Hydrodynamic fluctuations and instabilities in ordered suspensions of self-propelled particles

We construct the hydrodynamic equations for {\em suspensions} of self-propelled particles (SPPs) with spontaneous orientational order, and make a number of striking, testable predictions:(i) SPP suspensions with the symmetry of a true {\em nematic} are {\em always} absolutely unstable at long wavelengths.(ii) SPP suspensions with {\em polar}, i.e., head-tail {\em asymmetric}, order support novel propagating modes at long wavelengths, coupling orientation, flow, and concentration. (iii) In a wavenumber regime accessible only in low Reynolds number systems such as bacteria, polar-ordered suspensions are invariably convectively unstable.(iv) The variance in the number N of particles, divided by the mean , diverges as $^{2/3}$ in polar-ordered SPP suspensions.Comment: submitted to Phys Rev Let

Modelling human network behaviour using simulation and optimization tools: the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models.Peer Reviewe

Optimizing airline crew scheduling using biased randomization: a case study

Various complex decision making problems are related to airline planning. In the competitive airline industry, ecient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of ight pairings allowing for cost minimizing crew- ight assignments. Experiments are done using a real-life case with dierent constraints. The results show that our easy-to-use and fast algorithm reduces overall crew ying times and the necessary number of accompanying crews compared to the pairings currently applied by the company

A simheuristic algorithm for time-dependent waste collection management with stochastic travel times

A major operational task in city logistics is related to waste collection. Due to large problem sizes and numerous constraints, the optimization of real-life waste collection problems on a daily basis requires the use of metaheuristic solving frameworks to generate near-optimal collection routes in low computation times. This paper presents a simheuristic algorithm for the time-dependent waste collection problem with stochastic travel times. By combining Monte Carlo simulation with a biased randomized iterated local search metaheuristic, time-varying and stochastic travel speeds between different network nodes are accounted for. The algorithm is tested using real instances in a medium-sized city in Spain

Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty. Our simheuristic approach is able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times. A range of experiments underline the algorithm's competitiveness compared to previously used heuristic approaches. The results are analyzed in order to provide closer managerial insights

Family-Based Model Checking with mCRL2

\u3cp\u3eFamily-based model checking targets the simultaneous verfication of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines (SPLs). We present an approach for family-based verification based on the feature μ-calculus μL\u3csub\u3ef\u3c/sub\u3e, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants (a family of products). Via an embedding into the modal μ-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for μLf becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\u3c/p\u3

Quantitative Modal Transition Systems

International audienceThis extended abstract offers a brief survey presentation of the specification formalism of modal transition systems and its recent extensions to the quantitative setting of timed as well as stochastic systems. Some applications will also be briefly mentioned