A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.Comment: Working Paper -- UFPB, 26 page

Models and Algorithms for the Optimisation of Replenishment, Production and Distribution Plans in Industrial Enterprises

Tesis por compendio[ES] La optimización en las empresas manufactureras es especialmente importante, debido a las grandes inversiones que realizan, ya que a veces estas inversiones no obtienen el rendimiento esperado porque los márgenes de beneficio de los productos son muy ajustados. Por ello, las empresas tratan de maximizar el uso de los recursos productivos y financieros minimizando el tiempo perdido y, al mismo tiempo, mejorando los flujos de los procesos y satisfaciendo las necesidades del mercado. El proceso de planificación es una actividad crítica para las empresas. Esta tarea implica grandes retos debido a los cambios del mercado, las alteraciones en los procesos de producción dentro de la empresa y en la cadena de suministro, y los cambios en la legislación, entre otros. La planificación del aprovisionamiento, la producción y la distribución desempeña un papel fundamental en el rendimiento de las empresas manufactureras, ya que una planificación ineficaz de los proveedores, los procesos de producción y los sistemas de distribución contribuye a aumentar los costes de los productos, a alargar los plazos de entrega y a reducir los beneficios. La planificación eficaz es un proceso complejo que abarca una amplia gama de actividades para garantizar que los equipos, los materiales y los recursos humanos estén disponibles en el momento y el lugar adecuados. Motivados por la complejidad de la planificación en las empresas manufactureras, esta tesis estudia y desarrolla herramientas cuantitativas para ayudar a los planificadores en los procesos de la planificación del aprovisionamiento, producción y distribución. Desde esta perspectiva, se proponen modelos realistas y métodos eficientes para apoyar la toma de decisiones en las empresas industriales, principalmente en las pequeñas y medianas empresas (PYMES). Las aportaciones de esta tesis suponen un avance científico basado en una exhaustiva revisión bibliográfica sobre la planificación del aprovisionamiento, la producción y la distribución que ayuda a comprender los principales modelos y algoritmos utilizados para resolver estos planes, y pone en relieve las tendencias y las futuras direcciones de investigación. También proporciona un marco holístico para caracterizar los modelos y algoritmos centrándose en la planificación de la producción, la programación y la secuenciación. Esta tesis también propone una herramienta de apoyo a la decisión para seleccionar un algoritmo o método de solución para resolver problemas concretos de la planificación del aprovisionamiento, producción y distribución en función de su complejidad, lo que permite a los planificadores no duplicar esfuerzos de modelización o programación de técnicas de solución. Por último, se desarrollan nuevos modelos matemáticos y enfoques de solución de última generación, como los algoritmos matheurísticos, que combinan la programación matemática y las técnicas metaheurísticas. Los nuevos modelos y algoritmos comprenden mejoras en términos de rendimiento computacional, e incluyen características realistas de los problemas del mundo real a los que se enfrentan las empresas de fabricación. Los modelos matemáticos han sido validados con un caso de una importante empresa del sector de la automoción en España, lo que ha permitido evaluar la relevancia práctica de estos novedosos modelos utilizando instancias de gran tamaño, similares a las existentes en la empresa objeto de estudio. Además, los algoritmos matheurísticos han sido probados utilizando herramientas libres y de código abierto. Esto también contribuye a la práctica de la investigación operativa, y proporciona una visión de cómo desplegar estos métodos de solución y el tiempo de cálculo y rendimiento de la brecha que se puede obtener mediante el uso de software libre o de código abierto.[CA] L'optimització a les empreses manufactureres és especialment important, a causa de les grans inversions que realitzen, ja que de vegades aquestes inversions no obtenen el rendiment esperat perquè els marges de benefici dels productes són molt ajustats. Per això, les empreses intenten maximitzar l'ús dels recursos productius i financers minimitzant el temps perdut i, alhora, millorant els fluxos dels processos i satisfent les necessitats del mercat. El procés de planificació és una activitat crítica per a les empreses. Aquesta tasca implica grans reptes a causa dels canvis del mercat, les alteracions en els processos de producció dins de l'empresa i la cadena de subministrament, i els canvis en la legislació, entre altres. La planificació de l'aprovisionament, la producció i la distribució té un paper fonamental en el rendiment de les empreses manufactureres, ja que una planificació ineficaç dels proveïdors, els processos de producció i els sistemes de distribució contribueix a augmentar els costos dels productes, allargar els terminis de lliurament i reduir els beneficis. La planificació eficaç és un procés complex que abasta una àmplia gamma d'activitats per garantir que els equips, els materials i els recursos humans estiguen disponibles al moment i al lloc adequats. Motivats per la complexitat de la planificació a les empreses manufactureres, aquesta tesi estudia i desenvolupa eines quantitatives per ajudar als planificadors en els processos de la planificació de l'aprovisionament, producció i distribució. Des d'aquesta perspectiva, es proposen models realistes i mètodes eficients per donar suport a la presa de decisions a les empreses industrials, principalment a les petites i mitjanes empreses (PIMES). Les aportacions d'aquesta