Xerox has invented, tested, and implemented a novel class of operationsresearch-based productivity improvement offerings, marketed as Lean Document Production (LDP), for the $100 billion printing industry in the United States. The software toolkit that enables the optimization of print shops is data-driven and simulationbased. It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end user and then automatically generating complex simulation models that are used to determine improved design and operating policies. This chapter describes the addition of another layer of automation consisting of simulation-based optimization using simulated annealing and greedy search techniques that enable the search of a large number of design alternatives in the presence of operational and cost constraints. The greedy search procedure quickly determines an acceptable solution in a web-based online application environment. The simulated annealing technique is more time consuming and is performed offline. The results of the application of this approach to real-world problems are described.

Xerox has invented, tested, and implemented a novel class of operations-research-based productivity improvement offerings that has been described in Rai et al. (2009) and was a finalist in the 2008 Franz Edelman competition. The software toolkit that enables the optimization of print shops is data-driven and simulation based. It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described. INTRODUCTIONXerox has invented, tested, and implemented a novel class of operations-research-based productivity improvement offerings, marketed as Lean Document Production (LDP), for the $100 billion printing industry in the United States. The LDP software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points for the print shops. In this paper, we describe the addition of another layer of automation to the LDP toolkit consisting of a simulated annealing based simulation optimization that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution for the print production environment. The printing industry is highly fragmented with thousands of print shops that are geographically distributed. This approach lends itself to being utilized for optimizing print shops across multiple geographies by users less skilled in the art of simulation modeling and optimization thereby allowing unprecedented scalability of a simulation-based optimization toolkit to a wide user-base. Users are able to utilize the simulation-based optimization toolkit to make complex design and operational decisions and develop optimized designs without having to actually go through the arduous task of building the simulation models and the associated optimization framework around it.

Ranjit Kumar Ettam Xerox Business ServicesLevel 5 A viator Building ITPB Bangalore, KA 560066, India For simulation modeling, what-if analysis and optimization studies of many service and production operations, demand models that are reliable statistical representations of current and future operating conditions are required. Current simulation tools allow demand modeling using known closed-form statistical distributions or raw demand data collected from operations. In many instances, demand data cannot be described by known closed-form statistical distributions and the raw data collected from operations is not representative of future demand. This paper describes an approach to demand modeling where historical demand data collected over a finite time period is combined with user-input using two tier bootstrapping to produce synthetic demand data that preserves the statistical distribution of the original data but has overall metrics such as volume, workflow mix and individual task and job sizes that represent projected future state scenarios. When the customer demand data follows highly non-normal distributions, a modified procedure is presented.978-1-4673-9743-8/15/$31.00 ©2015 IEEE