Dario Landa-Silva's research while affiliated with University of Nottingham and other places
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Publications (104)
Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of the majority class to balance the class distributions. Evolutionary-based approaches are prominent, treatin...
We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scenario bundles, we first calculate a vector of probabilities for every scenario, which measures the association strength of...
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposi...
The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting...
Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario pre...
The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and preferences with products' shelf life. The objective is to avoid food overproduction as this leads to waste and value loss. In addition, product depletion has to...
Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problems such as multi-stage nurse rostering. This is because the large action space that needs to be explored. This paper proposes to replace the value function approximation with a genet...
This paper describes an agent based simulation model to create solutions for the office space allocation (OSA) problem. OSA is a combinatorial optimization problem concerned with the allocation of available office space to a set of entities such as people. The objective function in the OSA problem involves the minimization of space misuse and the m...
Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problem such as multi-stage nurse rostering. This is because the large action space that need to be explored. This paper proposes to replace the value function approximation by a genetic a...
Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario pre...
Learning to Rank (LTR) is one of the problems attracting researchers in Information Retrieval (IR). The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approaches based on machine learning and computational intelligence techniques. Mos...
Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is usually highly constrained and difficult to solve. The problem can be further complicated (i) if wo...
Service network design is used in less-than-truckload (LTL) transportation to address the selection, routing and scheduling of services, with the aim of making good cost-savings
while rendering excellent service. Compared to the intensively studied vehicle routing problems (VRP), service network design provides is a better way to model the freight...
An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a multi-stage problem. In each stage of the problem, a weekly roster is constructed taking into consid...
An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel way to exploit the benefits of each method. The local search component uses a novel neighbourhood operator. A streamlined...
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposi...
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 fitnes...
An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a multi-stage problem. In each stage of the problem, a weekly roster is constructed taking into consid...
We tackle home healthcare planning scenarios in the UK using decomposition methods that incorporate mixed integer programming solvers and heuristics. Home healthcare planning is a difficult problem that integrates aspects from scheduling and routing. Solving real-world size instances of these problems still presents a significant challenge to moder...
In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving...
In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving...
The bus driver scheduling problem (BDSP) under study consists in finding a set of duties that covers the bus schedule from a Brazilian public transportation bus company with the objective of minimizing the total cost. A deterministic 2-phase heuristic algorithm is proposed using multiple assignment problems that arise from a model based on a weight...
Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts the attention from researchers. The LTR problem is mainly about ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There are a number of LTR approaches from the areas of machine learning...
This is the presentation of ES-Rank: Evolutionary Strategy Learning to Rank Approach
Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives, that arise in real-world logistic scenarios, better support for the decision maker can be achieved t...
The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including customers and workers preferences while minimising both operational costs and travelling distance. This...
This paper gives an overview of research work in progress within the COSLE (Collaborative Optimisation in a Shared Logistics Environment) project between the University of Nottingham and Microlise Ltd. This is an R&D project that seeks to develop optimisation technology to enable more efficient collaboration in transportation, particularly real-wor...
http://www.cs.nott.ac.uk/~psxoi/freedocumentcollections.zip
Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature have reported the application of dynamic programming to workforce scheduling problems. This paper inv...
ES-Rank: Evolutionary Strategy Learning to Rank tool. it is a jar file that can be used for learning to rank problems (including ranking in information retrieval, ranking in medical diagnosis among other problems). You can use the tool in windows and Linux command line. For usage details, you can start using this command in command line: java -jar...
The workforce scheduling and routing problem (WSRP) is a combinatorial optimisation problem where a set of workers must perform visits to geographically scattered locations. We present a Variable Neighbourhood Search (VNS) metaheuristic algorithm to tackle this problem, incorporating two novel heuristics tailored to the problem-domain. The first he...
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 (API) to handle the problem data efficiently in all scenarios is impracticable. Nonetheless, on an R&D environment involving personnel from distinct backgr...
In many contexts of Information Retrieval (IR), term weights play an important role in retrieving the relevant documents responding to users' queries. The term weight measures the importance or the information content of a keyword existing in the documents in the IR system. The term weight can be divided into two parts, the Global Term Weight (GTW)...
In many contexts of Information Retrieval (IR), term weights play an important role in retrieving the relevant documents responding to users’ queries. The term weight measures the importance or the information content of a keyword existing in the documents in the IR system. The term weight can be divided into two parts, the Global Term Weight (GTW)...
