Metaheuristics approach for solving personalized crew rostering problem in public bus transit

The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.     

Staff rostering for the station personnel of a railway company

This paper presents a case study commissioned by the Spanish railway carrier Ferrocariles Españoles de Vía Estrecha for the annual rostering of work schedules for station personnel. A mixed rostering process is used. The first part of the process is carried out manually with the aid of a spreadsheet and Visual Basic, and consists of designing an initial graphic with 4-week patterns for each station, assigning those patterns in a rotating schedule over the year and factoring in vacation time. The second part consists of assigning relief shifts to cover those shifts left vacant in such a way as to minimize the distance travelled by personnel from other stations brought in for relief shift duty. To that end, basic programmes are designed using binary programming and a4-week time frame. The results obtained are a clear improvement on the system previously used at FEVE and the company has decided to implement the model.     

Integer programming model extensions for a multi-stage nurse rostering problem

Annals of Operations Research - In the variant of the well studied nurse rostering problem proposed in the Second International Nurse Rostering Competition, multiple stages have to be solved...     

Metaheuristics approach to the aircrew rostering problem

The solution of the aircrew-scheduling problem is represented by a set of rotations developed from a given set of flight segments. Once the set of rotations to be made by aircrew members has been determined, the air carrier must solve the aircrew rostering problem that entails the monthly assignment of aircrew members to planned rotations. This paper attempts to solve the aircrew rostering problem, thus constructing personalized monthly schedules using Simulated Annealing, Genetic Algorithms, and Tabu Search techniques. The developed models are tested on numerical examples that consist of constructing schedules for pilots. Dimensions of the considered examples are characteristic of small and medium-sized airlines.     

Integer programming techniques for the nurse rostering problem

This work presents integer programming techniques to tackle the problem of the International Nurse Rostering Competition. Starting from a compact and monolithic formulation in which the current generation of solvers performs poorly, improved cut generation strategies and primal heuristics are proposed and evaluated. A large number of computational experiments with these techniques produced the following results: the optimality of the vast majority of instances was proved, the best known solutions were improved by up to 15 % and strong dual bounds were obtained. In the spirit of reproducible science, all code was implemented using the Computational Infrastructure for Operations Research.     

The nurse rostering problem: A critical appraisal of the problem structure

This paper is concerned with the problem of nurse rostering within hospitals. We analyse a class of four benchmark instances from the nurse rostering literature to provide insight into the nature of the problem. By highlighting the structure of the problem we are able to reduce the relevant solution space. A mixed integer linear programme is then able to find optimal solutions to all four instances of this class of benchmark problems, each within half an hour. Our second contribution is to extend current mathematical approaches to nurse rostering to take better account of the practical considerations. We provide a methodology for handling rostering constraints and preferences arising from the continuity from one scheduling period to the next.     

A scatter search methodology for the nurse rostering problem

The benefits of automating the nurse scheduling process in hospitals include reducing the planning workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and to attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. This paper presents a scatter search approach for the problem of automatically creating nurse rosters. Scatter search is an evolutionary algorithm, which has been successfully applied across a number of problem domains. To adapt and apply scatter search to nurse rostering, it was necessary to develop novel implementations of some of scatter search's subroutines. The algorithm was then tested on publicly available real-world benchmark instances and compared against previously published approaches. The results show the proposed algorithm is a robust and effective method on a wide variety of real-world instances.     

