23 research outputs found

    Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

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    In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Socio-spatial analysis of small-area need and accessibility of Primary Healthcare Services in Nigeria: A sequential mixed methods study

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    Ph. D. Thesis.Accessibility of primary healthcare (PHC) services is crucial for maintaining the good health of a population. Not only is health(care) associated with quality of life and socioeconomic productivity, systematic variations in healthcare accessibility are matters of social justice. Consequently, the overall goal of this project is to comprehensively analyze and explain smallarea need and accessibility of PHC services in Nigeria through a case study of Kogi State, with a view to making policy-relevant recommendations. To this end, both quantitative and qualitative methods are synthesized in an ‘explanatory sequential mixed methods research design’, which also features innovative data exploitations. This entails a synergy of: Automated Zone Design method, Spatial Microsimulation Modelling, and Generalized TwoStep Floating Catchment Area method for quantitative analysis as well as qualitative framework thematic analysis, to obtain research findings that are more robust than existing studies. In this way, genuine small-area variations in PHC need and accessibility are revealed and explicated, including extreme Medically Underserved Areas (eMUAs). Urban areas are more accessible than rural areas, as expected. Of senatorial districts, Kogi Central has the best healthcare accessibility because of its topography. Furthermore, these variations are mediated by the extant sub-optimal zoning system in Nigeria, which is a product of problematic historical political processes. Not only are eMUAs both very remote and rural, they also lack basic social amenities. Hence, it is not surprising that research participants expressed a myriad of dire disincentives in meeting their PHC needs. These difficulties can be mitigated by improving the quality of road infrastructure and ensuring an optimal socio-spatial configuration of PHC services. Broad mechanisms of social exclusion are also implicated in causing access-related disutility of PHC. It is therefore crucial that holistic interventions to alleviate social exclusion are enacted, since previous efforts at addressing only proximal concerns of PHC accessibility have proven unsuccessful. Keywords: Primary Healthcare Needs, Spatial Accessibility, Mixed Methods Research, Automated Zone Design, Spatial Microsimulation, Small-Area Variations.Newcastle University Overseas Research Scholarship (NUORS), Research Excellence Academy of the Humanities and Social Science Faculty (HaSS RE

