Ana Respício

Bi-objective evolutionary heuristics for bus driver rostering

The Bus Driver Rostering Problem (BRP) refers to the assignment of drivers to the daily crew duties that cover a set of schedules for buses of a company during a planning period of a given duration, e.g., a month. An assignment such as this, denoted as roster, must comply with legal and institutional rules, namely Labour Law, labour agreements and the company’s regulations. This paper presents a new bi-objective model for the BRP, assuming a non-cyclic rostering context. One such model is appropriate to deal with the specific and diverse requirements of individual drivers, e.g. absences. Two evolutionary heuristics, differing as to the strategies adopted to approach the Pareto frontier, are described for the BRP. The first one, following a utopian strategy, extends elitism to include an infeasible (utopic) and two potential lexicographic individuals in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics’ empirical performance was studied through computational tests on BRP instances generated from the solution of integrated vehicle-crew scheduling problems, along with the rules of a public transit company operating in Portugal. This research shows that both methodologies are adequate to tackle these instances. However, the second one is, in general, the more favourable. In reasonable computation times they provide the company’s planning department with several rosters that satisfy all the constraints, an achievement which is very difficult to obtain manually. In addition, among these rosters they identify the potentially efficient ones with respect to the BRP model’s two objectives, one concerning the interests of administration, the other the interests of the workers. Both heuristics have advantages and drawbacks. This suggests that they should be used complementarily. On the other hand, the heuristics can, with little effort, be adapted to a wide variety of rostering rules.

Tactical exploration of tax compliance decisions in multi-agent based simulation

Tax compliance is a field that crosses over several research areas, from economics to machine learning, from sociology to artificial intelligence and multi-agent systems. The core of the problem is that the standing general theories cannot even explain why people comply as much as they do, much less make predictions or support prescriptions for the public entities. The compliance decision is a challenge posed to rational choice theory, and one that defies the current choice mechanisms in multi-agent systems. The key idea of this project is that by considering rationally-heterogeneous agents immersed in a highly social environment we can get hold of a better grasp of what is really involved in the individual decisions. Moreover, we aim at understanding how those decisions determine tendencies for the behaviour of the whole society, and how in turn those tendencies influence individual behaviour. This paper presents the results of some exploratory simulations carried out to uncover regularities, correlations and trends in the models that represent first and then expand the standard theories on the field. We conclude that forces like social imitation and local neighbourhood enforcement and reputation are far more important than individual perception of expected utility maximising, in what respects compliance decisions.

A new model for the integrated vehicle-crew-rostering problem and a computational study on rosters

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 obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management’s interests and drivers’ preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.

Software requirements negotiation using the software quality function deployment

We propose a groupware tool supporting the Software Quality Function Deployment approach to software requirements validation. The design challenge is to involve several stakeholders, having conflicting views and attitudes which may be difficult to reconcile, in the requirements validation. The adopted approach integrates collaboration and negotiation support. Negotiation models inspired the development of a set of mechanisms promoting integrative attitudes and avoiding distributive ones. Experiments with the tool revealed some usability problems, but also showed that it is convenient to use and beneficial promoting consensus.

e*plore v.0: Principia for Strategic Exploration of Social Simulation Experiments Design Space

Years ago, we addressed the issue of methodological procedures to develop the design of cognitive agents tuned to real problems, inserting them into a context where experimentation could have a meaningful outcome in terms of the original problems posed. Since then we have been building mechanisms and frameworks for mind design in multiagent systems. We proceeded with the evaluation of such systems through simulation that was many times exploratory. In many of those experiments, the evaluation of the deep meaning of outcomes was inherently complex, challenging the researchers and even the research questions.

Enhanced genetic algorithms for a bi‐objective bus driver rostering problem: a computational study

In this work, the bus driver rostering problem is considered in the context of a noncyclic rostering, with two objectives representing either the company or the drivers’ interests. A network model and a proof of the NP-hardness of the problem are presented, along with a bi-objective memetic algorithm that combines a specific decoder with a utopian/lexicographic elitism, a strength Pareto fitness evaluation, and a local search procedure. By taking real and benchmark instances the computational behavior of the memetic algorithm is compared with simpler versions to assess the effects of the embedded components. The developed algorithm is a valuable tool for bus companies’ planning departments insofar as it yields at low computing times a pool of good quality rosters that reconcile contradictory objectives. This study shows that simple enhancements in standard bi-objective genetic algorithms may improve the results for this difficult combinatorial problem.

On Statistically Estimated Optimistic Delivery in Wide-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 notification

Training Clinical Decision-Making through Simulation

Clinical decision making faces relevant uncertainties, outcomes and trade-offs. It has to deal with diagnosis uncertainties, the choice of diagnostic tests, the selection of prescriptions and procedures, and the treatment follow up, many times facing severe budget limitations and lack of sophisticated equipment. This paper presents a multi-agent learning system for health care practitioners: SimDeCS (Simulation for Decision Making in the Health Care Service). This system relies on simulations of complex clinical cases integrated in a virtual learning environment, and has been developed within a program offering continuous education, training and qualification to professionals in the Brazilian health care service. SimDeCS will be made available on the Internet, thus providing access to professionals working throughout the country. The main contribution is the system architecture and the model knowledge.The learning environment has been designed as a multi-agent system where three intelligent agents are included: Domain Agent, Learner Agent, and Mediator Agent. The knowledge model is implemented by the Domain Agent through probabilistic reasoning, relying on expert human knowledge encoded in Bayesian networks. A clinical case is presented and discussed.

Marketing-production Interface through an Integrated DSS

This paper reports on a real-world application addressing the coordination between cross functionality of marketing and production. We focus on the critical issues of this interface, namely: capacity planning and long-range to medium-range sales forecasting, production, short-term scheduling and short-range sales forecasting. We present a case study concerning the development of a decision support system for the paper industry. It assists both marketing and production decision makers, leading to a reduction of conflicts. The decision-making process is generalised into a framework that allows the sharing of the same decision models but with different perspectives. This framework may be adapted to other industries.

See, hear or read the film

Films have been themost entertaining art form during the past century. Sometimes they were inspired in written novels; sometimes they have inspired new written novels. Film scripts are halfway between the film in the screen and the pure world of written imagination. Real time is one of the dimensions lost in the script, breaking the anchors to the time signals of what films are made. This paper presents a full approach for merging these two worlds in the real time dimension. Using subtitles time stamping and a new parallel text alignment algorithm, a time stamped script is produced. This is used to create new enriched narrative films, also presented in the paper.