Literature presents many APIs and frameworks focusing on providing state of the art algorithms and solving techniques for optimisation problems. The same can not be said about APIs and frameworks focused on problem data itself and the reason is simple: due to the peculiarities and details of each variant of a problem, it is virtually impossible to...
In the context of Information Retrieval (IR) from text documents, the term-weighting scheme (TWS) is a key component of the matching mechanism when using the vector space model (VSM). In this paper we propose a new TWS that is based on computing the average term occurrences of terms in documents and it also uses a discriminative approach based on t...
The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling scheduling and routing constraints while aiming to minimise the total operational cost. This paper presents a Genetic Algorithm (GA) tailored to tackle a set of real-world instance...
This paper presents an investigation into the application of heuristic decomposition and mixed-integer programming to tackle workforce scheduling and routing problems (WSRP) that involve time dependent activities constraints. These constraints refer to time-wise dependencies between activities. The decomposition method investigated here is called r...
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...
In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at d...
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...
We present a mixed integer programming decomposition approach to tackle workforce scheduling and routing problems (WSRP) that involve time-dependent activities constraints. The proposed method is called repeated decomposition with conflict repair (RDCR) and it consists of repeatedly applying a phase of problem decomposition and sub-problem solving,...
We propose an approach based on mixed integer programming (MIP) with decomposition to solve a workforce scheduling and routing problem, in which a set of workers should be assigned to tasks that are distributed across different geographical locations. This problem arises from a number of home care planning scenarios in the UK, faced by our industri...
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...
We propose an approach based on mixed integer programming (MIP) with decomposition to solve a workforce scheduling and routing problem, in which a set of workers should be assigned to tasks that are distributed across different geographical locations. We present a mixed integer programming model that incorporates important real-world features of th...
The workforce scheduling and routing problem (WSRP) is a combinatorial optimisation problem where a set of workers must perform visits to geographically scattered locations. We present a Variable Neighbourhood Search (VNS) metaheuristic algorithm to tackle this problem, incorporating two novel heuristics tailored to the problem-domain. The first he...
This paper addresses the Ship Routing and Scheduling Problem with Discretized Time Windows. Being one of the most relevant and challenging problems faced by decision makers from shipping companies, this tramp shipping problem lies in determining the set of contracts that should be served by each ship and the time windows that ships should use to se...
Understanding the relationships between objectives in many-objective optimisation problems is desirable in order to develop more effective algorithms. We propose a techniquefor the analysis and visualisation of complex relationships between many (three or more) objectives. This technique looks at conflicting, harmonious and independent objectives r...
The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational...
We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These types of constraints are important because they allow the modelling of situations in which activities...
This paper introduces a new weighting scheme in information retrieval. It also proposes using the document centroid as a threshold for normalizing documents in a document collection. Document centroid normalization helps to achieve more effective information retrieval as it enables good discrimination between documents. In the context of a machine...
In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at d...
The operations research literature includes some papers describing collaborative work between researchers and industry. However, not much literature exists that outlines methodologies to guide the development of a decision support module and its integration into an existing information management system. Here we describe a framework to aid the coll...
We present a computational study on 112 instances of the Workforce Scheduling and Routing Problem (WSRP). This problem has applications in many service provider industries where employees visit customers to perform activities. Given their similarity, we adapt a mathematical programming model from the literature on vehicle routing problem with time...
A non-planar graph can only be planarised if it is structurally modified. This work presents a new heuristic algorithm that uses vertices deletion to modify a non-planar graph in order to obtain a planar subgraph. The proposed algorithm aims to delete a minimum number of vertices to achieve its goal. The vertex deletion number of a graph G = (V,E)...
Prognostics and Health Management/Monitoring (PHM) are methods to assess the health condition and reliability of systems for the purpose of maximising operational reliability and safety. Recently, PHM systems are emerging in the automotive industry. In the commercial vehicle sector, reducing the maintenance cost and downtime while also improving th...
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 algorit...
This paper presents the first investigation on applying a Particle Swarm Optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics
community. The l...
We present the results of a community survey regarding genetic programming benchmark practices. Analysis shows broad consensus that improvement is needed in problem selection and experimental rigor. While views expressed in the survey dissuade us from ...
In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers' locations, security guards performing rounds at diff...
The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and be...
Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include...
