With COVID 19 prevalent in the USA and the world, efficient social distance seating became an option for sports venues. The social distancing constraint requires six feet between individuals when the game has live audiences. Depending on the seats' dimensions, this would translate to a certain number of empty rows and empty seats in a row between the individuals. As a result, it is not possible to seat all ticket holders with safe social distancing. Hence, it necessitates reassigning spectators to games. An important feature of this problem is that season tickets are grouped by family, and only a safe distance between two different families needs to be maintained. Members of the same family can sit next to each other. Therefore, a large family needs fewer empty seats per person to maintain social distancing. A football season has about six home games. If priority is given to larger families for all the games, then many people can watch the live games, but the outcome will be highly unfair. Striking a good balance between efficiency and fairness is a nontrivial task.

Information technology (IT) has been the driver of increased productivity in the manufacturing and service sectors, bringing real-time information to decision makers and process owners to improve process behavior and performance. Thus, organizations have invested heavily in training their employees to use IT in a disciplined, scientific way to make process improvements. This has spawned such popular initiatives as Six Sigma, yielding significant returns, but at considerable investment in training in statistical-analysis and decision-making tools. Can aspects of the decision-making process be automated, letting humans do what they do best (create, define, and measure) and machines (e.g., learning machines) do what they do best (analyze)? We propose an automated intelligent manufacturing system (AIMS) for analysis and decision making that mines real-time or historical data, and uses statistical and computational-intelligence algorithms to model and optimize enterprise processes. The algorithms employed involve a regression support vector machine (SVM) for model construction and a genetic algorithm (GA) for model optimization. Performance of AIMS was compared to Six-Sigma-trained teams employing statistical methodologies, such as design of experiments (DOE), to improve a simulated manufacturing operation, a three-stage TV-manufacturing process, where the objectives were to maximize yield, minimize cycle time and its variation, and minimize manufacturing costs, which were affected by conflicting defects and their causes. AIMS generally outperformed the teams on the above criteria, required relatively little data and time to train the SVM, and was easy to use. AIMS could serve as a productivity springboard for enterprises in existing and emergent technologies, such as nanotechnology and biotechnology/life sciences, where environment and miniaturization may make human monitoring and intervention difficult or infeasible.

Designing the link topology and selecting capacities in a backbone network of a communication system involve complex tradeoffs between investment and operating costs and service considerations such as network reliability and vulnerability, delays, and blocking. Incorporating all these design criteria simultaneously in a comprehensive model results in a large-scale, nonlinear, discrete optimization problem that is intractable. This paper proposes an alternate optimization-based methodology to generate several cost-effective backbone network designs with varying cost and performance characteristics. Network planners can use this method together with detailed performance evaluation techniques to select a design that achieves the proper balance between conflicting objectives. To generate different configurations, the method parametrically varies a set of hop constraints that restrict the number of links over which messages can be transmitted. Reducing the maximum number of hops increases the number of alternate routes but incurs higher total cost for the communication system. For a given set of hop constraints, we develop a Lagrangian-based algorithm to identify a cost-minimizing network design that satisfies all internode traffic requirements. Our extensive computational tests using randomly generated networks demonstrate that, even for relatively large problems, the method identifies good heuristic solutions and tight lower bounds that confirm the near-optimality of the selected designs. Using a 25-node example, we illustrate how the model can be used to evaluate the cost versus performance tradeoff. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

This paper studies the problem of assigning capacities to the links in a backbone network and determining the primary routes used by messages for each origin-destination communicating pair in the network. The topology of the backbone network is assumed to be known and the end to end traffic requirements are given. The problem is to find the least cost design where the system costs are composed of connection costs which depend on link capacities and queueing costs which are incurred by users due to the limited capacities of links. The goal is to determine the routing and link capacities simultaneously. The problem is formulated and lower bounds are obtained by Lagrangean relaxation embedded in a subgradient optimization procedure. Cut constraints which are redundant in the original formulation are introduced, they improve the lower bounds. A heuristic method based on the Lagrangean solution is described. Extensive computational results are reported. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.