Algorithms for the multi-objective vehicle routing problem with hard time windows and stochastic travel time and service time

This paper introduces a multi-objective vehicle routing problem with hard time windows and stochastic travel and service times. This problem has two practical objectives: minimizing the operational costs, and maximizing the service level. These objectives are usually conflicting. Thus, we follow a multi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision maker to choose from. We propose two algorithms (a Multi-Objective Memetic Algorithm and a Multi-Objective Iterated Local Search) and compare them to an evolutionary multi-objective optimizer from the literature. We also propose a modified statistical method for the service level calculation. Experiments based on an adapted version of the 56 Solomon instances demonstrate the effectiveness of the proposed algorithms

Methane emission inventory and forecasting in Malaysia

The increase in global surface temperature by 0.74 ± 0.18 oC between 1901 and 2000 as a result of global warming has become a serious threat. It is caused by the emission of greenhouse gases into the atmosphere due to human activities. The major greenhouse gases are carbon dioxide, methane and nitrous oxide. Records show that only carbon dioxide received detailed investigation but not methane, hence the motive behind this study. This study examined the emission of methane from six main sources in Malaysia. Data for the inventories of the production of these six sources were taken from 1980 – 2011 and were used to forecast emissions from 2012 – 2020. The data were sourced from Ministries, Departments and International Agencies. Six categories of animals were studied under livestock with their corresponding methane emissions from 1980 – 2011 computed as follows: cattle: 1993Gg (6.13%), buffaloes: 341Gg (10.8%), sheep: 24Gg (0.8%), goats: 55Gg (1.8%), horses: 3Gg (0.1%), poultry: 161Gg (5.1%), and pigs: 579Gg (18.3%). Methane emissions from the other sources from 1980 to 2011 are rice production: 1617Gg (0.02%), crude oil production: 8016636Gg (99.8%), Wastewater (POME): 11362Gg (0.14%), municipal solid waste landfills: 3294Gg (0.04%), coal mining: 14Gg (0.0002%). Forecasting of methane emissions from 2012 to 2020 were carried out using the Box-Jenkins ARIMA method. There were close similarities between the observed and forecast values. In the year 2020 predicted methane emissions will be cattle: 113Gg (72.2%), buffaloes: 8.0Gg (5.1%), sheep: 1.2Gg (0.8%), goats: 4.2 Gg (2.7%), horses: 0.2Gg (0.1%), pigs: 13.2Gg (8.4%), and poultry: 16.8Gg (10.7%) for the livestock sector. For other sectors the forecast will be wastewater: 836Gg for wastewater, 4.7 Gg for coal production, 503,208 Gg for crude oil production, 50.6 Gg for rice production, and 167 Gg from municipal solid waste landfills. Population and GDP will rise to 33.26 million and 329US $ billion by 2020, respectively. Optimisation was carried out after running a linear regression to determine the significant parameters. The equation developed was a nonlinear programming problem and was solved using sequential quadratic programming (SQL) and implemented on MATLAB environment. Sensitivity analysis carried out on the constraints showed the need to maintain the present livestock and rice production levels. The amount of meat protein currently available far exceeds the dietary protein requirement by more than five times. Several mitigation measures aimed towards reducing future methane emissions in Malaysia were also suggested for the various sources. These are in line with the country’s commitment to reduce greenhouse gas emissions by 40% over the 2005 level by 2020. The use of renewable energy in the energy mix was suggested in line with the government’s five fuel policy and increase in the number of vehicles using gas was also proposed

Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

A biased-randomized iterated local search for the vehicle routing problem with optional backhauls

