Adaptive Cross-Layer Distributed Energy-Efficient Resource Allocation Algorithms for Wireless Data Networks

The issue of adaptive and distributed cross-layer resource allocation for energy efficiency in uplink code-division multiple-access (CDMA) wireless data networks is addressed. The resource allocation problems are formulated as noncooperative games wherein each terminal seeks to maximize its own energy efficiency, namely, the number of reliably transmitted information symbols per unit of energy used for transmission. The focus of this paper is on the issue of adaptive and distributed implementation of policies arising from this approach, that is, it is assumed that only readily available measurements, such as the received data, are available at the receiver in order to play the considered games. Both single-cell and multicell networks are considered. Stochastic implementations of noncooperative games for power allocation, spreading code allocation, and choice of the uplink (linear) receiver are thus proposed, and analytical results describing the convergence properties of selected stochastic algorithms are also given. Extensive simulation results show that, in many instances of practical interest, the proposed stochastic algorithms approach with satisfactory accuracy the performance of nonadaptive games, whose implementation requires much more prior information

Integer programming for building robust surgery schedules.

This paper proposes and evaluates a number of models for building robust cyclic surgery schedules. The developed models involve two types of constraints. Demand constraints ensure that each surgeon (or surgical group) obtains a specific number of operating room (OR) blocks. Capacity con- straints limit the available OR blocks on each day. Furthermore, the number of operated patients per block and the length of stay (LOS) of each operated patient are dependent on the type of surgery. Both are considered stochas- tic, following a multinomial distribution. We develop a number of MIP-based heuristics and a metaheuristic to minimize the expected total bed shortage and present computational results.Constraint; Demand; Distribution; Expected; Heuristic; Integer programming; Model; Models; Resource leveling; Surgery scheduling;

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations. Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency