Christine Strauss

An improved Ant System algorithm for theVehicle Routing Problem

The Ant System is a distributed metaheuristic that combines an adaptive memory with alocal heuristic function to repeatedly construct solutions of hard combinatorial optimizationproblems. In this paper, we present an improved ant system algorithm for the Vehicle RoutingProblem with one central depot and identical vehicles. Computational results on fourteenbenchmark problems from the literature are reported and a comparison with five othermetaheuristic approaches for solving Vehicle Routing Problems is given.

Applying the ANT System to the Vehicle Routing Problem

In this paper we use a recently proposed metaheuristic, the Ant System, to solve the Vehicle Routing Problem in its basic form, i.e., with capacity and distance restrictions, one central depot and identical vehicles. A “hybrid” Ant System algorithm is first presented and then improved using problem-specific information (savings, capacity utilization). Experiments on various aspects of the algorithm and computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches such as Tabu Search, Simulated Annealing and Neural Networks.

Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection

Abstract One of the most important, common and critical management issues lies in determining the “best” project portfolio out of a given set of investment proposals. As this decision process usually involves the pursuit of multiple objectives amid a lack of a priori preference information, its quality can be improved by implementing a two-phase procedure that first identifies the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows an interactive exploration of that space. However, determining the solution space is not trivial because brute-force complete enumeration only solves small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. While meta-heuristics in general provide an attractive compromise between the computational effort necessary and the quality of an approximated solution space, Pareto ant colony optimization (P-ACO) has been shown to perform particularly well for this class of problems. In this paper, the beneficial effect of P-ACO’s core function (i.e., the learning feature) is substantiated by means of a numerical example based on real world data. Furthermore, the original P-ACO approach is supplemented by an integer linear programming (ILP) preprocessing procedure that identifies several efficient portfolio solutions within a few seconds and correspondingly initializes the pheromone trails before running P-ACO. This extension favors a larger exploration of the search space at the beginning of the search and does so at a low cost.

An ant colony optimization approach for the single machine total tardiness problem

Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. We consider a machine scheduling problem with one machine, the Single Machine Total Tardiness Problem. To solve this NP hard problem, we apply the ant colony optimization metaphor, a recently developed meta-heuristic that has proven its potential for various other combinatorial optimization problems. We test our algorithm using 125 benchmark problems and present computational results.

A Stochastic Branch-and-Bound Approach to Activity Crashing in Project Management

Many applications such as project scheduling, workflow modeling, or business process re-engineering incorporate the common idea that a product, task, or service consisting of interdependent time-related activities should be produced or performed within given time limits. In real-life applications, certain measures like the use of additional manpower, the assignment of highly-skilled personnel to specific jobs, or the substitution of equipment are often considered as means of increasing the probability of meeting a due date and thus avoiding penalty costs. This paper investigates the problem of selecting, from a set of possible measures of this kind, the combination of measures that is the most cost-efficient. Assuming stochastic activity durations, the computation of the optimal combination of measures may be very expensive in terms of runtime. In this article, we introduce a powerful stochastic optimization approach to determine a set of efficient measures that crash selected activities in a stochastic activity network. Our approach modifies the conventional Stochastic Branch-and-Bound, using a heuristic--instead of exact methods--to solve the deterministic subproblem. This modification spares computational time and by doing so provides an appropriate method for solving various related applications of combinatorial stochastic optimization. A comparative computational study shows that our approach not only outperforms standard techniques but also definitely improves conventional Stochastic Branch-and-Bound.

An enhanced rostering model for airline crews

Abstract This paper introduces an efficient adaptation of the branch-and-bound technique that solves real-world rostering problems for airline crews. The efficiency of the algorithm is based on the exploitation of rostering-specific properties (e.g. variable selection, branching strategy and cutting-planes). This approach shortens the solution process and outperforms standard techniques. Furthermore, we formally introduce a general concept of downgrading that makes it possible to solve certain rostering problems that might otherwise have no solution. This paper also computes a sample monthly schedule on the basis of a medium-sized European airlines real data. Scope and purpose The scheduling of airline crews and the assignment of crews to flights are important and difficult planning functions that most airlines undertake on a monthly basis. Solving a so-called rostering problem includes the construction of individualized schedules that take into account various pre-assignments (like training or observer flights), as well as crew requests (such as days off or preferred flights). The European rostering approach implies large scale and complex integer problems with 10 000 variables and several hundred constraints. This paper develops a new algorithm (SWIFTROSTER) that incorporates several strategies that exploit problem-specific knowledge in order to solve even large problems in very short runtimes, thus outperforming commercial solvers. Using real data from a medium-sized European airline the article demonstrates that our approach generates efficient solutions that can be applied in the real world to produce crew schedules.

SavingsAnts for the Vehicle Routing Problem

In this paper we propose a hybrid approach for solving vehicle routing problems. The main idea is to combine an Ant System (AS) with a problem specific constructive heuristic, namely the well known Savings algorithm. This differs from previous approaches, where the subordinate heuristic was the Nearest Neighbor algorithm initially proposed for the TSP. We compare our approach with some other classic, powerful meta-heuristics and showthat our results are competitive.

Interactive Digital Signage - An Innovative Service and Its Future Strategies

Digital signage is an economically promising field in advertising. Yet, it is a hardly researched field, first empirical attempts focused on consumer reactions. Digital signage, though, brings together various market players, all of which with different objectives and expected benefits. For this reason, we introduce a conceptual framework for interactive digital signage that allows developing various business strategies and associated business values integrating the entire set of possible players in their relevant roles and configuration requirements. A rule set represents one of the core modules of the framework, which allows for a sustainable integration of functionalities. Acknowledging that technological and non-technological advancements of digital signage need reconcilement, our proposed integrated approach to digital signage allows for interdisciplinary contributions.

Integrated management platform for seamless services provisioning in converged network

With the convergence of IN and IP traditional network management systems have been replaced by a variety of standards. New principles of management have also emerged. Related to these changes new alternative network management techniques are needed. There are a lot of efforts in management protocols improvement based on advanced computer simulation techniques. But, there is a lack in the development of fundamental theoretical models for management architectures. To remedy the situation we develop an analytical modelling tool for the converged network management. More precisely, we implement a queuing model on the application layer to perform effectively a signalling management into both parts of the converged SIP- and IN-based network. We model this distributed management configuration as a finite source queuing model. Furthermore, we evaluate the steady-state performance measures such as expected waiting time and time in system, and present numerical results providing corresponding curves for them.

Managing an increased service heterogeneity in a converged enterprise infrastructure with SOA

The traditional service management techniques and frameworks are inadequate for handling new-generation integrated applications and cross-enterprise business processes and services. There is a need for management architecture that can provide real-time visibility and control from the application and data levels through the dynamic service and business process levels. The Service Delivery Platform (SDP) is the proper, but still not obvious, choice for such a management architecture. As yet, there is no standard definition for SDP or for its constituent components. In this paper, we identify both the current boundaries of SDP and the concepts importance. We classify and analyse the components and the relations between the elements of this management framework, and visualise the results of our analysis through the reference architecture. Through our representation of the service management framework, we also provide a rational view of the whole service delivery environment.