Margarida Moz

A genetic algorithm approach to a nurse rerostering problem

Abstract The nurse rerostering problem occurs when one or more nurses cannot work in shifts that were previously assigned to her or them. If no pool of reserve nurses exists to replace those absent, then the current roster must be rebuilt. This new roster must comply with the labour rules and institutional constraints. Moreover, it must be as similar as possible to the current one. The present paper describes constructive heuristics, besides several versions of genetic algorithms based on specific encoding and operators for sequencing problems applied to the nurse rerostering problem, defined with hard constraints. In the genetic algorithms described, each individual in the population is associated with a pair of chromosomes, representing permutations of tasks and nurses. Those permutations are used as input to a procedure that generates rosters. The fitness of individuals is given by the similarity between the roster generated from the permutations and the current one. The authors developed several versions of the genetic algorithm, whose difference lay in the encoding of permutations and in the genetic operators used for each encoding. These heuristics were tested with real data from a Lisbon hospital and yielded good quality solutions. Scope and purpose The research reported is part of a project designed to develop a system for the management of nurse schedules for implementation in Portuguese public hospitals. The specific problem of rebuilding nurse schedules is addressed when unexpected staff absences arise. The complexity of the problem led the authors to design heuristic procedures. The tests performed so far with real data have shown that the algorithms attain good quality solutions at a computing time within the bounds stipulated by the hospital.

Solving the Problem of Rerostering Nurse Schedules with Hard Constraints: New Multicommodity Flow Models

The problem of rerostering nurse schedules arises in hospitals when at least one nurse informs that she will be unable to perform the shifts assigned to her on one or more future work days. As a result, the current roster must be rebuilt in accordance with labour contract rules and institutional requirements. All such restraints are regarded as hard constraints. However, major alterations in the previously assigned nurse schedules must be avoided. This paper is based on a case study of a public hospital in Portugal. It presents two new integer multicommodity flow formulations for the rerostering problem, besides a computational experiment performed using real data. The first model is based on a directed multilevel acyclic network. The aggregation of nodes in this network led to the second model. The results obtained show that the second integer multicommodity flow formulation outperforms the first, both in terms of solution quality, as well as in computational time.

Solving a bi-objective nurse rerostering problem by using a utopic Pareto genetic heuristic

Abstract 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.

An Integer Multicommodity Flow Model Applied to the Rerostering of Nurse Schedules

The problem of rerostering service schedules is very common in organizations that work shifts around the clock every day of the year with a set number of employees. Whenever one or more workers announce that they will not be able to attend to tasks previously assigned in their schedule, those tasks must be performed at the expense of alterations in the schedules of other workers. These changes should not conflict with the rules laid down by the administration and employment contracts and should affect the previous schedules as little as possible. This is a difficult real problem calling for a computational tool to cope with it easily. In the paper the issue is described in detail in the context of nurse scheduling and formulated as an integer multicommodity flow problem with additional constraints, in a multi-level acyclical network. A heuristic was implemented as a first approach to solving the problem. Subsequently the integer linear programming formulation of the multicommodity flow model and two linear relaxations were tested using CPLEX [2] optimizers. The computational results reported regard real instances from a Lisbon state hospital. Satisfactory rosters were obtained within acceptable computational times in all instances tested, either with the integer optimizer, or with the heuristic. This being so, refinements will be undertaken to embed these methodologies in a decision support system that may assist the head nurse in her daily rerostering activities.

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.

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.

A decomposition approach for the integrated vehicle-crew-roster problem with days-off pattern

The integrated vehicle-crew-roster problem with days-off pattern aims to simultaneously determine minimum cost vehicle and daily crew schedules that cover all timetabled trips and a minimum cost roster covering all daily crew duties according to a pre-defined days-off pattern. This problem is formulated as a new integer linear programming model and is solved by a heuristic approach based on Benders decomposition that iterates between the solution of an integrated vehicle-crew scheduling problem and the solution of a rostering problem. Computational experience with data from two bus companies in Portugal and data from benchmark vehicle scheduling instances shows the ability of the approach for producing a variety of solutions within reasonable computing times as well as the advantages of integrating the three problems.

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.

A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern

Facing severe budgetary constraints, public transport companies are forced to efficiently manage staff, one of the most expensive resources.

An Integrated Vehicle-Crew-Roster Problem with Days-Off Pattern

The integrated vehicle-crew-roster problem with days-off pattern aims to simultaneously determine minimum cost sets of vehicle and daily crew schedules that cover all timetabled trips and a minimum cost roster covering all daily crew duties according to a pre-defined days-off pattern. This problem is modeled as a mixed binary linear programming problem. A heuristic approach with embedded column generation and branch-and-bound techniques within a Benders decomposition is proposed. The new methodology was tested on real instances and the computational results are promising.