Less is more approach: basic variable neighborhood search for the obnoxious p-median problem

The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p-median problem (OpMP). More precisely, we developed a basic variable neighborhood search for solving the OpMP, where the single search ingredient, the interchange neighborhood structure, is used. We also propose a new simple local search strategy for solving facility location problems, within the interchange neighborhood structure, which is in between the usual ones: first improvement and best improvement strategies. We call it facility best improvement local search. On the basis of experiments, it appeared to be more efficient and effective than both first and best improvement. According to the results obtained on the benchmark instances, our heuristic turns out to be highly competitive with the existing ones, establishing new state-of-the-art results. For example, four new best-known solutions and 133 ties are claimed in testing the set with 144 instances. © 2019 The Authors. International Transactions in Operational Research © 2019 International Federation of Operational Research Societies15YJC630097Higher Education Discipline Innovation ProjectMinistarstvo Prosvete, Nauke i TehnoloÅ¡kog Razvoja, MPNTR: BR05236839, 044006, 174010National Natural Science Foundation of China, NSFC: 71601065, 71690235, 71521001, 71871080, 71501058This research is partially supported by Serbian Ministry of Education, Science and Technological Development under the grants nos. 174010 and 044006. In addition, this research is partially supported by the framework of the grant number BR05236839 ?Development of information technologies and systems for stimulation of personality's sustainable development as one of the bases of development of digital Kazakhstan? and by the National Natural Science Foundation of China (Nos. 71871080, 71601065, 71690235, 71501058), Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), and the Base of Introducing Talents of Discipline to Universities for Optimization and Decision-Making in the Manufacturing Process of Complex Product (111 project)

