An assessment of a days off decomposition approach to personnel scheduling

This paper studies a two-phase decomposition approach to solve the personnel scheduling problem. The first phase creates a days off schedule, indicating working days and days off for each employee. The second phase assigns shifts to the working days in the days off schedule. This decomposition is motivated by the fact that personnel scheduling constraints are often divided in two categories: one specifies constraints on working days and days off, while the other specifies constraints on shift assignments. To assess the consequences of the decomposition approach, we apply it to public benchmark instances, and compare this to solving the personnel scheduling problem directly. In all steps we use mathematical programming. We also study the extension that includes night shifts in thefirst phase of the decomposition. We present a detailed results analysis, and analyze the effect of various instance parameters on the decompositions' results. In general, we observe that the decompositions significantly reduce the computation time, and that they produce good solutions for most instances

A tensor based hyper-heuristic for nurse rostering

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances

The falling tide algorithm: A new multi-objective approach for complex workforce scheduling

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making

Job Insertion for the Pickup and Delivery Problem with Time Windows

Two heuristic algorithms are proposed for a practical but relatively under-studied vehicle routing scenario. It requires the insertion of jobs into already planned routes. It occurs when new jobs arrive throughout a day but the current plans are already being performed. The benefit of solving such a problem is providing a better service for collection and delivery jobs whilst also providing better vehicle fill rates and increased revenue for delivery companies. Solutions must be generated quickly because of the dynamic nature of the problem. Two iterative heuristic algorithms are presented and tested on a well-known benchmark set. The algorithms are able to insert new jobs quickly and efficiently and even found some new best known solutions for the benchmark instances

Progress Control in Variable Neighbourhood

Introduction The methods of intensification and diversification are indispensable in successful meta heuristics for local search. Intensification corresponds in some sense to local optimisation; the neighbourhood of a solution is searched intensively for solutions which are better or have better opportunities. On the other hand, diversification tries to escape from (relatively small) neighbourhoods to solutions which might lead to better final results. A heuristic that is well aware of the intensification versus diversification problems, is the Variable Neighbourhood Search (VNS), see [2]. In this method, more than one neighbourhood structure is considered. After finishing intensification with respect to one neighbourhood, the heuristic diversifies to another neighbourhood. In this way one hopes to escape from poor local optima. In this work we introduce a model to predict the quality of a neighbourhood. We use this model to identify `bad' neighbourhoods and avoid searching them. W

Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework

HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem