Ademir Aparecido Constantino

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses’ preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust.

An evolutionary algorithm for the one-dimensional cutting stock problem

This paper deals with the one-dimensional integer cutting stock problem, which consists of cutting a set of available objects in stock in order to produce ordered smaller items in such a way as to minimize the waste of material. The case in which there are various types of objects available in stock in limited quantities is studied. A new heuristic method based on the evolutionary algorithm concept is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and the results are compared with other methods from the literature.

Effective local search algorithms for high school timetabling problems

Abstract This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

Towards an Efficient API for Optimisation Problems Data

The literature presents many application programming interfaces (APIs) and frameworks that provide state of the art algorithms and techniques for solving optimisation problems. The same cannot be said about APIs and frameworks focused on the problem data itself because with the peculiarities and details of each variant of a problem, it is virtually impossible to provide general tools that are broad enough to be useful on a large scale. However, there are benefits of employing problem-centred APIs in a R&D environment: improving the understanding of the problem, providing fairness on the results comparison, providing efficient data structures for different solving techniques, etc. Therefore, in this work we propose a novel design methodology for an API focused on an optimisation problem. Our methodology relies on a data parser to handle the problem specification files and on a set of efficient data structures to handle the information on memory, in an intuitive fashion for researchers and efficient for the solving algorithms. Also, we present the concepts of a solution dispenser that can manage solutions objects in memory better than built-in garbage collectors. Finally, we describe the positive results of employing a tailored API to a project involving the development of optimisation solutions for workforce scheduling and routing problems.

A Variable Neighbourhood Search for Nurse Scheduling with Balanced Preference Satisfaction

The nurse scheduling problem (NSP) is a combinatorial optimisation problem widely tackled in the literature. Recently, a new variant of this problem was proposed, called nurse scheduling problem with balanced preference satisfaction (NSPBPS). This paper further investigates this variant of the NSP as we propose a new algorithm to solve the problem and obtain a better balance of overall preference satisfaction. Initiall, the algorithm converts the problem to a bottleneck assignment problem and solves it to generate an initial feasible solution for the NSPBPS. Posteriorly, the algorithm applies the Variable Neighbourhood Search (VNS) metaheuristic using two sets of search neighbourhoods in order to improve the initial solution. We empirically assess the performance of the algorithm using the NSPLib benchmark instances and we compare our results to other results found in the literature. The proposed VNS algorithm exhibits good performance by achieving solutions that are fairer (in terms of preference satisfaction) for the majority of the scenarios.

Using Goal Programming on Estimated Pareto Fronts to Solve Multiobjective Problems.

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time.

A decomposition-based kernel of Mallows models algorithm for bi- and tri-objective permutation flowshop scheduling problem

Abstract In the last decades, the permutation flowshop scheduling problem (PFSP) has been thoroughly studied in combinatorial optimization and scheduling research. The most common objectives for this problem are makespan, total flowtime, and total tardiness. Furthermore, most production scheduling problems naturally involve multiples conflicting objectives to be optimized. Over the years, several approaches have been proposed to solve multi-objective PFSP. In the multi-objective optimization field of research, the multi-objective evolutionary algorithms based on decomposition has been recognized as one of the main strategies for solving multi-objective optimization problems. In this paper, we present a MOEA that integrates the decomposition approach and a probability model for ranking data (particularly, the Mallows model) for solving PFSP minimizing two and three objectives. We have conducted an extensive experimental study using a well-known benchmark set. In the bi-objective case, the proposed algorithm achieves competitive results compared to the best-known approximated Pareto fronts from the literature. In the tri-objective scenario, our algorithm achieves the best results in comparison to some state-of-the-art decomposition-based algorithms.

An application programming interface with increased performance for optimisation problems data

An optimisation problem can have many forms and variants. It may consider different objectives, constraints, and variables. For that reason, providing a general application programming interface (A...

Combining Heuristic and Utility Function for Fair Train Crew Rostering

In this paper we address the problem of defining a work assignment for train drivers within a monthly planning horizon with even distribution of satisfaction based on a real-would problem. We propose an utility function, in order to measure the individual satisfaction, and a heuristic approach to construct and assign the rosters. In the first phase we apply stated preference methods to devise a utility function. The second phase we apply a heuristic algorithm which constructs and assigns the rosters based on the previous utility function. The heuristic algorithm constructs a cyclic roster in order to find out a minimum number of train drivers required for the job. The cyclic roster generated is divided into different truncated rosters and assigned to each driver in such way the satisfactions should be evenly distributed among all drivers as much as possible. Computational tests are carried out using real data instance of a Brazilian railway company. Our experiments indicated that the proposed method is feasible to reusing the discrepancies between the individual rosters.

Evolutionary Algorithms Applied to Agribusiness Scheduling Problem

This paper addresses a scheduling problem in agribusiness context, especifically about chicken catching. To solve that problem, memetic algorithms combined with local search in a two-phase algorithm were proposed and investigated. Four versions of memetic algorithms were implemented and compared. Also, to apply local search, k-swap and SRP is proposed and experimented. At last we analyze the results, comparing performances. The obtained results show a good improvement in solutions, especially when compared to the manual scheduling actually performed by the company that provides the data to this study.