центра Белый Раст

Решение задачи проектирования логистической сети дистрибьютора спортивных товаров с помощью Anylogic

"Компания "Procter & Gamble (P & G) Fabric Care" контролирует широкий спектр продуктов, включающий такие "бытовые" бренды, как Tide, Dash, и Gain. Мы описываем новую аналитическую структуру, которая использует визуальные статистические инструменты и передовые методы математического программирования, помогая P & G определять уровни ингредиентов и архитектуру продуктов и процессов, чтобы создать одни из лучших в мире продуктов для стирки. Эта структура обеспечивает целевые потребительские преимущества, позволяя сэкономить миллионы долларов". "The Procter & Gamble (P&G) Fabric Care business oversees a broad portfolio of products, including household brands such as Tide, Dash, and Gain. We describe a novel analytical framework that uses visual statistical tools and advanced mathematical programming methods, helping P&G determine ingredient levels and product and process architecture to create some of the world's best laundry products. This framework has provided targeted consumer benefits while enabling cost savings in the order of millions of dollars."