    Multi-period sales districting problem

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    In the sales districting problem, we are given a set of customers and a set of salesmen in some area. The salesmen have to provide services at the customers' locations to satisfy their requirements. The task is to allocate each customer to one salesman, which partitions the set of customers into subsets, called districts. Each district is expected to have approximately equal workload and travel time for each salesman to promote fairness among them. Also, the districts should be geographically compact since they are more likely to reduce unnecessary travel time, which is desirable for economic reasons. Moreover, each customer can require recurring services with different visiting frequencies such as every week or two weeks during a planning horizon. This problem is called the `Multi-Period Sales Districting Problem (MPSDP)' and can be found typically in regular engineering maintenance and sales promotion. In addition to determining the sales districts, we also want to get valid weekly visiting schedules for the salesmen corresponding to the customers' visiting requirements. The schedules should result in weekly districts with the following desirable characteristics: each weekly district should be balanced in weekly workload and geographically compact. The compactness in the schedules provides benefits when a salesman has to deal with short-term requests from customers or change a visiting plan during the week. Namely, the salesman can postpone a visit to another day if necessary, without increasing the travel time too much compared to the original schedule. This is beneficial when the salesman has to deal with unexpected situations, for example, road maintenance, traffic jams, or short notice of time windows from customers. Although the problem is very practical, it has been studied only recently. Since most of the previous literature on general scheduling problems did not consider compactness, a few recent studies have begun to focus on solving the scheduling part of the problem. The purpose of this research is to develop a more sophisticated exact solution approach as well as an efficient high-quality heuristic to solve the scheduling part. Eventually, with an effective elaborate method to solve the scheduling part, we aim for a robust algorithm to solve the districting and scheduling part of the problem simultaneously. This thesis contains three main parts. The first part introduces the problem and provides a mixed-integer linear programming formulation for only the scheduling part and formulation for the whole problem. The second part presents solution approaches, including an exact method and a heuristic, for only the scheduling part. The last part is dedicated to further development of a successful approach from the second part to solve the districting and scheduling part of the problem simultaneously. For solving the scheduling part, Benders' decomposition is developed as a new exact solution method. The linear relaxation of the problem is strengthened by adding several Benders' cuts derived from fractional solutions at the beginning of the algorithm. Moreover, a good-quality integer solution derived from a location-allocation heuristic is used to generate cuts beforehand, which significantly improves the upper bound of the objective function value. Nondominated optimality cuts are implemented to guarantee the strongest Benders' cuts in each iteration. Also, instead of generating a Benders' cut per iteration, we exploit the decomposable structure of the problem formulation to generate multiple cuts per iteration, resulting in a noticeable improvement in the lower bound of the objective function value. In the classical Benders' decomposition, one of the main factors that slow down the algorithm is that one has to solve the integer programmes from scratch in each iteration. To alleviate this problem, a modern implementation creates only one branch-and-bound tree and adds Benders' cuts derived from a solution in each node in a solution cut pool. This method is called branch-and-Benders' cut. To assess the suitability of the algorithm, we compare its performance on small data instances that contain 30−-50 customers to the Benders' algorithm in CPLEX and show that our algorithm is highly competitive. Since an exact solution method usually struggles to solve realistic large data instances, a meta-heuristic called tabu search is proposed. A high-quality initial solution to start the algorithm is derived from the location-allocation heuristic. Three different neighbourhoods based on information about week centres or customers' week patterns are created within which we search for the best solution. An infeasible solution is allowed in the search to expand the search space. During the search, the size of a whole neighbourhood can be excessively large, so we limit the search to promising areas of the solution space to save computational time. Also, a surrogate objective value is used to save on computational time in cases when computing the real objective value is too time-consuming. Although the tabu search defines a list of forbidden moves to avoid the cycle of solutions, the algorithm can still struggle to avoid being trapped around a local optimum. Therefore, a diversification scheme is proposed for such cases. The algorithm is also accelerated by combining all neighbourhoods and selecting the appropriate neighbourhood for each iteration by a roulette wheel selection. It shows impressive results in small data instances that contain 30−-50 customers. The comparison with built-in heuristics in CPLEX confirms the robustness of the tabu search algorithm. Finally, we combine the tabu search algorithm with our developed Benders' decomposition. Numerical results show that the tabu search method improves the upper bound of the Benders' decomposition algorithm. However, the overall performance is not satisfying so the combination of these two techniques still requires more proper development. As the tabu search algorithm performs well on the scheduling part, it is extended to solve the whole problem, i.e., the districting and scheduling part at the same time. Computational results on large data instances, which contain between 100 and 300 customers, demonstrate its capacity to derive a high-quality solution within a reasonable amount of time, i.e., less than 17 minutes. At the same time, the Benders' decomposition algorithm in CPLEX, which is a benchmark in this case, and the built-in heuristics in CPLEX cannot even find any feasible integer solution for most of the instances within an hour. Importantly, there is a conflict between the districting part and the scheduling part so we recommend solving both parts simultaneously for tackling the MPSDP. The multi-period sales districting problem is highly practical and challenging to solve. To the best of our knowledge, we are the first to propose a single integrated solution approach to solve the whole problem. Further studies including adding more realistic planning requirements into consideration and effective solution approaches to solve the problem are still required

    A benchmark test problem toolkit for multi-objective path optimization

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    Due to the complexity of multi-objective optimization problems (MOOPs) in general, it is crucial to test MOOP methods on some benchmark test problems. Many benchmark test problem toolkits have been developed for continuous parameter/numerical optimization, but fewer toolkits reported for discrete combinational optimization. This paper reports a benchmark test problem toolkit especially for multi-objective path optimization problem (MOPOP), which is a typical category of discrete combinational optimization. With the reported toolkit, the complete Pareto front of a generated test problem of MOPOP can be deduced and found out manually, and the problem scale and complexity are controllable and adjustable. Many methods for discrete combinational MOOPs often only output a partial or approximated Pareto front. With the reported benchmark test problem toolkit for MOPOP, we can now precisely tell how many true Pareto points are missed by a partial Pareto front, or how large the gap is between an approximated Pareto front and the complete one

    Dissecting the local:Territorial Scale and the Social Mechanisms of Place

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    Developing Police Patrol Strategies Based on the Urban Street Network