tesi suposen un avenç científic basat en una exhaustiva revisió bibliogràfica sobre la planificació de l'aprovisionament, la producció i la distribució que ajuda a comprendre els principals models i algorismes utilitzats per resoldre aquests plans, i posa de relleu les tendències i les futures direccions de recerca. També proporciona un marc holístic per caracteritzar els models i algorismes centrant-se en la planificació de la producció, la programació i la seqüenciació. Aquesta tesi també proposa una eina de suport a la decisió per seleccionar un algorisme o mètode de solució per resoldre problemes concrets de la planificació de l'aprovisionament, producció i distribució en funció de la seua complexitat, cosa que permet als planificadors no duplicar esforços de modelització o programació de tècniques de solució. Finalment, es desenvolupen nous models matemàtics i enfocaments de solució d'última generació, com ara els algoritmes matheurístics, que combinen la programació matemàtica i les tècniques metaheurístiques. Els nous models i algoritmes comprenen millores en termes de rendiment computacional, i inclouen característiques realistes dels problemes del món real a què s'enfronten les empreses de fabricació. Els models matemàtics han estat validats amb un cas d'una important empresa del sector de l'automoció a Espanya, cosa que ha permés avaluar la rellevància pràctica d'aquests nous models utilitzant instàncies grans, similars a les existents a l'empresa objecte d'estudi. A més, els algorismes matheurístics han estat provats utilitzant eines lliures i de codi obert. Això també contribueix a la pràctica de la investigació operativa, i proporciona una visió de com desplegar aquests mètodes de solució i el temps de càlcul i rendiment de la bretxa que es pot obtindre mitjançant l'ús de programari lliure o de codi obert.[EN] Optimisation in manufacturing companies is especially important, due to the large investments they make, as sometimes these investments do not obtain the expected return because the profit margins of products are very tight. Therefore, companies seek to maximise the use of productive and financial resources by minimising lost time and, at the same time, improving process flows while meeting market needs. The planning process is a critical activity for companies. This task involves great challenges due to market changes, alterations in production processes within the company and in the supply chain, and changes in legislation, among others. Planning of replenishment, production and distribution plays a critical role in the performance of manufacturing companies because ineffective planning of suppliers, production processes and distribution systems contributes to higher product costs, longer lead times and less profits. Effective planning is a complex process that encompasses a wide range of activities to ensure that equipment, materials and human resources are available in the right time and the right place. Motivated by the complexity of planning in manufacturing companies, this thesis studies and develops quantitative tools to help planners in the replenishment, production and delivery planning processes. From this perspective, realistic models and efficient methods are proposed to support decision making in industrial companies, mainly in small- and medium-sized enterprises (SMEs). The contributions of this thesis represent a scientific breakthrough based on a comprehensive literature review about replenishment, production and distribution planning that helps to understand the main models and algorithms used to solve these plans, and highlights trends and future research directions. It also provides a holistic framework to characterise models and algorithms by focusing on production planning, scheduling and sequencing. This thesis also proposes a decision support tool for selecting an algorithm or solution method to solve concrete replenishment, production and distribution planning problems according to their complexity, which allows planners to not duplicate efforts modelling or programming solution techniques. Finally, new state-of-the-art mathematical models and solution approaches are developed, such as matheuristic algorithms, which combine mathematical programming and metaheuristic techniques. The new models and algorithms comprise improvements in computational performance terms, and include realistic features of real-world problems faced by manufacturing companies. The mathematical models have been validated with a case of an important company in the automotive sector in Spain, which allowed to evaluate the practical relevance of these novel models using large instances, similarly to those existing in the company under study. In addition, the matheuristic algorithms have been tested using free and open-source tools. This also helps to contribute to the practice of operations research, and provides insight into how to deploy these solution methods and the computational time and gap performance that can be obtained by using free or open-source software.This work would not have been possible without the following funding sources: Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana for hiring predoctoral research staff with Grant (ACIF/2018/170) and the European Social Fund with the Grant Operational Programme of FSE 2014-2020. Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana for predoctoral contract students to stay in research centers outside the research centers outside the Valencian Community (BEFPI/2021/040) and the European Social Fund.Guzmán Ortiz, BE. (2022). Models and Algorithms for the Optimisation of Replenishment, Production and Distribution Plans in Industrial Enterprises [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/187461Compendi

