Perbaikan Rute Distribusi Bahan Baku untuk Minimasi Jarak Tempuh dan Beban Kerja pada UKM Kuliner Mie

Delivery activities have a great role for a business actor. However, delivery issues are there all the time. This was experienced by a noodle culinary SME that experienced delays in the delivery of various noodle raw materials and noodle toppings which caused noodle sales activities to be disrupted. In addition, these raw materials have a lifespan (without preservatives) so they must be delivered according to a predetermined schedule and hours. This research will solve this problem by minimizing delays through finding the shortest mileage delivery route. Because each delivery location has its own schedule and time window, the Nearest Neighbor method by paying attention to the time window is used. Based on the calculation results, each distribution route with the shortest distance was obtained, as evidenced by a reduction in the middle route by 0.23 km and the northern route by 5.88 km. In addition, this proposed delivery route was able to save fuel consumption on the middle route by 3% and the northern route by 10%. In terms of the workload aspect, car fleet drivers were also reduced by 31% and motorbike fleet drivers by 47%.Aktivitas pengiriman mempunyai peran serta yang besar bagi sebuah pelaku usaha. Namun, permasalahan pengiriman selalu ada sepanjang waktu. Hal ini dialami oleh sebuah UKM kuliner mie yang mengalami keterlambatan pengiriman bahan baku mie dan topping mie yang beraneka macam yang menyebabkan aktivitas penjualan mie menjadi terganggu. Selain itu, bahan-bahan baku ini memiliki umur (tanpa pengawet) sehingga harus diantar sesuai jadwal dan jam yang telah ditentukan. Penelitian ini akan menyelesaikan permasalahan tersebut dengan meminimalkan adanya keterlambatan melalui pencarian rute pengiriman dengan jarak tempuh terpendek. Oleh karena setiap lokasi pengiriman memiliki jadwal dan time window masing-masing, metode Nearest Neighbor dengan memperhatikan time window pun digunakan. Berdasarkan hasil perhitungan, didapat masing-masing rute distribusi dengan jarak terpendek yang dibuktikan dengan pengurangan rute tengah sebesar 0,23 km dan rute utara sebesar 5.88 km. Selain itu, rute pengiriman usulan ini mampu menghemat konsumsi bahan bakar pada rute tengah sebesar 3% dan rute utara sebesar 10%. Ditinjau dari aspek beban kerja pengemudi armada mobil pun juga berkurang sebesar 31% dan pengemudi armada motor sebesar 47%

Improvement of Raw Material Distribution Routes for Minimization of Mileage and Workload in Noodle Culinary SMEs

Delivery activities have a great role for a business actor. However, delivery issues are there all the time. This was experienced by a noodle culinary SME that experienced delays in the delivery of various noodle raw materials and noodle toppings which caused noodle sales activities to be disrupted. In addition, these raw materials have a lifespan (without preservatives) so they must be delivered according to a predetermined schedule and hours. This research will solve this problem by minimizing delays through finding the shortest mileage delivery route. Because each delivery location has its own schedule and time window, the Nearest Neighbor method by paying attention to the time window is used. Based on the calculation results, each distribution route with the shortest distance was obtained, as evidenced by a reduction in the middle route by 0.23 km and the northern route by 5.88 km. In addition, this proposed delivery route was able to save fuel consumption on the middle route by 3% and the northern route by 10%. In terms of the workload aspect, car fleet drivers were also reduced by 31% and motorbike fleet drivers by 47%

