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

On multimodality of obnoxious faclity location models

Obnoxious single facility location models are models that have the aim to find the best location for an undesired facility. Undesired is usually expressed in relation to the so-called demand points that represent locations hindered by the facility. Because obnoxious facility location models as a rule are multimodal, the standard techniques of convex analysis used for locating desirable facilities in the plane may be trapped in local optima instead of the desired global optimum. It is assumed that having more optima coincides with being harder to solve. In this thesis the multimodality of obnoxious single facility location models is investigated in order to know which models are challenging problems in facility location problems and which are suitable for site selection. Selected for this are the obnoxious facility models that appear to be most important in literature. These are the maximin model, that maximizes the minimum distance from demand point to the obnoxious facility, the maxisum model, that maximizes the sum of distance from the demand points to the facility and the minisum model, that minimizes the sum of damage of the facility to the demand points. All models are measured with the Euclidean distances and some models also with the rectilinear distance metric. Furthermore a suitable algorithm is selected for testing multimodality. Of the tested algorithms in this thesis, Multistart is most appropriate. A small numerical experiment shows that Maximin models have on average the most optima, of which the model locating an obnoxious linesegment has the most. Maximin models have few optima and are thus not very hard to solve. From the Minisum models, the models that have the most optima are models that take wind into account. In general can be said that the generic models have less optima than the weighted versions. Models that are measured with the rectilinear norm do have more solutions than the same models measured with the Euclidean norm. This can be explained for the maximin models in the numerical example because the shape of the norm coincides with a bound of the feasible area, so not all solutions are different optima. The difference found in number of optima of the Maxisum and Minisum can not be explained by this phenomenon

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

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

Parallel Optimization Algorithm for Competitive Facility Location

A stochastic search optimization algorithm is developed and applied to solve a bi-objective competitive facility location problem for firm expansion. Parallel versions of the developed algorithm for shared- and distributed-memory parallel computing systems are proposed and experimentally investigated by approximating the Pareto front of the competitive facility location problem of different scope. It is shown that the developed algorithm has advantages against its precursor in the sense of the precision of approximation. It is also shown that the proposed parallel versions of the algorithm have almost linear speed-up when solving competitive facility location problems of different scope reasonable for practical applications

A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research

Models and algorithms for the capacitated location-routing problem

Le problème de localisation-routage avec capacités (PLRC) apparaît comme un problème clé dans la conception de réseaux de distribution de marchandises. Il généralisele problème de localisation avec capacités (PLC) ainsi que le problème de tournées de véhicules à multiples dépôts (PTVMD), le premier en ajoutant des décisions liées au routage et le deuxième en ajoutant des décisions liées à la localisation des dépôts. Dans cette thèse on dévelope des outils pour résoudre le PLRC à l’aide de la programmation mathématique. Dans le chapitre 3, on introduit trois nouveaux modèles pour le PLRC basés sur des flots de véhicules et des flots de commodités, et on montre comment ceux-ci dominent, en termes de la qualité de la borne inférieure, la formulation originale à deux indices [19]. Des nouvelles inégalités valides ont été dévelopées et ajoutées aux modèles, de même que des inégalités connues. De nouveaux algorithmes de séparation ont aussi été dévelopés qui dans la plupart de cas généralisent ceux trouvés dans la litterature. Les résultats numériques montrent que ces modèles de flot sont en fait utiles pour résoudre des instances de petite à moyenne taille. Dans le chapitre 4, on présente une nouvelle méthode de génération de colonnes basée sur une formulation de partition d’ensemble. Le sous-problème consiste en un problème de plus court chemin avec capacités (PCCC). En particulier, on utilise une relaxation de ce problème dans laquelle il est possible de produire des routes avec des cycles de longueur trois ou plus. Ceci est complété par des nouvelles coupes qui permettent de réduire encore davantage le saut d’intégralité en même temps que de défavoriser l’apparition de cycles dans les routes. Ces résultats suggèrent que cette méthode fournit la meilleure méthode exacte pour le PLRC. Dans le chapitre 5, on introduit une nouvelle méthode heuristique pour le PLRC. Premièrement, on démarre une méthode randomisée de type GRASP pour trouver un premier ensemble de solutions de bonne qualité. Les solutions de cet ensemble sont alors combinées de façon à les améliorer. Finalement, on démarre une méthode de type détruir et réparer basée sur la résolution d’un nouveau modèle de localisation et réaffectation qui généralise le problème de réaffectaction [48].The capacitated location-routing problem (CLRP) arises as a key problem in the design of distribution networks. It generalizes both the capacitated facility location problem (CFLP) and the multiple depot vehicle routing problem (MDVRP), the first by considering additional routing decisions and the second by adding the location decision variables. In this thesis we use different mathematical programming tools to develop and specialize new models and algorithms for solving the CLRP. In Chapter 3, three new models are presented for the CLRP based on vehicle-flow and commodity-flow formulations, all of which are shown to dominate, in terms of the linear relaxation lower bound, the original two-index vehicle-flow formulation [19]. Known valid inequalities are complemented with some new ones and included using separation algorithms that in many cases generalize extisting ones found in the literature. Computational experiments suggest that flow models can be efficient for dealing with small or medium size instances of the CLRP (50 customers or less). In Chapter 4, a new branch-and-cut-and-price exact algorithm is introduced for the CLRP based on a set-partitioning formulation. The pricing problem is a shortest path problem with resource constraints (SPPRC). In particular, we consider a relaxation of such problem in which routes are allowed to contain cycles of length three or more. This is complemented with the development of new valid inequalities that are shown to be effective for closing the optimality gap as well as to restrict the appearance of cycles. Computational experience supports the fact that this method is now the best exact method for the CLRP. In Chapter 5, we introduce a new metaheuristic with the aim of finding good quality solutions in short or moderate computing times. First, a bundle of good solutions is generated with the help of a greedy randomized adaptive search procedure (GRASP). Following this, a blending procedure is applied with the aim of producing a better upper bound as a combination of all the others in the bundle. An iterative destroy-and-repair method is then applied using a location-reallocation model that generalizes the reallocation model due to de Franceschi et al. [48]