The Two-Echelon Vehicle Routing Problem with Pickups, Deliveries, and Deadlines

This paper introduces the Two-Echelon Vehicle Routing Problem with Pickups, Deliveries, and Deadlines (2E-VRP-PDD), a new and emerging routing variant addressing the operations of logistics companies connecting consumers and suppliers in megacities. Logistics companies typically organize their logistics in such megacities via multiple geographically dispersed two-echelon distribution systems. The 2E-VRP-PDD is the practical problem that needs to be solved within each of such a single two-echelon distribution setting, thereby merging first and last-mile logistics operations. Specifically, it integrates forward flow, reverse flow, and vehicle time-synchronization aspects such as parcel time windows, satellite synchronization, and customer-dependent deadlines on the arrival of parcels at the hub. We solve the 2E-VRP-PDD with a tailored matheuristic that combines a newly developed Adaptive Large Neighborhood Search (ALNS) with a set-partitioning model. We show that our ALNS provides high-quality solutions on established benchmark instances from the literature. On a new benchmark set for the 2E-VRP-PDD, we show that loosening or tightening time restrictions, such as parcel delivery deadlines at the city hub, can lead to an 8.5% cost increase; showcasing the overhead associated with same-day delivery compared to next-day delivery operations. Finally, we showcase the performance of our matheuristic based on real-life instances which we obtained from our industry collaborator in Jakarta, Indonesia. On these instances, which we share publicly and consists of 1500 - 2150 customers, we show that using our ALNS can significantly improve current operations, leading to a 17% reduction in costs

Models and matheuristics for the unrelated parallel machine scheduling problem with additional resources

[EN] In this paper we analyze a parallel machine scheduling problem in which the processing of jobs on the machines requires a number of units of a scarce resource. This number depends both on the job and on the machine. The availability of resources is limited and fixed throughout the production horizon. The ob- jective considered is the minimization of the makespan. We model this problem by means of two integer linear programming problems. One of them is based on a model previously proposed in the literature. The other one, which is based on the resemblance to strip packing problems, is an original contribution of this paper. As the models presented are incapable of solving medium-sized instances to optimality, we propose three matheuristic strategies for each of these two models. The algorithms proposed are tested over an extensive computational experience. Results show that the matheuristic strategies significantly outperform the mathematical models.The authors are supported by the Spanish Ministry of Economy and Competitiveness, under project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R), partially financed with FEDER funds. Thanks are due to our colleagues Eva Vallada and Ful Villa, for their useful suggestions. Special thanks are due to three anonymous referees which have significantly contributed to the improvement of the manuscript. Apart from accompanying on-line materials, interested readers can download more contents from http://soa.iti.es/problem-instances, like the instances used, software for generating instances and all the binaries of the algorithms tested in this paper. We also provide complete solutions, full tables of results and the statistics software files to replicate all results and plots. Additional explanations are also provided in "how-to" text files.Perea Rojas Marcos, F.; Fanjul Peyró, L.; Ruiz García, R. (2017). Models and matheuristics for the unrelated parallel machine scheduling problem with additional resources. European Journal of Operational Research. 260(2):482-493. https://doi.org/10.1016/j.ejor.2017.01.002S482493260

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

Driver Routing and Scheduling with Synchronization Constraints

This paper investigates a novel type of driver routing and scheduling problem motivated by a practical application in long-distance bus networks. A key difference from other crew scheduling problems is that drivers can be exchanged between buses en route. These exchanges may occur at arbitrary intermediate stops such that our problem requires additional synchronization constraints. We present a mathematical model for this problem that leverages a time-expanded multi-digraph and derive bounds for the total number of required drivers. Moreover, we develop a destructive-bound-enhanced matheuristic that converges to provably optimal solutions and apply it to a real-world case study for Flixbus, one of Europe's leading coach companies. We demonstrate that our matheuristic outperforms a standalone MIP implementation in terms of solution quality and computational time and improves current approaches used in practice by up to 56%. Our solution approach provides feasible solutions for all instances within seconds and solves instances with up to 390 locations and 70 requests optimally with an average computational time under 210 seconds. We further study the impact of driver exchanges on personnel costs and show that allowing for such exchanges leads to savings of up to 75%

Multi-period Stochastic Network Design for Combined Natural Gas and Hydrogen Distribution

Hydrogen is produced from water using renewable electricity. Unlike electricity, hydrogen can be stored in large quantities for long periods. This storage ability acts as a \textit{green battery}, allowing solar and wind energy to be generated and used at different times. As a result, green hydrogen plays a central role in facilitating a climate-neutral economy. However, the logistics for hydrogen are complex. As new hydrogen pipelines are developed for hydrogen, there is a trend toward repurposing the natural gas network for hydrogen, due to its economic and environmental benefits. Yet, a rapid conversion could disrupt the balance of natural gas supply and demand. Furthermore, technical and economic developments surrounding the transition contribute additional complexity, which introduces uncertainty in future supply and demand levels for both commodities. To address these challenges, we introduce a multi-period stochastic network design problem for the transition of a natural gas pipeline network into a green hydrogen pipeline network. We develop a progressive hedging based matheuristic to solve the problem. Results demonstrate our matheuristic is efficient, both in computation time and in solution quality. We show that factoring in uncertainty avoids premature expansion and ensures the development of an adequate pipeline network meeting long-term needs. In a case study in the Northern Netherlands for \textit{Hydrogen Energy Applications in Valley Environments for Northern Netherlands} initiative, we focus on two key scenarios: local production and importation, exploring their impacts on performance indicators. Our case insights exemplify the solid foundation for strategic decision-making in energy transitions through our approach