This paper proposes a dynamic lexicographic approach to tackle multi-objective optimization problems. In this method, the order-ing of objectives, which reflects their relative preferences, is changed in a dynamic fashion during the search. This approach eliminates the need for the decision-maker to establish fixed preferences among the competing o...
This paper proposes tackling the difficult course timetabling problem using a multi-agent approach. The proposed design seeks to deal with the problem using a distributed solution environment in which a mediator agent coordinates various timetabling agents that cooperate to improve a common global solution. Initial timetables provided to the multi-...
The Vehicle Routing Problem with Time Windows (VRPTW) is an important logistics problem which in the real-world appears to be multi-objective. Most research in this area has been carried out using classic datasets designed for the single-objective case, like the well-known Solomon's problem instances. Some unrealistic assumptions are usually made w...
This paper describes a hybrid heuristic approach to construct transportation plans for a singlecustomer multi-carrier scenario that arises at 3T Logistics Ltd, a UK company that provides outsourced transportation planning and management services. The problem consists on planning the delivery, using a set of carrier companies, of a set of shipments...
This paper tackles the nurse scheduling problem with balanced preference satisfaction which consists of generating an assignment of shifts to nurses over a given time horizon and ensuring that the satisfaction of nurses' personal preferences for shifts is as even as possible in order to ensure fairness. We propose a heuristic algorithm based on suc...
Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.),
with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical
programming approach is developed to model and generate test instances for this difficult and imp...
This extended abstract outlines four hybrid heuristics to gener-ate initial solutions to the University course timetabling problem. These hybrid approaches combine graph colouring heuristics and local search in different ways. Results of experiments using two benchmark datasets from the literature are presented. All the four hybrid initialisation h...
A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multipl...
The great deluge algorithm explores neighbouring solutions which are accepted if they are better than the best solution so
far or if the detriment in quality is no larger than the current water level. In the original great deluge method, the water
level decreases steadily in a linear fashion. In this paper,we conduct a computational study of a modi...
There is a variety of methods for ranking objectives in multiobjective optimization and some are difficult to define because they require information a priori (e.g. establishing weights in a weighted approach or setting the ordering in a lexicographic approach). In manyobjective optimization problems, those methods may exhibit poor diversification...
This work presents CODEA, a COoperative DEcentralized Architecture for Multiobjective Optimization. CODEA is an objectoriented framework that aims at the creation of groups of agents to tackle complex problems by cooperative search. This cooperation is carried out without any individual controlling the cooperation nor the behaviour of the agents. E...
Universities strive to manage space as effectively as possible. A major part of the management process is the allocation of teaching activities to rooms which have appropriate size, layout and resource availability. Matching the needs of the overall teaching requirement with the space provision within an institution is difficult to achieve and is c...
Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic representation, called Q-bit, to encode individuals in population. Unlike standard evolutionary algorithms, each Q-bit individual is a probability model, which can represent multiple...
We tackle the classroom assignment problem in a large University with the objective of minimising the total distance between all classrooms assigned to teaching activities in the same course. Additional requirements that should be satisfied include: making an efficient utilisation of the space, satisfying room preferences and complying with other a...
We propose a 0/1 integer programming model to tackle the office space allocation (OSA) problem which refers to assigning room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional requirements. In the proposed approach, these requirements can be modelled as...
We describe progress towards a workforce management system in which person-nel scheduling is integrated with other important processes such as payroll processing, atten-dance and absence recording, staffing forecast and planning, etc. Our focus is on customer-oriented sectors in which a considerable proportion of the available workforce is part-tim...
The current investigations on hyper-heuristics design have sprung up in two
different flavours: heuristics that choose heuristics and heuristics that
generate heuristics. In the latter, the goal is to develop a problem-domain
independent strategy to automatically generate a good performing heuristic for
the problem at hand. This can be done, for ex...
The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics
and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically
generate a good performing heuristic for specific problems, that is, the input to the...
This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of re-i...
There is a perception that teaching space in universities is a rather scarce resource. However, some studies have revealed that in many institutions it is actually chronically under-used. Often, rooms are occupied only half the time, and even when in use they are often only half full. This is usually measured by the "utilisation" which is basically...
Technical Report TR/IRIDIA/2009-024, IRIDIA, Universite Libre de Bruxelles, Brussels, Belgium
Course timetabling is the process of allocating, subject to constraints, limited rooms and timeslots for a set of courses to take place. Usually, in addition to constructing a feasible timetable (all constraints satisfied), there are desirable goals like minimising the number of undesirable allocations (e.g. courses timetabled in the last timeslot...
We present a multi-objective approach to tackle a real-world nurse scheduling problem using an evolutionary algorithm. The
aim is to generate a few good quality non-dominated schedules so that the decision-maker can select the most appropriate one.
Our approach is designed around the premise of ‘satisfying individual nurse preferences’ which is of...
We are interested in the role of restricted mating schemes in the context of evolutionary multi-objective algorithms. In this
paper, we propose an adaptive assortative mating scheme that uses similarity in the decision space (genotypic assortative
mating) and adapts the mating pressure as the search progresses. We show that this mechanism improves...
This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the Vehicle Routing Problem with Time Windows (VRPTW). The proposed algorithm incorporates some principles from multi-objective optimization to allow particles to conduct a dynamic trade-off betwee...
Citations
... An effective solution to overcome the problems of the central authority is a decentralized p2p approach for the ride-sharing process (Sánchez et al., 2016). Therefore, some research has exploited the blockchain technology to mitigate the issues of existing client-server architecture of RSS systems (Baza et al., 2019;Kudva et al., 2020;Baza et al., 2020;Vazquez & Landa-silva, 2021;Renu & Banik, 2021). Blockchain is a ledger with the characteristics of immutability, distribution, and traceability. ...
... The approaches to address the imbalanced binary classification problem are categorized into algorithmlevel methods [9][10][11][12] and data-level methods [13][14][15][16][17][18][19][20][21][22][23][24][25]. The algorithm-level solutions solve the decision bias by changing the classification algorithm or the model structure. ...
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... Financial engineering give rise to many challenging problems in portfolio management and cashflow control; see, e.g., [188,87]. The design of service networks takes place under uncertainty about future demand [126]. Demand is likewise uncertain in online and brick-and-mortar retail operations [233]. ...
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... Instead of accuracy, other metrics such as the area under the receiver operating characteristic (ROC) curve, F-measure, or geometric mean (GM) are commonly used to measure performance [22]. For comparison with previous studies [23], [24], we use GM as the performance evaluation metric, i.e.: ...
... The regression results were surprisingly pleasing, which proves that CNNs can be used effectively for time series forecasting. Xue et al. have proposed a combination of the CNN and the Long Short Term Memory (LSTM) model to achieve accurate forecasts [22]. In their proposed approach, a technique to automate the searching of the optimal model architecture has also been proposed. ...
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... The problem concerned in this paper is part of a realworld production planning of highly perishable foods [29] and staff scheduling [30] in an environment with highly variable customers demand. ...
... Since NSP is an NP-hard problem, 25 several heuristic and meta-heuristic approaches have been introduced over the past two decades to reach near-optimal solutions in a reasonable time. Some of these approaches include tabu search (TS), [26][27][28] rolling horizon, 29 harmony search algorithm, 30,31 genetic algorithm, [32][33][34][35] spreadsheet-based two-phase heuristic algorithm, 36 simulated annealing, 37,38 two-stage swapping heuristic coupled with a column generation, 39 hybridization of harmony search algorithm and a hill-climbing optimizer, 40 a hybridization of variable neighborhood search accelerated column generation method and a relax-and-fix heuristic, 41 and a hybrid elitist-ant metaheuristic method. 42,43 Nurses' preferences for shifts, and considering their skills in NSPs, have been explored in several studies, which were mentioned above. ...
... On an individual basis, the complexity of WSRP (Algethami et al., 2019) and VPSP (Bei and Zhang, 2018) are both NP-Hard. As seen in its mathematical model, HHSRP-VS can be considered as difficult as these problems, as it can be treated as a combination of WSRP and VPSP in the context of HHSRP. ...
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... This paper gives a new classi¯cation method and overview for the application area of the studies An SLR for Personnel Scheduling Problems 1707 which are done in personnel scheduling problems. As can be seen in Fig. 7, studies about nurse scheduling, 11 165,166,169,172,176,178,185,190,191,201,233,240,241,292,298,309,310,331,344 and other health service scheduling 28,29,63,83,88,90,130,148,190,229,248,249,265,273,283,297,304,311,312,363,368 are the most popular areas in personnel scheduling problems. ...
... The customers from this structure are inserted into the solution by allowing some customers from the solution to be removed and put in the queue. As in Curtois et al. (2018) the procedure is heuristically sped up. First, to insert one customer, at most two customers can be removed from the solution. ...
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