Dynamical Monte Carlo Study of Equilibrium Polymers (II): The Role of Rings

We investigate by means of a number of different dynamical Monte Carlo simulation methods the self-assembly of equilibrium polymers in dilute, semidilute and concentrated solutions under good-solvent conditions. In our simulations, both linear chains and closed loops compete for the monomers, expanding on earlier work in which loop formation was disallowed. Our findings show that the conformational properties of the linear chains, as well as the shape of their size distribution function, are not altered by the formation of rings. Rings only seem to deplete material from the solution available to the linear chains. In agreement with scaling theory, the rings obey an algebraic size distribution, whereas the linear chains conform to a Schultz--Zimm type of distribution in dilute solution, and to an exponentional distribution in semidilute and concentrated solution. A diagram presenting different states of aggregation, including monomer-, ring- and chain-dominated regimes, is given

Self-organisation of tip functionalised elongated colloidal particles

Weakly attractive interactions between the tips of rod-like colloidal particles affect their liquid-crystal phase behaviour due to a subtle interplay between enthalpy and entropy. Here, we employ molecular dynamics simulations on semi-flexible, repulsive bead-spring chains of which one of the two end beads attract each other. We calculate the phase diagram as a function of both the volume fraction of the chains and the strength of the attractive potential. We identify a large number of phases that include isotropic, nematic, smectic A, smectic B and crystalline states. For tip attraction energies lower than the thermal energy, our results are qualitatively consistent with experimental findings: we find that an increase of the attraction strength shifts the nematic to smectic A phase transition to lower volume fractions, with only minor effect on the stability of the other phases. For sufficiently strong tip attraction, the nematic phase disappears completely, in addition leading to the destabilisation of the isotropic phase. In order to better understand the underlying physics of these phenomena, we also investigate the clustering of the particles at their attractive tips and the effective molecular field experienced by the particles in the smectic A phase. Based on these results, we argue that the clustering of the tips only affects the phase stability if lamellar structures (``micelles'') are formed. We find that an increase of the attraction strength increases the degree of order in the layered phases.Interestingly, we also find evidence for the existence of an anti-ferroelectric smectic A phase transition induced by the interaction between the tips. A simple Maier-Saupe-McMillan model confirms our findings

A Class of Convex Quadratic Nonseparable Resource Allocation Problems with Generalized Bound Constraints

We study a convex quadratic nonseparable resource allocation problem that arises in the area of decentralized energy management (DEM), where unbalance in electricity networks has to be minimized. In this problem, the given resource is allocated over a set of activities that is divided into subsets, and a cost is assigned to the overall allocated amount of resources to activities within the same subset. We derive two efficient algorithms with $O(n \log  n)$ worst-case time complexity to solve this problem. For the special case where all subsets have the same size, one of these algorithms even runs in linear time given the subset size. Both algorithms are inspired by well-studied breakpoint search methods for separable convex resource allocation problems. Numerical evaluations on both real and synthetic data confirm the theoretical efficiency of both algorithms and demonstrate their suitability for integration in DEM systems

On a reduction for a class of resource allocation problems

In the resource allocation problem (RAP), the goal is to divide a given amount of resource over a set of activities while minimizing the cost of this allocation and possibly satisfying constraints on allocations to subsets of the activities. Most solution approaches for the RAP and its extensions allow each activity to have its own cost function. However, in many applications, often the structure of the objective function is the same for each activity and the difference between the cost functions lies in different parameter choices such as, e.g., the multiplicative factors. In this article, we introduce a new class of objective functions that captures the majority of the objectives occurring in studied applications. These objectives are characterized by a shared structure of the cost function depending on two input parameters. We show that, given the two input parameters, there exists a solution to the RAP that is optimal for any choice of the shared structure. As a consequence, this problem reduces to the quadratic RAP, making available the vast amount of solution approaches and algorithms for the latter problem. We show the impact of our reduction result on several applications and, in particular, we improve the best known worst-case complexity bound of two important problems in vessel routing and processor scheduling from $O(n^2)$ to $O(n \log n)$

A fast algorithm for quadratic resource allocation problems with nested constraints

We study the quadratic resource allocation problem and its variant with lower and upper constraints on nested sums of variables. This problem occurs in many applications, in particular battery scheduling within decentralized energy management (DEM) for smart grids. We present an algorithm for this problem that runs in $O(n \log n)$ time and, in contrast to existing algorithms for this problem, achieves this time complexity using relatively simple and easy-to-implement subroutines and data structures. This makes our algorithm very attractive for real-life adaptation and implementation. Numerical comparisons of our algorithm with a subroutine for battery scheduling within an existing tool for DEM research indicates that our algorithm significantly reduces the overall execution time of the DEM system, especially when the battery is expected to be completely full or empty multiple times in the optimal schedule. Moreover, computational experiments with synthetic data show that our algorithm outperforms the currently most efficient algorithm by more than one order of magnitude. In particular, our algorithm is able to solves all considered instances with up to one million variables in less than 17 seconds on a personal computer

Quadratic nonseparable resource allocation problems with generalized bound constraints

We study a quadratic nonseparable resource allocation problem that arises in the area of decentralized energy management (DEM), where unbalance in electricity networks has to be minimized. In this problem, the given resource is allocated over a set of activities that is divided into subsets, and a cost is assigned to the overall allocated amount of resources to activities within the same subset. We derive two efficient algorithms with $O(n\log n)$ worst-case time complexity to solve this problem. For the special case where all subsets have the same size, one of these algorithms even runs in linear time given the subset size. Both algorithms are inspired by well-studied breakpoint search methods for separable convex resource allocation problems. Numerical evaluations on both real and synthetic data confirm the theoretical efficiency of both algorithms and demonstrate their suitability for integration in DEM systems

Offline and online scheduling of electric vehicle charging with a minimum charging threshold

The increasing penetration of electric vehicles (EVs) requires the development of smart charging strategies that accommodate the increasing load of these EVs on the distribution grid. Many existing charging strategies assume that an EV is allowed to charge at any rate up to a given maximum rate. However, in practice, charging at low rates is inefficient and often even impossible. Therefore, this paper presents an efficient algorithm for scheduling an EV within a decentralized energy management system that allows only charging above a given threshold. We show that the resulting optimal EV schedule is characterized by an activation level and a fill-level. Moreover, based on this result, we derive an online approach that does not require predictions of uncontrollable loads as input, but merely a prediction of these two characterizing values. Simulation results show that the online algorithm is robust against prediction errors in these values and can produce near-optimal online solutions