A systematic two phase approach for the nurse rostering problem

Nurse rostering is an NP-hard combinatorial problem which makes it extremely difficult to efficiently solve real life problems due to their size and complexity. Usually real problem instances have complicated work rules related to safety and quality of service issues in addition to rules about quality of life of the personnel. For the aforementioned reasons computer supported scheduling and rescheduling for the particular problem is indispensable. The specifications of the problem addressed were defined by the First International Nurse Rostering Competition (INRC2010) sponsored by the leading conference in the Automated Timetabling domain, PATAT-2010. Since the competition imposed quality and time constraint requirements, the problem instances were partitioned into sub-problems of manageable computational size and were then solved sequentially using Integer Mathematical Programming. A two phase strategy was implemented where in the first phase the workload for each nurse and for each day of the week was decided while in the second phase the specific daily shifts were assigned. In addition, local optimization techniques for searching across combinations of nurses’ partial schedules were also applied. This sequence is repeated several times depending on the available computational time. The results of our approach and the submitted software produced excellent solutions for both the known and the hidden problem instances, which in respect gave our team the first position in all tracks of the INRC-2010 competition.     

Roster evaluation based on classifiers for the nurse rostering problem

The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100 % correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality.     

Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem

Airline crew scheduling problem is a complex and difficult problem faced by all airline companies.To tackle this problem, it was often decomposed into two subproblems solved successively. First, the airline crew-pairing problem, which consists on finding a set of trips – called pairings – i.e. sequences of flights, starting and ending at a crew base, that cover all the flights planned for a given period of time. Secondly, the airline crew rostering problem, which consists on assigning the pairings found by solving the first subproblem, to the named airline crew members. For both problems, several rules and regulations must be respected and costs minimized.It is sure that this decomposition provides a convenient tool to handle the numerous and complex restrictions, but it lacks, however, of a global treatment of the problem. For this purpose, in this study we took the challenge of proposing a new way to solve both subproblems simultaneously. The proposed approach is based on a hybrid genetic algorithm. In fact, three heuristics are developed here to tackle the restriction rules within the GA’s process.     

Towards a practical engineering tool for rostering

The profitability and morale of many organizations (such as factories, hospitals and airlines) are affected by their ability to schedule their personnel properly. Sophisticated and powerful constraint solvers such as ILOG, CHIP, ECLiPSe, etc. have been demonstrated to be extremely effective on scheduling. Unfortunately, they require non-trivial expertise to use. This paper describes ZDC-rostering, a constraint-based tool for personnel scheduling that addresses the software crisis and fills a void in the space of solvers. ZDC-rostering is easier to use than the above constraint-based solvers and more effective than Microsoft’s Excel Solver. ZDC-rostering is based on an open-source computer-aided constraint programming package called ZDC, which decouples problem formulation (or modelling) from solution generation in constraint satisfaction. ZDC is equipped with a set of constraint algorithms, including Extended Guided Local Search, whose efficiency and effectiveness have been demonstrated in a wide range of applications. Our experiments show that ZDC-rostering is capable of solving realistic-sized and very tightly-constrained problems efficiently. ZDC-rostering demonstrates the feasibility of applying constraint satisfaction techniques to solving rostering problems, without having to acquire deep knowledge in constraint technology.     

Remarks on a nonlinear transport problem

A nonlinear transport problem of hyperbolic–elliptic type is studied. Estimates of potentials over varying domains and the method of characteristics enable one to treat the initial value problem for Hölder continuous data as an abstract evolution equation via Picard–Lindelöf theorem. In addition, existence for all times is proved. Similar techniques yield the existence of shock front solutions with smooth interfaces at least for a small time interval. By a priori estimates of approximating solutions, the results extend to the case of only bounded initial values. A modification of the system applies to the construction of a diffeomorphism with prescribed Jacobian determinant.     

A multi-level bottleneck assignment approach to the bus drivers' rostering problem

The problem of finding a work assignment for drivers in a given time horizon, in such a way as to have an even distribution of the workload, is considered. This problem is formulated as a Multi-level Bottleneck Assignment Problem (MBA). The MBA problem is studied: it is shown that it is NP-complete and an asymptotically optimal algorithm is presented. Some computational results are illustrated which prove the efficiency of the algorithm.     

