Combinatorial optimisation of a large, constrained simulation model: an application of compressed annealing

Simulation models are valuable tools in the analysis of complex, highly constrained economic systems unsuitable for solution by mathematical programming. However, model size may hamper the efforts of practitioners to efficiently identify the most valuable configurations. This paper investigates the efficacy of a new metaheuristic procedure, compressed annealing, for the solution of large, constrained systems. This algorithm is used to investigate the value of incorporating a sown annual pasture, French serradella (Ornithopus sativa Brot. cv. Cadiz), between extended cropping sequences in the central wheat belt of Western Australia. Compressed annealing is shown to be a reliable means of considering constraints in complex optimisation problems in agricultural economics. It is also highlighted that the value of serradella to dryland crop rotations increases with the initial weed burden and the profitability of livestock production.combinatorial optimisation, crop rotation, simulated annealing, Research Methods/ Statistical Methods, C63, Q15,

A primer on implementing compressed simulated annealing for the optimisation of a constrained simulation model in Microsoft Excel

This short paper provides a simple introduction on how a simulation model implemented in Microsoft Excel® can be optimised using Visual Basic for Applications (VBA) programming and the compressed simulated annealing algorithm (Ohlmann et al., 2004; Ohlmann and Thomas, 2007). The standard simulated annealing procedure enters as a special case. Practical advice for determining the parameters that guide the stochastic search process in an annealing algorithm is also given.Research and Development/Tech Change/Emerging Technologies,

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks