A computational analysis of lower bounds for big bucket production planning problems

In this paper, we analyze a variety of approaches to obtain lower bounds for multi-level production planning problems with big bucket capacities, i.e., problems in which multiple items compete for the same resources. We give an extensive survey of both known and new methods, and also establish relationships between some of these methods that, to our knowledge, have not been presented before. As will be highlighted, understanding the substructures of difficult problems provide crucial insights on why these problems are hard to solve, and this is addressed by a thorough analysis in the paper. We conclude with computational results on a variety of widely used test sets, and a discussion of future research

Transmit Signal and Bandwidth Optimization in Multiple-Antenna Relay Channels

Transmit signal and bandwidth optimization is considered in multiple-antenna relay channels. Assuming all terminals have channel state information, the cut-set capacity upper bound and decode-and-forward rate under full-duplex relaying are evaluated by formulating them as convex optimization problems. For half-duplex relays, bandwidth allocation and transmit signals are optimized jointly. Moreover, achievable rates based on the compress-and-forward transmission strategy are presented using rate-distortion and Wyner-Ziv compression schemes. It is observed that when the relay is close to the source, decode-and-forward is almost optimal, whereas compress-and-forward achieves good performance when the relay is close to the destination.Comment: 16 pages, 10 figure

Convex Relaxations for Gas Expansion Planning

Expansion of natural gas networks is a critical process involving substantial capital expenditures with complex decision-support requirements. Given the non-convex nature of gas transmission constraints, global optimality and infeasibility guarantees can only be offered by global optimisation approaches. Unfortunately, state-of-the-art global optimisation solvers are unable to scale up to real-world size instances. In this study, we present a convex mixed-integer second-order cone relaxation for the gas expansion planning problem under steady-state conditions. The underlying model offers tight lower bounds with high computational efficiency. In addition, the optimal solution of the relaxation can often be used to derive high-quality solutions to the original problem, leading to provably tight optimality gaps and, in some cases, global optimal soluutions. The convex relaxation is based on a few key ideas, including the introduction of flux direction variables, exact McCormick relaxations, on/off constraints, and integer cuts. Numerical experiments are conducted on the traditional Belgian gas network, as well as other real larger networks. The results demonstrate both the accuracy and computational speed of the relaxation and its ability to produce high-quality solutions