Symbolic Configuration for Interactive Container Ship Stowage Planning

Low‐cost containerized shipping requires high‐quality stowage plans. Scalable stowage planning optimization algorithms have been developed recently. All of these algorithms, however, produce monolithic solutions that are hard for stowage coordinators to modify, which is necessary in practice owing to exceptions and operational disruptions. This article introduces an approach for modifying a stowage plan interactively without breaking its constraints. We focus on rearranging the containers in a single‐bay section and show two approaches for providing complete and backtrack‐free decision support using symbolic configuration techniques, one based on binary decision diagrams and one based on DPLL solvers. We show that binary decision diagrams can be used to solve real‐world sized instances of a single bay, and that search‐based solvers can be used to solve simplified instances going beyond a single bay.     

Using evolutionary search to optimise the energy consumption for natural gas liquefaction

Liquefaction of natural gas is a highly energy intensive process. Therefore its energy optimisation is an important matter. Sequential Quadratic Programming (SQP) will often be the preferred method for solving these optimisation problems, but there are some obstacles due to the fact that they use the local shape of the functions. Evolutionary search could be a way of getting around these problems and in this work, evolutionary search methods were combined and adapted to the liquefaction problem. The research focused on how to efficiently use all the function evaluations to obtain robust convergence, leading to the concept of diversity; and secondly how to deal with the infeasible individuals. Tests were performed on a benchmark function to assess the effect of different methods from the literature and the parameters which control them. Finally an application to the process simulator showed satisfactory results which were less than 5% from the assumed optimal solution.     

Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories

We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike intervals. A method for efficiently generating inhomogeneous realizations of the proposed Markov process is given, numerical methods for solving the population equation are presented, and an expression for the first-order interspike interval correlation is derived. Further, we show that the full five-dimensional master equation for a conductance-based integrate-and-fire neuron with spike-frequency adaptation and a relative refractory mechanism driven by Poisson spike trains can be reduced to a two-dimensional generalization of the proposed Markov process by an adiabatic elimination of fast variables. For static and dynamic stimulation, negative serial interspike interval correlations and transient population responses, respectively, of Monte Carlo simulations of the full five-dimensional system can be accurately described by the proposed two-dimensional Markov process.     

Common biases in client involved decision-making in the AEC industry

Understanding of the constitution of client involved decisions is important for future improvements of the processes. Significant decisions in construction projects are reliant on heuristic processes where assumptions are developed from past experience. The paper presents a methodology to collect empirical data in an unstructured manner utilizing participant intuition and experience regarding project level collaboration, a term easily understood by practitioners. Empirical data collected from 6 focus group discussions in Norway and 18 individual interviews in Finland is associated with biases in decision making aimed at bridging the gap of understanding and literature’s insufficient coverage. An analytic framework was developed to suit the diverse emergence of concepts to allow application of psychological principles in a structured manner to empirical data. The paper contributes by identifying types of cognitive and motivational biases in client involved decisions. The biases are found to be alleviated by one another depending on the particular application of the decision. Findings suggest that normative beliefs exist developed from past experience and habitual thinking. A number of emerged biases in this domain are alleviated from normative beliefs which are discussed in this paper.