Capacitated Vehicle Routing Problems: Nearest Neighbour vs. Tabu Search

The purpose of the Vehicle Routing Problem is to obtain a vehicle route with a minimum mileage in meeting customer demand according to their respective locations. One variant of the Vehicle Routing Problem (VRP) is the Capacitated Vehicle Routing Problem (CVRP), namely VRP with vehicle capacity constraints. Problems with Capacitated Vehicle Routing Problems (CVRP), can be solved by using the nearest neighbour and Tabu Search Algorithm. The step to complete the Tabu Search Algorithm begins with the determination of the initial solution using Nearest Neighbour, determining alternative solutions with exchange, namely to move two points in the solution, evaluate alternative solutions with Tabu list, choose the best solution and set the optimum solution, update tabu list, then if the discharge criteria are met then the process stops and if not, then returns to the determination of alternative solutions. Based on the results of calculations using the Tabu Search Algorithm, the traveling distance is less about 10.01% than the nearest neighbor

Alternative Measures for the Analysis of Online Algorithms

In this thesis we introduce and evaluate several new models for the analysis of online algorithms. In an online problem, the algorithm does not know the entire input from the beginning; the input is revealed in a sequence of steps. At each step the algorithm should make its decisions based on the past and without any knowledge about the future. Many important real-life problems such as paging and routing are intrinsically online and thus the design and analysis of online algorithms is one of the main research areas in theoretical computer science. Competitive analysis is the standard measure for analysis of online algorithms. It has been applied to many online problems in diverse areas ranging from robot navigation, to network routing, to scheduling, to online graph coloring. While in several instances competitive analysis gives satisfactory results, for certain problems it results in unrealistically pessimistic ratios and/or fails to distinguish between algorithms that have vastly differing performance under any practical characterization. Addressing these shortcomings has been the subject of intense research by many of the best minds in the field. In this thesis, building upon recent advances of others we introduce some new models for analysis of online algorithms, namely Bijective Analysis, Average Analysis, Parameterized Analysis, and Relative Interval Analysis. We show that they lead to good results when applied to paging and list update algorithms. Paging and list update are two well known online problems. Paging is one of the main examples of poor behavior of competitive analysis. We show that LRU is the unique optimal online paging algorithm according to Average Analysis on sequences with locality of reference. Recall that in practice input sequences for paging have high locality of reference. It has been empirically long established that LRU is the best paging algorithm. Yet, Average Analysis is the first model that gives strict separation of LRU from all other online paging algorithms, thus solving a long standing open problem. We prove a similar result for the optimality of MTF for list update on sequences with locality of reference. A technique for the analysis of online algorithms has to be effective to be useful in day-to-day analysis of algorithms. While Bijective and Average Analysis succeed at providing fine separation, their application can be, at times, cumbersome. Thus we apply a parameterized or adaptive analysis framework to online algorithms. We show that this framework is effective, can be applied more easily to a larger family of problems and leads to finer analysis than the competitive ratio. The conceptual innovation of parameterizing the performance of an algorithm by something other than the input size was first introduced over three decades ago [124, 125]. By now it has been extensively studied and understood in the context of adaptive analysis (for problems in P) and parameterized algorithms (for NP-hard problems), yet to our knowledge this thesis is the first systematic application of this technique to the study of online algorithms. Interestingly, competitive analysis can be recast as a particular form of parameterized analysis in which the performance of opt is the parameter. In general, for each problem we can choose the parameter/measure that best reflects the difficulty of the input. We show that in many instances the performance of opt on a sequence is a coarse approximation of the difficulty or complexity of a given input sequence. Using a finer, more natural measure we can separate paging and list update algorithms which were otherwise indistinguishable under the classical model. This creates a performance hierarchy of algorithms which better reflects the intuitive relative strengths between them. Lastly, we show that, surprisingly, certain randomized algorithms which are superior to MTF in the classical model are not so in the parameterized case, which matches experimental results. We test list update algorithms in the context of a data compression problem known to have locality of reference. Our experiments show MTF outperforms other list update algorithms in practice after BWT. This is consistent with the intuition that BWT increases locality of reference

