91,164 research outputs found
Extreme-Point-based Heuristics for the Three-Dimensional Bin Packing problem
One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points at which to place the items inside the bins, because the performance of exact and heuristic solution methods is actually strongly influenced by the choice of a placement rule. We introduce the extreme point concept and present a new extreme point-based rule for packing items inside a three-dimensional container. The extreme point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new extreme point rule is also used to derive new constructive heuristics for the three-dimensional bin-packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the two-dimensional bin-packing problem, outperform those specifically designed for the proble
Which heuristics can aid financial-decision-making?
© 2015 Elsevier Inc. We evaluate the contribution of Nobel Prize-winner Daniel Kahneman, often in association with his late co-author Amos Tversky, to the development of our understanding of financial decision-making and the evolution of behavioural finance as a school of thought within Finance. Whilst a general evaluation of the work of Kahneman would be a massive task, we constrain ourselves to a more narrow discussion of his vision of financial-decision making compared to a possible alternative advanced by Gerd Gigerenzer along with numerous co-authors. Both Kahneman and Gigerenzer agree on the centrality of heuristics in decision making. However, for Kahneman heuristics often appear as a fall back when the standard von-Neumann-Morgenstern axioms of rational decision-making do not describe investors' choices. In contrast, for Gigerenzer heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. Gigerenzer challenges Kahneman to move beyond substantiating the presence of heuristics towards a more tangible, testable, description of their use and disposal within the ever changing decision-making environment financial agents inhabit. Here we see the emphasis placed by Gigerenzer on how context and cognition interact to form new schemata for fast and frugal reasoning as offering a productive vein of new research. We illustrate how the interaction between cognition and context already characterises much empirical research and it appears the fast and frugal reasoning perspective of Gigerenzer can provide a framework to enhance our understanding of how financial decisions are made
Runtime Scheduling, Allocation, and Execution of Real-Time Hardware Tasks onto Xilinx FPGAs Subject to Fault Occurrence
This paper describes a novel way to exploit the computation capabilities delivered by modern Field-Programmable Gate Arrays (FPGAs), not only towards a higher performance, but also towards an improved reliability. Computation-specific pieces of circuitry are dynamically scheduled and allocated to different resources on the chip based on a set of novel algorithms which are described in detail in this article. These algorithms consider most of the technological constraints existing in modern partially reconfigurable FPGAs as well as spontaneously occurring faults and emerging permanent damage in the silicon substrate of the chip. In addition, the algorithms target other important aspects such as communications and synchronization among the different computations that are carried out, either concurrently or at different times. The effectiveness of the proposed algorithms is tested by means of a wide range of synthetic simulations, and, notably, a proof-of-concept implementation of them using real FPGA hardware is outlined
Towards the Design of Heuristics by Means of Self-Assembly
The current investigations on hyper-heuristics design have sprung up in two
different flavours: heuristics that choose heuristics and heuristics that
generate heuristics. In the latter, the goal is to develop a problem-domain
independent strategy to automatically generate a good performing heuristic for
the problem at hand. This can be done, for example, by automatically selecting
and combining different low-level heuristics into a problem specific and
effective strategy. Hyper-heuristics raise the level of generality on automated
problem solving by attempting to select and/or generate tailored heuristics for
the problem at hand. Some approaches like genetic programming have been
proposed for this. In this paper, we explore an elegant nature-inspired
alternative based on self-assembly construction processes, in which structures
emerge out of local interactions between autonomous components. This idea
arises from previous works in which computational models of self-assembly were
subject to evolutionary design in order to perform the automatic construction
of user-defined structures. Then, the aim of this paper is to present a novel
methodology for the automated design of heuristics by means of self-assembly
TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem
Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one
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