A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem

This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and the solution quality can be significantly improved by the careful combination and repeated use of heuristic ordering, variable neighbourhood search and back-tracking. The amount of computational time that is allowed plays a significant role and we analyse and discuss this. The algorithms are evaluated against a commercial Genetic Algorithm on commercial data. We demonstrate that this methodology can significantly outperform the commercial algorithm. This paper is one of the few in the scientific nurse rostering literature which deal with commercial data and which compare against a commercially implemented algorithm.     

Optimal transport and a bilevel location-allocation problem

In this paper a two-stage optimization model is studied to find the optimal location of new facilities and the optimal partition of the consumers (location-allocation problem). The social planner minimizes the social costs, i.e. the fixed costs plus the waiting time costs, taking into account that the citizens are partitioned in the region according to minimizing the capacity costs plus the distribution costs in the service regions. By using optimal transport tools, existence results of solutions to the location-allocation problem are presented together with some examples.     

A relax-and-fix-based algorithm for the vehicle-reservation assignment problem in a car rental company

Empty repositions are a major problem for car rental companies that deal with special types of vehicles whose number of units is small. In order to meet reservation requirements concerning time and location, companies are forced to transfer cars between rental stations, bearing significant costs and increasing the environmental impact of their activity due to the fuel consumption and CO₂${\text{CO}}_2$ emission. In this paper, this problem is tackled under a vehicle-reservation assignment framework as a network-flow model in which the profit is maximized. The reservations are allocated considering the initial and future availability of each car, interdependencies between rental groups, and different reservation priorities. To solve this model, a relax-and-fix heuristic procedure is proposed, including a constraint based on local branching that enables and controls modifications between iterations. Using real instances, the value of this approach is established and an improvement of 33% was achieved when compared to the company’s current practices.     

Monge's transport problem on a Riemannian manifold

Monge's problem refers to the classical problem of optimally transporting mass: given Borel probability measures , find the measure-preserving map between them which minimizes the average distance transported. Set on a complete, connected, Riemannian manifold -- and assuming absolute continuity of -- an optimal map will be shown to exist. Aspects of its uniqueness are also established.

     

Towards a reference model for timetabling and rostering

Timetabling and rostering research often starts from particular real world problems. The last two decennia have seen a large number of papers discussing cases, models and approaches. This large body of publications does not presently constitute a structured domain that provides guidelines for addressing particular problem instances, nor does it allow identifying gaps where new research is needed.     

Nitsche-XFEM for a transport problem in two-phase incompressible flows

We consider the transport of a dissolved species in a divergence-free immiscible incompressible two-phase flow modeled by a convection diffusion equation. The so-called Henry interface condition leads to a jump condition for the concentration at the interface between the two phases. This discontinuity of the solution render the numerical solution on unfitted meshes difficult. Furthermore time discretization on moving interfaces and handling typically convection dominant situations makes the overall problem delicate. We propose a numerical method using extended finite elements and a Nitsche-type technique combined with streamline diffusion stabilization. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Discrete optimization in public rail transport

Many problems arising in traffic planning can be modelled and solved using discrete optimization. We will focus on recent developments which were applied to large scale real world instances. Most railroad companies apply a hierarchically structured planning process. Starting with the definition of the underlying network used for transport one has to decide which infrastructural improvements are necessary. Usually, the rail system is periodically scheduled. A fundamental base of the schedule are the lines connecting several stations with a fixed frequency. Possible objectives for the construction of the line plan may be the minimization of the total cost or the maximization of the passengers’s comfort satisfying certain regulations. After the lines of the system are fixed, the train schedule can be determined. A criterion for the quality of a schedule is the total transit time of the passengers including the waiting time which should be minimized satisfying some operational constraints. For each trip of the schedule a train consisting of a locomotive and some carriages is needed for service. The assignment of rolling stock to schedule trips has to satisfy operational requirements. A comprehensible objective is to minimize the total cost. After all strategic and tactical planning the schedule has to be realized. Several external influences, for example delayed trains, force the dispatcher to recompute parts of the schedule on-line. A Web page with examples quoted in this survey can be found at http://www.math.tu-bs.de/mo/ismp.html.