Model-based approaches for large-scale optimization in business operations

Companies nowadays have to operate in an increasingly competitive and complex environment. Under these challenging conditions, it has become essential for them to optimize their business operations, i.e., the activities that they must conduct on a regular, often daily, basis. The nature of these business operations strongly varies between companies. For a pharmaceutical company, an important business operation is, for example, the scheduling of their research activities. With improved scheduling, new drugs are brought to markets earlier, which can lead to a decisive competitive advantage. For a telecommunications company, an important business operation is, for example, the promotion of new products and services to existing customers. Contacting the right customers for the right products may lead to an increase in sales and profitability of these products. Many business operations, including the two examples from above, can be improved by solving mathematical optimization problems with techniques from the field of Operations Research. An optimization problem consists of the decisions to be taken, the constraints that define the set of feasible decisions, and an objective that is either maximized (profit) or minimized (project duration). In the case of the telecommunications company, the decisions to be taken are which customers are contacted for which product on which day. An example of a constraint is an overall budget that cannot be exceeded, and an example of the objective is the maximization of the total expected profit that results from contacting the customers. A standard approach for solving such an optimization problem is first to express the problem as a mathematical model and then use standard optimization software, known as a solver, to find the best possible solution. A great advantage of this approach is that the mathematical model can easily be adjusted to changes in the underlying problem. This flexibility is required in a dynamic business environment where constraints or objectives may change over time. However, a major drawback of this standard approach is its limited scalability when applied to specific types of complex optimization problems. For these problems, the generic solvers fail to find the best or even a good solution in a reasonable running time. Specialized algorithms, so-called heuristics, are required instead. Heuristics apply problem-specific search strategies to derive a good solution to an optimization problem quickly. However, because these heuristics are designed for specific optimization problems, they are difficult to adapt if the constraints or the objective of the optimization problem change. A solution technique that has been shown to be both flexible and scalable for complex optimization problems are matheuristics. Matheuristics are model-based approaches that decompose an optimization problem into smaller subproblems and solve these subproblems using mathematical models. Essential for the performance of a matheuristic is how the problem is decomposed into subproblems, which is an important field of research in Operations Research. This thesis contributes to this field of research by introducing model-based approaches for large-scale optimization in business operations. It consists of three papers on three specific optimization problems in direct marketing, project management, and facility location. Real-world instances of all three of these problems involve a large number of customers, activities, or facilities and require the flexibility to incorporate practical constraints easily. To address these challenges, we developed three matheuristics. The matheuristics employ innovative problem decomposition strategies and outperform state-of-the-art approaches on large-scale instances. In the first paper, we study a customer assignment problem from a major telecommunications company. The telecommunications company runs different direct marketing campaigns to promote its products and services. The goal of the telecommunications company is to assign the customers to the direct marketing campaigns so that the total expected profit is maximized. Thereby, various business constraints, such as budgets and sales constraints, must be considered. Also, different customer-specific constraints ensure that each customer is not assigned to a direct marketing campaign too frequently. A particular challenge is the size of practical problem instances. These instances involve millions of customers and hundreds of direct marketing campaigns. The methodological contribution of this paper consists of decomposing the optimization problem into two subproblems that each can be solved efficiently. In the first subproblem, customers are assigned to campaigns based on their membership to a customer group. In the second subproblem, individual customers are assigned to campaigns based on the solution that was derived in the first subproblem. The unique feature of our decomposition strategy is that the customer-specific constraints are already considered in the first subproblem, even though the first subproblem deals with groups of customers and not individual customers. In an experimental analysis based on numerous generated and real-world instances, we can demonstrate that even though we decompose the problem, the resulting solutions are still of very high quality. The matheuristic has been deployed in the company and is now used daily. In a proof of benefit conducted by the company based on a selected campaign, they observed that using the matheuristic increased the number of sales by 90%, resulting in an improvement in the profitability of this campaign by 300%. The second paper deals with a project scheduling problem that often arises in the pharmaceutical industry, where research activities, e.g., clinical tests, can be executed at different locations, e.g., research labs. The problem consists of determining a start time for each activity, selecting a location for the execution of each activity, and assigning resource units, e.g., research staff or equipment, to the execution of the activities. Various practical constraints must be considered, such as transportation times that arise when, e.g., a resource unit must be transported from one location to another. With only a few activities involved, the number of possible schedules can already grow very large. We developed a mathematical model and, based on this model, a novel matheuristic for this problem. The main methodological contribution of the matheuristic is its problem decomposition strategy. Instead of dividing the project into subprojects, the model in the matheuristic is set up for all project activities. However, the solver makes some decisions only for a subset of the activities. To schedule an entire project, multiple iterations have to be performed, where in each iteration, another subset of activities is considered. This iterative decision process substantially reduces running times compared to when all decisions are conducted simultaneously. In a computational experiment, the novel model outperforms the leading model from the literature on small instances. The matheuristic outperforms the state-of-the-art heuristics on all considered performance metrics on larger instances. In the third paper, we consider the problem of locating obnoxious facilities. Obnoxious means that the facilities negatively affect their nearby environment and should thus be located far away from clients. Examples of obnoxious facilities are waste plants, oil refineries, and wind turbines. The problem consists of opening from a set of potential locations a given number of facilities such that the open facilities are far away from the clients. We further study an extension of this problem that includes practical constraints which limit the number of facilities that can be opened in certain regions of an instance. Our matheuristic starts from an initial solution and iteratively improves the solution by removing and adding facilities. The quality of the final solution (after the improvement iterations) strongly depends on the initial solution. When two very similar initial solutions are provided, the likelihood of finding very similar final solutions is high. One main methodological contribution is a procedure that we designed, which is guaranteed to generate initial solutions that are very different from each other. This diversification in the initial solutions increases the likelihood of finding high-quality final solutions. The matheuristic outperforms the state-of-the-art metaheuristics on instances including thousands of clients and potential locations for facilities. Even though we consider three specific optimization problems in this thesis, the contributions of the three papers can be generalized and applied to related problems and thus advance the state of knowledge in the field of large-scale optimization

The obnoxious facilities planar p-median problem

In this paper we propose the planar obnoxious p-median problem. In the p-median problem the objective is to find p locations for facilities that minimize the weighted sum of distances between demand points and their closest facility. In the obnoxious version we add constraints that each facility must be located at least a certain distance from a partial set of demand points because they generate nuisance affecting these demand points. The resulting problem is extremely non-convex and traditional non-linear solvers such as SNOPT are not efficient. An efficient solution method based on Voronoi diagrams is proposed and tested. We also constructed the efficient frontiers of the test problems to assist the planers in making location decisions

Locating a bioenergy facility using a hybrid optimization method

In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Problemas de localização-distribuição de serviços semiobnóxios: aproximações e apoio à decisão

Doutoramento em Gestão IndustrialA presente tese resulta de um trabalho de investigação cujo objectivo se centrou no problema de localização-distribuição (PLD) que pretende abordar, de forma integrada, duas actividades logísticas intimamente relacionadas: a localização de equipamentos e a distribuição de produtos. O PLD, nomeadamente a sua modelação matemática, tem sido estudado na literatura, dando origem a diversas aproximações que resultam de diferentes cenários reais. Importa portanto agrupar as diferentes variantes por forma a facilitar e potenciar a sua investigação. Após fazer uma revisão e propor uma taxonomia dos modelos de localização-distribuição, este trabalho foca-se na resolução de alguns modelos considerados como mais representativos. É feita assim a análise de dois dos PLDs mais básicos (os problema capacitados com procura nos nós e nos arcos), sendo apresentadas, para ambos, propostas de resolução. Posteriormente, é abordada a localização-distribuição de serviços semiobnóxios. Este tipo de serviços, ainda que seja necessário e indispensável para o público em geral, dada a sua natureza, exerce um efeito desagradável sobre as comunidades contíguas. Assim, aos critérios tipicamente utilizados na tomada de decisão sobre a localização destes serviços (habitualmente a minimização de custo) é necessário adicionar preocupações que reflectem a manutenção da qualidade de vida das regiões que sofrem o impacto do resultado da referida decisão. A abordagem da localização-distribuição de serviços semiobnóxios requer portanto uma análise multi-objectivo. Esta análise pode ser feita com recurso a dois métodos distintos: não interactivos e interactivos. Ambos são abordados nesta tese, com novas propostas, sendo o método interactivo proposto aplicável a outros problemas de programação inteira mista multi-objectivo. Por último, é desenvolvida uma ferramenta de apoio à decisão para os problemas abordados nesta tese, sendo apresentada a metodologia adoptada e as suas principais funcionalidades. A ferramenta desenvolvida tem grandes preocupações com a interface de utilizador, visto ser direccionada para decisores que tipicamente não têm conhecimentos sobre os modelos matemáticos subjacentes a este tipo de problemas.This thesis main objective is to address the location-routing problem (LRP) which intends to tackle, using an integrated approach, two highly related logistics activities: the location of facilities and the distribution of materials. The LRP, namely its mathematical formulation, has been studied in the literature, and several approaches have emerged, corresponding to different real-world scenarios. Therefore, it is important to identify and group the different LRP variants, in order to segment current research and foster future studies. After presenting a review and a taxonomy of location-routing models, the following research focuses on solving some of its variants. Thus, a study of two of the most basic LRPs (capacitated problems with demand either on the nodes or on the arcs) is performed, and new approaches are presented. Afterwards, the location-routing of semi-obnoxious facilities is addressed. These are facilities that, although providing useful and indispensible services, given their nature, bring about an undesirable effect to adjacent communities. Consequently, to the usual objectives when considering their location (cost minimization), new ones must be added that are able to reflect concerns regarding the quality of life of the communities impacted by the outcome of these decisions. The location-routing of semi-obnoxious facilities therefore requires to be analysed using multi-objective approaches, which can be of two types: noninteractive or interactive. Both are discussed and new methods proposed in this thesis; the proposed interactive method is suitable to other multi-objective mixed integer programming problems. Finally, a newly developed decision-support tool to address the LRP is presented (being the adopted methodology discussed, and its main functionalities shown). This tool has great concerns regarding the user interface, as it is directed at decision makers who typically don’t have specific knowledge of the underlying models of this type of problems

New heuristic algorithms for solving the planar p-median problem

In this paper we propose effective heuristics for the solution of the planar p-median problem. We develop a new distribution based variable neighborhood search and a new genetic algorithm, and also test a hybrid algorithm that combines these two approaches. The best results were obtained by the hybrid approach. The best known solution was found in 466 out of 470 runs, and the average solution was only 0.000016% above the best known solution on 47 well explored test instances of 654 and 1060 demand points and up to 150 facilities

A MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GENERAL INVERSE ORDERED p-MEDIAN LOCATION PROBLEM ON NETWORKS

This paper is concerned with a general inverse ordered p-median location problem on network where the task is to change (increase or decrease) the edge lengths and vertex weights at minimum cost subject to given modification bounds such that a given set of p vertices becomes an optimal solution of the location problem, i.e., an ordered p-median under the new edge lengths and vertex weights. A modified particle swarm optimization algorithm is designed to solve the problem under the cost functions related to the sum-type Hamming, bottleneck-type Hamming distances and the recti-linear and Chebyshev norms. By computational experiments, the high efficiency of the proposed algorithm is illustrated