[EN] The vehicle routing problem with backhauls integrates decisions on product delivery with decisions on the collection of returnable items. In this paper, we analyze a scenario in which collection of items is optional-but subject to a penalty cost. Both transportation costs and penalties associated with non-collecting decisions are considered. A mixed-integer linear model is proposed and solved for small instances. Also, a metaheuristic algorithm combining biased randomization techniques with iterated local search is introduced for larger instances. Our approach yields cost savings and is competitive when compared to other state-of-the-art approaches.This work has been partially supported by COLCIENCIAS - Colombia, the School of Industrial Engineering of Universidad del Valle, the IoF2020, the AGAUR (2018-LLAV-00017), and the Erasmus+ Program (2018-1-ES01-KA103-049767). We also acknowledge the support of the doctoral programs at the Universitat Oberta de Catalunya and the Universidad de La Sabana.Londoño, JC.; Tordecilla, RD.; Do C. Martins, L.; Juan, AA. (2021). A biased-randomized iterated local search for the vehicle routing problem with optional backhauls. Top. 29(2):387-416. https://doi.org/10.1007/s11750-020-00558-x387416292Al Chami Z, El Flity H, Manier H, Manier MA (2018) A new metaheuristic to solve a selective pickup and delivery problem. In: 2018 4th international conference on logistics operations management (GOL), IEEE, pp 1–5Arab R, Ghaderi S, Tavakkoli-Moghaddam R (2018) Bi-objective inventory routing problem with backhauls under transportation risks: two meta-heuristics. Transportation Letters, pp 1–17Assis LP, Maravilha AL, Vivas A, Campelo F, Ramírez JA (2013) Multiobjective vehicle routing problem with fixed delivery and optional collections. Optimiz Lett 7(7):1419–1431Belloso J, Juan AA, Martinez E, Faulin J (2017) A biased-randomized metaheuristic for the vehicle routing problem with clustered and mixed backhauls. Networks 69(3):241–255Belloso J, Juan AA, Faulin J (2019) An iterative biased-randomized heuristic for the fleet size and mix vehicle-routing problem with backhauls. Int Trans Oper Res 26(1):289–301Berbeglia G, Cordeau JF, Gribkovskaia I, Laporte G (2007) Static pickup and delivery problems: a classification scheme and survey. Top 15(1):1–31Brandão J (2016) A deterministic iterated local search algorithm for the vehicle routing problem with backhauls. Top 24(2):445–465Bruck BP, Iori M (2017) Non-elementary formulations for single vehicle routing problems with pickups and deliveries. Oper Res 65(6):1597–1614Caceres-Cruz J, Arias P, Guimarans D, Riera D, Juan AA (2014) Rich vehicle routing problem: survey. ACM Comput Surv (CSUR) 47(2):1–28Calvet L, Juan AA, Serrat C, Ries J (2016) A statistical learning based approach for parameter fine-tuning of metaheuristics. SORT-Stat. Oper. Res. Trans. 1(1):201–224Christofides N, Eilon S (1969) An algorithm for the vehicle-dispatching problem. J Oper Res Soc 20(3):309–318Deif I, Bodin L (1984) Extension of the Clarke and Wright algorithm for solving the vehicle routing problem with backhauling. In: Proceedings of the Babson conference on software uses in transportation and logistics management, Babson Park, MA, pp 75–96Del Ser J, Torre-Bastida AI, Lana I, Bilbao MN, Perfecto C (2017) Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria. In: 2017 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 480–487Dominguez O, Guimarans D, Juan AA, de la Nuez I (2016) A biased-randomised large neighbourhood search for the two-dimensional vehicle routing problem with backhauls. Eur J Oper Res 255(2):442–462Elia V, Gnoni MG (2015) Designing an effective closed loop system for pallet management. Int J Prod Econ 170:730–740Falcon R, Li X, Nayak A, Stojmenovic I (2010) The one-commodity traveling salesman problem with selective pickup and delivery: An ant colony approach. In: IEEE congress on evolutionary computation, IEEE, pp 1–8Fan X, Gong Y, Xu X, Zou B (2019) Optimal decisions in reducing loss rate of returnable transport items. J Clean Prod 214:1050–1060Ferone D, Gruler A, Festa P, Juan AA (2019) Enhancing and extending the classical grasp framework with biased randomisation and simulation. J Oper Res Soc 70(8):1362–1375García-Nájera A, Bullinaria JA, Gutiérrez-Andrade MA (2015) An evolutionary approach for multi-objective vehicle routing problems with backhauls. Comput Ind Eng 81:90–108Gavish B, Graves SC (1978) The travelling salesman problem and related problems (Working Paper). Tech. rep., Massachusetts Institute of Technology, Operations Research CenterGlock CH (2017) Decision support models for managing returnable transport items in supply chains: A systematic literature review. Int J Prod Econ 183:561–569Goetschalckx M, Jacobs-Blecha C (1989) The vehicle routing problem with backhauls. Eur J Oper Res 