Problemas de localização e roteamento dependentes do tempo

Orientador: Prof. Dr. Arinei Carlos Lindbeck da SilvaTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa : Curitiba, 17/12/2019Inclui referências: p. 93-98Área de concentração: Programação matemáticaResumo: O problema de localização e roteamento é amplamente estudado na literatura. Sua popularidade se deve, principalmente, à importância da logística integrada e aos crescentes esforços para fornecer soluções eficientes para sua resolução. Ao integrar decisões de localização de instalações e roteamento de veículos, o problema visa minimizar o custo total a elas associado. Contudo, ao considerar o custo de roteirização apenas a partir da distância, assume-se uma simplificação que não traduz apropriadamente a realidade. Isso porque há mudanças no tráfego que alteram sensivelmente o tempo de viagem, fazendo com que o percurso mais curto nem sempre seja o mais rápido. Essa alegação deu origem ao problema de roteamento de veículos dependente do tempo e, apesar do interesse crescente por essa variante, a questão ainda apresenta lacunas na área de logística integrada. Diante disso, propõe-se integrar dois tópicos populares da pesquisa: o problema de roteamento de veículos dependente do tempo e o problema de localização e roteamento. O objetivo é definir, modelar e resolver problemas para os quais a velocidade não é considerada constante. O trabalho apresenta a primeira formulação matemática para o problema de localização e roteamento dependente do tempo, fortalecida por um conjunto de desigualdades válidas. Assume-se frota homogênea e limitada, e horário fixo de saída dos veículos para atendimento aos clientes. Busca-se definir o depósito com a melhor localização e as rotas que devem ser executadas para minimizar o tempo total de viagem. Propõe-se ainda um algoritmo mateurístico, que combina heurísticas construtivas com um modelo de particionamento de conjuntos. Extensivos testes computacionais foram conduzidos para comparar as abordagens propostas, em um conjunto de instâncias gerado com base em dados reais de tráfego. Usando um solver comercial, o modelo foi capaz de fornecer soluções para instâncias com até 100 clientes e 15 intervalos de tempo, enquanto as desigualdades válidas melhoraram os limites inferiores. Os resultados evidenciam a importância da integração entre métodos heurísticos e exatos. Além disso, este trabalho contempla ainda uma extensão do problema, com dimensionamento de frota heterogênea. Nela, consideram-se depósitos capacitados, custos fixos de utilização de depósitos e veículos, frota heterogênea e não limitada, e flexibilidade no momento de saída dos veículos nos depósitos. Novamente, duas abordagens de resolução são propostas: uma formulação matemática com um conjunto de desigualdades válidas, e um algoritmo meta-heurístico de três fases baseado em busca evolucionária. Experimentos computacionais foram conduzidos para comparar essas abordagens. Os resultados reforçam a importância das desigualdades válidas na melhoria dos limites inferiores. Eles também mostram que a abordagem meta-heurística é capaz de reduzir consideravelmente o tempo de processamento e elevar a quabdade média das soluções. Palavras-chave: Logística integrada. Roteamento de veículos dependente do tempo. Localização de instalações. Dimensionamento de frota heterogênea.Abstract: Location-routing problem is a widely studied problem in the literature. Its popularity is m ainly due to the importance of the integrated logistics and the increasing efforts to provide efficient solutions to these problems. It integrates facihty location and vehicle routing decisions and as such, it minimizes the facility as well as the routing costs. However, assuming that the routing cost only depends on the distance traveled is an oversimplification which is inconsistent with the reality. In fact, changes in the traffic pattern significantly influence the travel time. Therefore, the shortest route is not always the fastest one. This argument has given rise to the emergence of the time-dependent vehicle routing problem. Despite the recent growing interest for this variant, there is a gap in the integrated logistics and models with this regard. To fulfill this lack of the literature, this thesis proposes to integrate two popular research areas: the time-dependent vehicle routing and the location-routing problems. The purpose of this thesis is to define, model, and solve integrated routing problems in which the speed is not considered constant over time. It presents the first mathematical formulation for the time-dependent location-routing problem, which is strengthened by a set of valid inequalities. A homogeneous and limited fleet is considered while the departure time from the depot to serve the customers is fixed. The objective is to minimize the total travel time while identifying the best location for a single depot and the routes that must be taken to serve customers. Moreover, a matheuristic algorithm Is proposed, which combines constructive heuristics with a set partitioning model. Using a set of instances generated based on real traffic data, extensive computational experiments were conducted to assess the performance of proposed methods. With a commercial solver, the mathematical model was able to provide solutions for instances with up to 100 customers and 15 time intervals, obtaining improved lower bounds using valid inequalities. The results show the benefits of combining heuristic and exact methods. Furthermore, this research extends the problem to the case with heterogeneous fleet sizing. In this problem setting, capacitated depots, fixed costs of depots and vehicles, heterogeneous and non-limited fleet, and flexibility for the vehicle departure time from depots are considered. Once more, two different approaches to solve the problem are proposed: a mathematical formulation, with a set of valid inequalities, and a three-phase evolutionary search-based metaheuristic algorithm. Computational experiments were conducted on a set of instances to compare the performance of the developed methods. The results highlight the importance of valid inequalities to improve the lower bounds. They further indicate how the proposed metaheuristic considerably reduces the execution time and improves the quality of the obtained solutions. Keywords: Integrated logistics. Time-dependent vehicle routing. Facility location. Fleet size and mix

Simheuristics to support efficient and sustainable freight transportation in smart city logistics

