Solving a Bi-objective Nurse Rerostering Problem by Using a Utopic Pareto Genetic Heuristic

Nurse rerostering arises when at least one nurse announces that she will be unable to undertake the tasks previously assigned to her. The problem amounts to building a new roster that satisfies the hard constraints already met by the current one and, as much as possible, fulfils two groups of soft constraints which define the two objectives to be attained. A bi-objective genetic heuristic was designed on the basis of a population of individuals characterised by pairs of chromosomes, whose fitness complies with the Pareto ranking of the respective decoded solution. It includes an elitist policy, as well as a new utopic strategy, introduced for purposes of diversification. The computational experiments produced promising results for the practical application of this approach to real life instances arising from a public hospital in Lisbon

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

Grasp and tabu search for redesigning web communities

Web topologies are commonly characterised by hierarchical structures and highly unbalanced compositions, as illustrated by the difference of centrality and connectivity as to their elements. The major interest of the problem addressed in this paper lies in restructuring web communities to reduce these initial disequilibria so as to democratise information access or even for the purpose of preserving contents distributed on the Internet. Discussion of this issue thus leads to a hub location problem, formalised by network and integer programming models. Due to its highly complex nature, a GRASP and a tabu search heuristics were developed to find good quality feasible solutions to the problem. The set of test instances includes web communities obtained by crawling the web and using epistemic boundaries, as well as other randomly generated communities, built with specific network analysis software. The experiment demonstrated that the metaheuristics produced low costs and balanced structures, at least for the lower dimension web communities considered. All the redesigned web communities are more closely connected than before and the average distance among their elements reduced

Time-of-use electricity tariffs with smart meters

This paper proposes a method for scheduling tariff time periods for electricity consumers. Europe will see a broader use of modern smart meters for electricity at residential consumers which must be used for enabling demand response. A heuristic-based method for tariff time period scheduling and pricing is proposed which considers different consumer groups with parameters studied a priori, taking advantage of demand response potential for each group and the fairness of electricity pricing for all consumers. This tool was applied to the case of Portugal, considering the actual network and generation costs, specific consumption profiles and overall electricity low voltage demand diagram. The proposed method achieves valid results. Its use will provide justification for the setting of tariff time periods by energy regulators, network operators and suppliers. It is also useful to estimate the consumer and electric sector benefits from changes in tariff time periods

A bi-objective hub-and-spoke approach for reconfiguring Web communities

Web communities in general grow naturally, thus creating unbalanced network structures where a few domains centralise most of the linkups. When one of them breaks down, a significant part of the community might be unable to communicate with the remaining domains. Such a situation is highly inconvenient, as in the case of wishing to pursue distribution policies within the community, or for marketing purposes. In order to reduce the damages of such an occurrence, the Web community should be reconfigured, in such a way that a complete sub-network of main domains -the hubs - is identified and that each of the other domains of the community - the spokes - is doubly linked at least with a hub. This problem can be modellised through a bi-objective optimisation problem, the Web Community Reconfiguring Problem, which will be presented in this paper. A bi-objective mixed binary formulation will also be shown, along with a brief description of GRASP, tabu search and hybrid heuristics which were developed to find feasible solutions to the problem, possibly efficient solutions to the bi-objective problem. A computational experiment is reported, involving comparison of these metaheuristics when applied to several Web communities, obtained by crawling the Web and using epistemic boundaries and to other randomly generated ones. The heuristics revealed excellent quality for the small dimension cases whose efficient solutions were roughly all determined. As for the other medium and higher dimension instances, the heuristics were successful in building a wide variety of feasible solutions that are candidate efficient solutions. The best behaviour was attained with the GRASP and the GRASP and tabu hybrid search. Comparison of some metrics before and after reconfiguration confirmed that the final structures are more balanced in terms of degree distribution reinforcing the connecting effect imposed by the reconfiguration process

A Comparison of Discrete and Continuous Neural Network Approaches to Solve the Class/Teacher Timetabling Problem

This study explores the application of neural network-based heuristics to the class/teacher timetabling problem (CTTP). The paper begins by presenting the basic CTTP characteristics in terms of hard and soft constraints and proposing a formulation for the energy function required to map the problem within the artificial neural network model. There follow two distinct approaches to simulating neural network evolution. The first uses a Potts mean-field annealing simulation based on continuous Potts neurons, which has obtained favorable results in various combi¬natorial optimization problems. Afterwards, a discrete neural network simulation, based on discrete winner-take-all neurons, is proposed. The paper concludes with a comparison of the computational results taken from the application of both heuris¬tics to hard hypothetical and real CTTP instances. This experiment demonstrates that the discrete approach performs better, in terms of solution quality as well as execution time

A decomposition approach to the integrated vehicle-crew-rostering problem

The problem addressed in this paper is the integrated vehicle-crew-rostering problem (VCRP) aiming to define the schedules for the buses and the rosters for the drivers of a public transit company. The VCRP is described by a bi-objective mixed binary linear programming model with one objective function aggregating vehicle and crew scheduling costs and the other the rostering features. The VCRP is solved by a heuristic approach based on Benders decomposition where the master problem is partitioned into daily integrated vehicle-crew scheduling problems and the sub-problem is a rostering problem. Computational experience with data from a bus company in Lisbon shows the ability of the decomposition approach for producing a variety of potentially efficient solutions for the VCRP within low computing times

Redesigning a food bank supply chain network, Part I: Background and mathematical formulation

Motivated by the increasing global interest in reducing food waste, we address the problem of redesigning a multi-echelon supply chain network for the collection of donated food products and their distribution to non-profit organisations that provide food assistance to the needy population. For the social enterprise managing the network, important strategic decisions comprise opening new food bank warehouses and selecting their storage and transport capacities from a set of discrete sizes over a multi-period planning horizon. Facility decisions also affect existing food banks that may be closed or have its capacity expanded. Logistics decisions involve the number of organisations to be supplied, their allocation to operating food banks and the flow of multiple food products throughout the network. Decisions must be made taking into account that food donations are insufficient and a limited investment budget is available. The paper is organised in two parts. In Part I, we propose a novel mixed-integer linear programming model that captures various practical features of a food aid supply chain. In particular, sustainability is explicitly accounted for within the decision-making process by integrating economic, environmental and social objectives. In Part II, a computational study is conducted to investigate the trade-offs achieved by considering three conflicting objective functions. Numerical results are presented for real-case based instances shaped by the food bank network coordinated by the Portuguese Federation of Food Banks