42(1):39–51Gonzalez-Neira EM, Ferone D, Hatami S, Juan AA (2017) A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times. Simul Model Pract Theory 79:23–36Govindan K, Soleimani H (2017) A review of reverse logistics and closed-loop supply chains: a journal of cleaner production focus. J Clean Prod 142:371–384Grasas A, Juan AA, Faulin J, de Armas J, Ramalhinho H (2017) Biased randomization of heuristics using skewed probability distributions: a survey and some applications. Comput Ind Eng 110:216–228Gribkovskaia I, Laporte G, Shyshou A (2008) The single vehicle routing problem with deliveries and selective pickups. Comput Oper Res 35(9):2908–2924Gruler A, Panadero J, de Armas J, Moreno JA, Juan AA (2018) Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs. Comput Ind Eng 123:278–288Gruler A, Panadero J, de Armas J, Moreno JA, Juan AA (2020) A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands. Int Trans Oper Res 27(1):314–335Gutiérrez-Jarpa G, Desaulniers G, Laporte G, Marianov V (2010) A branch-and-price algorithm for the vehicle routing problem with deliveries, selective pickups and time windows. Eur J Oper Res 206(2):341–349Hellström D, Johansson O (2010) The impact of control strategies on the management of returnable transport items. Transp Res Part E Logist Transp Rev 46(6):1128–1139Ilic A, Ng JW, Bowman P, Staake T (2009) The value of rfid for rti management. Electron Mark 19(2–3):125–135ISO (2016) ISO/IEC 19762:2016. Information technology—Automatic identification and data capture (AIDC) techniques—Harmonized vocabularyJacobs-Blecha C, Goetschalckx M (1992) The vehicle routing problem with backhauls: properties and solution algorithms. Natl Transp Res Board 13Kim T, Glock CH, Kwon Y (2014) A closed-loop supply chain for deteriorating products under stochastic container return times. Omega 43:30–40Koç Ç, Laporte G (2018) Vehicle routing with backhauls: Review and research perspectives. Comput Oper Res 91:79–91Kroon L, Vrijens G (1995) Returnable containers: an example of reverse logistics. Int J Phys Distrib Logist Manag 25(2):56–68Küçükoğlu I, Öztürk N (2015) An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Comput Ind Eng 86:60–68Lin S, Bard JF, Jarrah AI, Zhang X, Novoa LJ (2017) Route design for last-in, first-out deliveries with backhauling. Transp Res Part C Emerg Technol 76:90–117Lourenço HR, Martin OC, Stützle T (2010) Iterated local search: framework and applications. In: Handbook of metaheuristics, Springer, pp 363–397Mahmoudi M, Parviziomran I (2020) Reusable packaging in supply chains: A review of environmental and economic impacts, logistics system designs, and operations management. Int J Prod Econ:107730Martins LC, Hirsch P, Juan A (2020) Agile optimization of a two-echelon vehicle routing problem with pick-up and delivery. Int Trans Oper ResMason A, Shaw A, Al-Shamma’a A (2012) Peer-to-peer inventory management of returnable transport items: a design science approach. Comput Ind 63(3):265–274Öncan T, Altınel K, Laporte G (2009) A comparative analysis of several asymmetric traveling salesman problem formulations. Comput Oper Res 36(3):637–654Panadero J, Currie C, Juan AA, Bayliss C (2020) Maximizing reward from a team of surveillance drones under uncertainty conditions: a simheuristic approachParragh SN, Doerner KF, Hartl RF (2008) A survey on pickup and delivery problems. J für Betriebswirtschaft 58(1):21–51Reil S, Bortfeldt A, Mönch L (2018) Heuristics for vehicle routing problems with backhauls, time windows, and 3D loading constraints. Eur J Oper Res 266(3):877–894Ruiz R, Stützle T (2007) A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur J Oper Res 177(3):2033–2049Soysal M (2016) Closed-loop inventory routing problem for returnable transport items. Transp Res Part D Transp Environ 48:31–45Ting CK, Liao XL (2013) The selective pickup and delivery problem: formulation and a memetic algorithm. Int J Prod Econ 141(1):199–211Ting CK, Liao XL, Huang YH, Liaw RT (2017) Multi-vehicle selective pickup and delivery using metaheuristic algorithms. Inf Sci 406:146–169Tordecilla-Madera R, Roa AP, Escobar JW, Buriticá NC (2018) A mathematical model for collecting and distributing perishable products by considering costs minimisation and $${CO}_2$$ emissions. Int J Serv Oper Manag 31(2):207–234Toth P, Vigo D (1997) An exact algorithm for the vehicle routing problem with backhauls. Transp Sci 31(4):372–385Wassan N (2007) Reactive tabu adaptive memory programming search for the vehicle routing problem with backhauls. J Oper Res Soc 58(12):1630–1641Wu W, Tian Y, Jin T (2016) A label based ant colony algorithm for heterogeneous vehicle routing with mixed backhaul. Appl Soft Comput 47:224–234Zachariadis EE, Kiranoudis CT (2012) An effective local search approach for the vehicle routing problem with backhauls. Expert Syst Appl 39(3):3174–318