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    In urban areas, crime and disorder have been long-lasting problems that spoil the economic and emotional well-being of residents. A significant way to deter crime, and maintain public safety is through police patrolling. So far, the deployment of police forces in patrolling has relied mainly on expert knowledge, and is usually based on two-dimensional spatial units, giving insufficient consideration to the underlying urban structure and collaboration among patrol officers. This approach has led to impractical and inefficient police patrol strategies, as well as a workload imbalance among officers. Therefore, it is of essential importance to devise advanced police patrol strategies that incorporate urban structure, the collaboration of the patrol officers, and a workload balance. This study aims to develop police patrol strategies that would make intelligent use of the street network layout in urban areas. The street network is a key component in urban structure and is the domain in which crime and policing take place. By explicitly considering street network configurations in their operations, police forces are enabled to provide timely responses to emergency calls and essential coverage to crime hotspots. Although some models have considered street networks in patrolling to some extent, challenges remain. First, most existing methods for the design of police districts use two-dimensional units, such as grid cells, as basic units, but using streets as basic units would lead to districts that are more accessible and usable. Second, the routing problem in police patrolling has several unique characteristics, such as patrollers potentially starting from different stations, but most existing routing strategies have failed to consider these. Third, police patrolling strategies should be validated using real-world scenarios, whilst most existing strategies in the literature have only been tested in small hypothetical instances without realistic settings. In this thesis, a framework for developing police patrol strategies based on the urban street network is proposed, to effectively cover crime hotspots, as well as the rest of the territory. This framework consists of three strategies, including a districting model, a patrol routing strategy for repeated coverage, and a patrol routing strategy for infrequent coverage. Various relevant factors have been considered in the strategy design, including the underlying structure of the street network and the collaboration among patrollers belonging to different stations. Moreover, these strategies have been validated by the patrolling scenarios in London. The results demonstrate that these strategies outperform the current corresponding benchmark strategies, which indicates that they may have considerable potential in future police operations

    Uso de diferentes algoritmos de otimização para definição de áreas de serviço de esquadras de polícia para Portugal

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceA segurança é considerada um direito fundamental nos estados democráticos. É uma condição que exige cada vez mais ferramentas avançadas de análise espacial, para apoiar a adequação de recursos e a disposição espacial das forças de segurança. A reorganização das forças de segurança no contexto atual depende da distribuição da população e do seu dinamismo. O processo de agrupar pequenas áreas geográficas para formar as áreas de serviço é designado por districting. Esta dissertação tem como objetivo a otimização espacial das forças de segurança tendo em consideração a distribuição espacial da população residente do distrito administrativo de Setúbal. A análise de dados da população residente foi efetuada com a análise hot spot para estudar a sua distribuição ao nível da freguesia. Implementou-se um algoritmo genético e efetuaram-se testes experimentais com os dados da população residente e dos grupos vulneráveis, para criar áreas de serviço das esquadras de polícia. Compararam-se os resultados obtidos com o Automatic Zoning Procedure – Simulated Annealing (AZP-SA) utilizando os mesmos dados. A população do distrito concentra-se sobretudo na península de Setúbal, existindo uma grande assimetria na sua distribuição geográfica. Os testes experimentais com o algoritmo genético demonstram que as áreas de serviço criadas, apresentam uma soma das diferenças da população com uma grande variação. O AZP-SA obteve um desempenho ligeiramente superior ao algoritmo genético implementado. A implementação do algoritmo permitiu obter soluções de áreas de serviço, no entanto, o desempenho do AZP-SA foi ligeiramente superior. A grande assimetria da população do distrito administrativo de Setúbal dificultou a criação de áreas de serviço mais equitativas.Security is to consider being a fundamental right in democratic societies. It is a condition that requires advanced spatial analysis tools to support security forces in the spatial disposition and adequacy of resources. Nowadays security forces reorganization, depends on the population distribution and dynamic. Districting is the process of grouping small geographic areas in service areas. The main objective of this thesis is the spatial optimization of security forces considering the spatial disposition of the population in Setubal administrative district. Data analysis was done with the hot spot analysis to study the population and their distribution at freguesia level. A genetic algorithm was implemented to create service areas and experimental tests were performed with the population data and vulnerable groups. We compared the results with the Automatic Zoning Procedure - Simulated Annealing (AZP-SA). The population concentrates in Setubal peninsula denoting a great asymmetry on their geographical distribution. Experimental tests with the genetic algorithm show a large variation of a sum of the population differences in service areas. AZP-SA performed better than the genetic algorithm. The solutions for the service areas were obtained with the genetic algorithm. However, the performance of AZP-SA is slightly higher. The difficult to obtain equitable areas is due the great asymmetry of the population

