Addressing the Conflicting Dimension of Groupware: A Case Study in Software Requirements Validation

This paper addresses the conflicting dimension of groupware, seeking the reconciliation of two very different assumptions about the users' attitudes using groupware tools: users either collaborate or negotiate to reach consensus. We argue that groupware should integrate the full spectrum of attitudes occurring between these two extremes. The designed solution integrates content and process support in a coherent model supporting low and high conflict situations. Furthermore, we propose a set of benefits and resistances, developed at the user-interface level, aiming to influence users towards low conflict attitudes when interacting with groupware. This approach was applied in a case study involving the development of a groupware tool supporting Quality Function Deployment for software requirements validation in a real-world organization. The case study indicated that the proposed approach was beneficial promoting consensus

Formação de equipa para combate a incêndio urbano

Neste artigo descreve-se uma abordagem para dimensionar uma equipa de bombeiros para combate a incêndio urbano em ambiente de simulação. A simulação foi realizada no ambiente RoboCupRescue que combina, num mesmo espaço geográfico (e.g. cidade), a evolução de uma catástrofe (e.g. incêndio urbano) e a actuação dos meios (humanos) que visam mitigar os efeitos dessa catástrofe. A abordagem seguida permite determinar o número mínimo de elementos a constituir numa equipa de bombeiros para extinguir um determinado incêndio. Utilizou-se um processo de exploração de conhecimento a partir de um conjunto de treino para construir uma árvore de decisão, recorrendo ao algoritmo de classificação ID3. O conjunto de treino foi obtido a partir da simulação de diferentes situações de incêndio usando o espaço geográfico (mapa) da cidade japonesa de Kobe. São analisados os resultados da avaliação das regras geradas e apresentam-se algumas conclusões sobre os factores que influenciam o critério de formação das equipas.info:eu-repo/semantics/publishedVersio

Bi-objective Evolutionary Heuristics for Bus Drivers

The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model

A Memetic Algorithm for a Bi-objective Bus Driver Rostering Problem

The Bus Driver Rostering Problem (DRP) consists of assigning bus drivers to daily duties during a planning period. The problem considers hard constraints imposed by institutional and legal requirements. Solutions should as much as possible satisfy soft constraints that qualify rosters according to either the company's or the drivers' interests. A bi-objective version of the DRP is considered and two models are presented. Due to the high computational complexity of DRP, this paper proposes the Strength Pareto Utopic Memetic Algorithm (SPUMA) a new heuristic algorithm specially devised to tackle the problem. SPUMA genetic component combines utopic elitism with a strength Pareto fitness evaluation and includes an improvement procedure. Computational results show that SPUMA outperforms an adaptation of one of the state-of-the-art most competitive multi-objective evolutionary algorithms, SPEA2

Solving Public Transit Scheduling Problems

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle scheduling, crew scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to solve this multi-objective problem is a sequential algorithm considered within a preemptive goal programming framework that starts from the solution of an integrated vehicle and crew scheduling problem and ends with the solution of a driver rostering problem. Feasible solutions for the vehicle and crew scheduling problem are obtained by combining a column generation scheme with a branch-and-bound method. These solutions are the input of the rostering problem, which is tackled through a mixed binary linear programming approach. An application to real data of a Portuguese bus company is reported and shows the importance of integrating the three scheduling problems

On Statistically Estimated Optimistic Delivery inWide-Area Total Order Protocols

Total order broadcast protocols have been successfully applied as the basis for the construction of many fault-tolerant distributed systems. Unfortunately, the implementation of such a primitive can be expensive both in terms of communication steps and of number of messages exchanged. To alleviate this problem, optimistic total order protocols have been proposed. This paper addresses the problem of offering optimistic total order in geographically wide-area systems. We present a protocol that outperforms previous work, by minimizing the average latency of the optimistic notificatio

Branching approaches for integrated vehicle and crew scheduling

The integrated multi-depot vehicle and crew scheduling problem simultaneously builds vehicle blocks and crew duties. We present an integer mathematical formulation that combines a multi-commodity flow model with a mixed set partitioning/covering model. We propose solution approaches that start by solving the linear programming relaxation of the model. Whenever the resulting linear programming solution is not integer, three branching alternative strategies can be applied: a branch-and-bound algorithm and two branch-and-price schemes. The branch-and-bound algorithm performs branching over the set of feasible crew duties generated while solving the linear relaxation. In the first branch-and-price scheme the linear programming relaxation is solved approximately, while in the second one it is solved exactly. Computational experience is reported over two different types of problems: randomly generated data publicly available for benchmarking in the Internet and data from a bus company operating in Lisbon