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The fourth annual AnyLogic Conference will continue to prove the power of collaboration, not only with the AnyLogic team, but other experienced model developers. Don’t miss the opportunity to see how AnyLogic is being applied across multiple industries, experience innovative ideas and expand your vision of AnyLogic software. For your convenience, the two day event falls directly after the INFORMS Annual Meeting. INFORMS attendees should plan to stay Wednesday evening and join us for Wednesday afternoon and Thursday morning presentations. The Conference will be held in the Trumbauer Ballroom of Le Meridien Philadelphia Hotel. The hotel is less than a half mile from the INFORMS venue. In addition to the Conference presentations and multiple networking events, the AnyLogic Conference will offer an optional training course Wednesday morning prior to Conference registration and presentation proceedings.

Equipment part breakdown in the Intel factories, as in many factories is inevitable. These failures typically cause capacity constraints and ultimatley cost the corporation time and money. Equipment parts can be repaired locally or may require shipping to the vendor for repair. Since the repair loop takes significant time, it is necessary to have extra spare parts on-hand to keep the equipment running while broken parts are repaired. It is pertinent to avoid overbuying of the spare parts, as they are very expensive. Intel needed a model of the repair loop to increase the visibility of problems such as broken parts accumulating at the vendor repair center and sites over purchasing spare parts. At the AnyLogic Conference 2014, Victor Chang, Software Engineer at Intel presents an AnyLogic simulation that was developed to model the complexities and variability

“Cesarean delivery” is a method of childbirth in which a surgeon cuts through the pregnant woman’s abdomen and uterus to deliver the baby. The more natural method of childbirth is called “vaginal delivery”, in which the baby leaves the mother’s uterus through her vaginal canal. Ideally, cesarean delivery would only be used when vaginal delivery would endanger the life or health of the child or mother, because cesarean delivery involves major abdominal surgery that is accompanied by much greater risks for both mother and child than vaginal delivery. Cesarean delivery also costs about 50 percent more. Over the last 40 years, the U.S. rate of cesarean delivery has increased dramatically.

There are many cases of simulation modeling in healthcare. Application areas can vary, from process optimization in hospitals to macrolevel agent-based epidemiology models. Due to its multimethod nature, AnyLogic allows models to be built at various abstraction levels. A good illustration of how researchers and consultants can apply the same tool to different problems is the three models built by the Stockholm County Health Administration in Sweden. The models included macro, meso, and micro abstraction level applications in healthcare simulation. The microlevel model simulated the maternity ward in a hospital that was currently under construction. The purpose of the model was to support discussions related to which resources, capacity, and work methods were required in the new ward. One relevant discussion was whether to keep mother and child in the same room during their entire stay or to have dedicated rooms for antenatal care, delivery, and postnatal care.

While the extensive rail library was a key reason that CSX chose AnyLogic as its general purpose simulation tool for the Network Modeling, Operations Research, and Process Excellence groups, the other libraries and methods have added significant value as well. In fact, the first major project where AnyLogic was used did not utilize the rail library. After reviewing the problem in more detail, a discrete-event simulation model was built to help managers studying train throughput. The model simulated the demand of empty trains from five coal mines, as well as the fulfillment of the demand. A supply-chain-like network model was created, which implemented logic to depict the demand, supply and staging of empty trains. The trains were modeled as moving entities across the network. By varying values of relevant parameters, users can infer the impacts of different factors to the train throughput (i.e. siding staging capacity and loading speeds at the coal mines). The model provides a way for decision makers to gain insight into the system to help identify the maximum possible throughput. The objective was to identify the best operational/capital strategy to handle the increased business.

Growing competition and a high demand for individual and highly sophisticated products in combination with shorter innovation cycles is leading to a rising number of ramp-ups especially in Small batch production. Daily challenges such as late changes and missing maturity of high Technology products and processes create significant risks. Since 2012 a group of 14 European companies and research institutes have developed novel planning and control solutions in the European public funded project ARUM (Adaptive Production Management; www.arum-project.eu) to overcome those challenges in production ramp-up. The validation of the developed control strategies and their implementation into novel planning and scheduling solutions within a realistic industrial environment is mandatory and several industrial use Cases have been selected, e.g. an Airbus system installation flowline in Hamburg.

Conaprole, the biggest dairy production company in Uruguay, produces more than 150 SKUs in their ice cream plant, using five production lines, and up to five different packaging configurations for each line. The company plans ice cream production on a 12-month rolling basis as part of the Sales & Operations Planning process, and the demand plan varies a lot due to seasonality. The factory management needs to prepare the production lines for the peak season during the low season, taking into account product shelf life and the warehouse’s freezing cameras’ capacity and costs. In addition, management found it very difficult to reschedule quickly their detailed plans due to the challenges they faced including bottlenecks, production process constraints, and staff turnover.

An initiative of the Department of Mechanical and Industrial Engineering at the University of Toronto, the Centre for Research in Healthcare Engineering (CRHE) is a response to the immediate and compelling desire for efficiency and quality improvements in the Canadian health care system. CRHE is committed to both research and education in the field of healthcare delivery. From an academic perspective, their work falls into two categories: research and service. A study completed by the Commenwealth Fund Commision (http://www.commonwealthfund.org/) ranked Canada very low in the categories of quality of health care, access to health care, efficiency, equity and expenditures. This study among others, prompted the CRHE to dedicate a research project that would test potential changes in Canadian healthcare policy that could increase the quality of patient care.

Join us for the“Delivery Fleet Optimization with GIS” webinar May 28th at 12noon CST for a step-by-step process of building a supply chain model with real-time GIS features and optimization capabilities. Webinar Objectives: - Build a regional supply network - Integrate real-time GIS technology - Optimize the delivery fleet assets - Enrich the simulation by integrating additional data sources Webinar attendees will receive the model source code, all necessary associated files and how-to-build written instructions.

Inland waterway logistics have their own special aspects that include high seasonality due to weather conditions, long delivery times, restricted door-to-door delivery capabilities, and more. On the other hand, river transport is cheap, environmentally friendly, and allows large amounts of goods to be carried. Barges are widely used to carry both bulk cargo and containers. Simulation can help companies justify switching to river logistics by comparing water transportation to other means in terms of costs, throughput, or shipping time. Shipping operators can use simulation to optimize routing and fleet management policies, taking into account uncertainties, breakdowns, bottlenecks, weather conditions, and water level. The following is a success story of river transport simulation-based optimization in South America.