Answering Conjunctive Queries under Updates

We consider the task of enumerating and counting answers to $k$-ary conjunctive queries against relational databases that may be updated by inserting or deleting tuples. We exhibit a new notion of q-hierarchical conjunctive queries and show that these can be maintained efficiently in the following sense. During a linear time preprocessing phase, we can build a data structure that enables constant delay enumeration of the query results; and when the database is updated, we can update the data structure and restart the enumeration phase within constant time. For the special case of self-join free conjunctive queries we obtain a dichotomy: if a query is not q-hierarchical, then query enumeration with sublinear$^\ast$ delay and sublinear update time (and arbitrary preprocessing time) is impossible. For answering Boolean conjunctive queries and for the more general problem of counting the number of solutions of k-ary queries we obtain complete dichotomies: if the query's homomorphic core is q-hierarchical, then size of the the query result can be computed in linear time and maintained with constant update time. Otherwise, the size of the query result cannot be maintained with sublinear update time. All our lower bounds rely on the OMv-conjecture, a conjecture on the hardness of online matrix-vector multiplication that has recently emerged in the field of fine-grained complexity to characterise the hardness of dynamic problems. The lower bound for the counting problem additionally relies on the orthogonal vectors conjecture, which in turn is implied by the strong exponential time hypothesis. $^\ast)$ By sublinear we mean $O(n^{1-\varepsilon})$ for some $\varepsilon>0$, where $n$ is the size of the active domain of the current database

zCap: a zero configuration adaptive paging and mobility management mechanism

Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards

Parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors

In this paper we consider the problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles. This is a common problem that arises in many agent-based simulation studies, and is of central importance in the context of High Level Architecture (HLA), where it is at the core of the Data Distribution Management (DDM) service. Several realizations of the DDM service have been proposed; however, many of them are either inefficient or inherently sequential. These are serious limitations since multicore processors are now ubiquitous, and DDM algorithms -- being CPU-intensive -- could benefit from additional computing power. We propose a parallel version of the Sort-Based Matching algorithm for shared-memory multiprocessors. Sort-Based Matching is one of the most efficient serial algorithms for the DDM problem, but is quite difficult to parallelize due to data dependencies. We describe the algorithm and compute its asymptotic running time; we complete the analysis by assessing its performance and scalability through extensive experiments on two commodity multicore systems based on a dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.Comment: Proceedings of the 21-th ACM/IEEE International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017). Best Paper Award @DS-RT 201

Status of women report 2015

At the ALP\u27s 2015 National Conference in July, EMILY\u27s List will be seeking rule changes to increase Labor women\u27s representation to 50% by 2020. To underpin this campaign, we have produced the Status of Women 2015 report which provides an update on the current representation by women in our parliaments. This report was launched by Labor Opposition Leader Bill Shorten on 31 May. While the report shows that the ALP\u27s current national target of 40/40/20 has played a significant role in increasing the number of Australian women MPs since its introduction, 40% is not equal. ________ This status report on the representation of women in Australian parliaments comes two decades after the introduction of the ALP’s first affirmative action rule in 1994. The report has been compiled by EMILY’s List, which was set up in 1996 by a group of progressive Labor women intimately involved in the campaign to introduce AA. Realising quickly that structural change to the ALP Platform was not going to ensure equality within the party, they set about creating cultural change. EMILY’s List, which remains Australia’s only political, financial and personal support network for progressive Labor women candidates, was the result of these efforts. This Status of Women Report shows that the ALP’s quota system has been a success. It has increased ALP women’s representation from single digits in the early 1990s to 43% today. But the report also shows that there is a lot more to be done. Overall representation of women in our parliaments stands at just over 30%, with Liberal women still struggling for representation in the absence of a quota in their own party. There are still barriers to women participating in the ALP, too. The fact that the ALP has no female State/Territory or National Secretaries remains a problem for the party. Women continue to face challenges in the timing of meetings for women with children, insufficient training and mentoring as well as having to deal with attitudes amongst some men that deliberately discriminate and want to hold women back. The ALP’s current national target of 40/40/20 has played a significant role in increasing the number of Australian women MPs in recent years, but 40% is not equal. In the lead up to National Conference, EMILY’s List is launching a campaign for gender equality - 50% representation of women by 2020. We will be working to strengthen the sanction for failing to comply with targets and ensuring that they are applied in all areas of the party, not just in preselections. Supporting women and getting them elected to parliament matters. In the past 18 months, EMILY’s List has analysed the impact of its endorsed women MPs in parliament. Assessments of Federal Labor in power from 2007-2013 and the Tasmanian Labor Government 1996 - 2014 have shown that a critical mass of women in our parliaments has a significant impact on legislation of benefit to women, children and their families. More women need to be at Caucus and Cabinet decision-making tables as their presence brings about a broader, more representative legislative program. It also makes electoral sense for the ALP to ensure it maintains a steady flow of quality women candidates. Recent election results in Victoria, Queensland and NSW have also shown that women candidates resonate strongly with voters, particularly those unhappy with male-dominated conservative governments. Such results have echoed recent Massachusetts Institute of Technology research showing “a simple way to improve a political party’s chances at the ballot box is to have more women as candidates”. This report and our campaign to lift the target and strengthen AA across the party has been the work of hundreds of women, across many generations and diverse groups within the party