    Strategic districting for the mitigation of educational segregation : a pilot model for school district optimization in Helsinki

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    Helsingin kaupunkirakenne on eriytynyt viimeisten vuosikymmenien aikana merkittävästi sosiaalisilla mittareilla tarkasteltuna. Kehitys on heijastunut kouluihin oppilaspohjien ja oppimistuloksien erojen kasvuna, minkä lisäksi Helsingissä on löydetty viitteitä myös itsenäisistä kouluvaikutuksista. Koulujen eriytymiskehityksen pelätään mainevaikutuksen kautta kiihdyttävän alueellista segregaatiota ja siten oppilaspohjien eriytymistä entisestään. Oppilaspohjien eroihin on kuitenkin mahdollista vaikuttaa määrittämällä oppilasalueet uudelleen tavalla, joka minimoi oppilasalueiden välisiä sosiaalisia eroja mahdollisimman tehokkaasti. Tätä varten tarvitaan uudenlaisia, koneoppimiseen perustuvia optimointityökaluja. Tämän opinnäytetyön päätavoitteena on tutkia mahdollisuutta optimoida Helsingin oppilasalueita väestöpohjiltaan sisäisesti heterogeenisemmiksi ja keskenään homogeenisemmiksi. Tavoitetta varten olen kehittänyt työssäni automatisoidun optimointimallin, joka minimoi sosiaalisten muuttujien varianssia oppilasalueiden välillä oppilasalueiden muotoa varioimalla. Mallin pilottisovelluksessa optimoin Helsingin oppilaaksiottoalueita tasaisemmiksi käyttäen optimoitavana muuttujana vieraskielisen väestön osuutta. Olemassa olevaa kouluverkostoa eli koulujen sijaintia, oppilasalueiden maantieteellistä yhtenäisyyttä, enimmäisoppilasmääriä koulukohtaisella marginaalilla sekä koulukohtaista koulumatkan enimmäispituutta on käytetty mallissa alueiden muodostamista rajoittavina tekijöinä. Tutkimukseni keskeinen löydös on, että oppilasaluerajoja siirtelemällä oppilasalueiden sosiaalisen pohjan eroihin voidaan vaikuttaa Helsingissä merkittävästi. Malli vaatii kuitenkin vielä perusteellista jatkokehittämistä soveltuakseen aluejakojen käytännön suunnitteluun, ja tässä vaiheessa sen merkittävimmät kehityskohteet liittyvät optimoitujen alueiden muotoon, mallin laskennalliseen vaativuuteen ja koulumatkojen turvallisuutta mittaavan optimointiparametrin puuttumiseen.The social urban structure of Helsinki has experienced a significant rise in spatial differences during the last two decades. This development has reflected on schools as rising differences between schools’ student compositions and learning outcomes. Additionally, signs of independent school effects have been observed in several studies. The differentiation of student compositions is feared to exacerbate residential segregation and differentiate schools’ operating environments further. It is possible, however, to intervene this development by drawing the school attendance districts such that the social differences between schools’ student compositions are effectively minimized. For this purpose, new machine learning based optimization tools are needed. The main objective of this master’s thesis study is to examine the possibility to optimize Helsinki’s school districts toward more internally heterogeneous and externally homogeneous social compositions. For this purpose, I have developed an optimization model that minimizes the variance of social variables between school districts by iteratively redrawing the districts’ borders. In a pilot application of the model I optimize the school districts of Helsinki by using the share of population with immigrant background as the optimization variable, while existing school infrastructure (the school locations and student capacities), spatial contiguity of the districts, and school-specific maximum travel distances are used as constraints restricting the shapes that the districts can take. The core finding of this study is that in Helsinki, the social compositions of school districts can be significantly evened out by redrawing the school district borders. However, for the model to be suitable for district planning in practice it needs further development. At this stage, the main limitations of the model are related to the shapes of the optimized districts, the model’s time complexity and the lack of a constraint or optimization parameter that accounts for the safety of children’s school trips
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