La logística urbana intel·ligent constitueix un factor crucial en la creació de sistemes de transport urbà eficients i sostenibles. Entre altres factors, aquests sistemes es centren en la incorporació de dades en temps real i en la creació de models de negoci col·laboratius en el transport urbà de mercaderies, considerant l’augment dels habitants en les ciutats, la creixent complexitat de les demandes dels clients i els mercats altament competitius. Això permet als que planifiquen el transport minimitzar els costos monetaris i ambientals del transport de mercaderies a les àrees metropolitanes. Molts problemes de presa de decisions en aquest context es poden formular com a problemes d’optimació combinatòria. Tot i que hi ha diferents enfocaments de resolució exacta per a trobar solucions òptimes a aquests problemes, la seva complexitat i grandària, a més de la necessitat de prendre decisions instantànies pel que fa a l’encaminament de vehicles, la programació o la situació d’instal·lacions, fa que aquestes metodologies no s’apliquin a la pràctica. A causa de la seva capacitat per a trobar solucions pseudoòptimes en gairebé temps real, els algorismes metaheurístics reben una atenció creixent dels investigadors i professionals com a alternatives eficients i fiables per a resoldre nombrosos problemes d’optimació en la creació de la logística de les ciutats intel·ligents. Malgrat el seu èxit, les tècniques metaheurístiques tradicionals no representen plenament la complexitat dels sistemes més realistes. En assumir entrades (inputs) i restriccions de problemes deterministes, la incertesa i el dinamisme experimentats en els escenaris de transport urbà queden sense explicar. Els algorismes simheurístics persegueixen superar aquests inconvenients mitjançant la integració de qualsevol tipus de simulació en processos metaheurístics per a explicar la incertesa inherent a la majoria de les aplicacions de la vida real. Aquesta tesi defineix i investiga l’ús d’algorismes simheurístics com el mètode més adequat per a resoldre problemes d’optimació derivats de la logística de les ciutats. Alguns algorismes simheurístics s’apliquen a una sèrie de problemes complexos, com la recollida de residus urbans, els problemes de disseny de la cadena de subministrament integrada i els models de transport innovadors relacionats amb la col·laboració horitzontal entre els socis de la cadena de subministrament. A més de les discussions metodològiques i la comparació d’algorismes desenvolupats amb els referents de la bibliografia acadèmica, es mostra l’aplicabilitat i l’eficiència dels algorismes simheurístics en diferents casos de gran escala.Las actividades de logística en ciudades inteligentes constituyen un factor crucial en la creación de sistemas de transporte urbano eficientes y sostenibles. Entre otros factores, estos sistemas se centran en la incorporación de datos en tiempo real y la creación de modelos empresariales colaborativos en el transporte urbano de mercancías, al tiempo que consideran el aumento del número de habitantes en las ciudades, la creciente complejidad de las demandas de los clientes y los mercados altamente competitivos. Esto permite minimizar los costes monetarios y ambientales del transporte de mercancías en las áreas metropolitanas. Muchos de los problemas de toma de decisiones en este contexto se pueden formular como problemas de optimización combinatoria. Si bien existen diferentes enfoques de resolución exacta para encontrar soluciones óptimas a tales problemas, su complejidad y tamaño, además de la necesidad de tomar decisiones instantáneas con respecto al enrutamiento, la programación o la ubicación de las instalaciones, hacen que dichas metodologías sean inaplicables en la práctica. Debido a su capacidad para encontrar soluciones pseudoóptimas casi en tiempo real, los algoritmos metaheurísticos reciben cada vez más atención por parte de investigadores y profesionales como alternativas eficientes y fiables para resolver numerosos problemas de optimización en la creación de la logística de ciudades inteligentes. A pesar de su éxito, las técnicas metaheurísticas tradicionales no representan completamente la complejidad de los sistemas más realistas. Al asumir insumos y restricciones de problemas deterministas, se ignora la incertidumbre y el dinamismo experimentados en los escenarios de transporte urbano. Los algoritmos simheurísticos persiguen superar estos inconvenientes integrando cualquier tipo de simulación en procesos metaheurísticos con el fin de considerar la incertidumbre inherente en la mayoría de las aplicaciones de la vida real. Esta tesis define e investiga el uso de algoritmos simheurísticos como método adecuado para resolver problemas de optimización que surgen en la logística de ciudades inteligentes. Se aplican algoritmos simheurísticos a una variedad de problemas complejos, incluyendo la recolección de residuos urbanos, problemas de diseño de la cadena de suministro integrada y modelos de transporte innovadores relacionados con la colaboración horizontal entre los socios de la cadena de suministro. Además de las discusiones metodológicas y la comparación de los algoritmos desarrollados con los de referencia de la bibliografía académica, se muestra la aplicabilidad y la eficiencia de los algoritmos simheurísticos en diferentes estudios de casos a gran escala.Smart city logistics are a crucial factor in the creation of efficient and sustainable urban transportation systems. Among other factors, they focus on incorporating real-time data and creating collaborative business models in urban freight transportation concepts, whilst also considering rising urban population numbers, increasingly complex customer demands, and highly competitive markets. This allows transportation planners to minimize the monetary and environmental costs of freight transportation in metropolitan areas. Many decision-making problems faced in this context can be formulated as combinatorial optimization problems. While different exact solving approaches exist to find optimal solutions to such problems, their complexity and size, in addition to the need for instantaneous decision-making regarding vehicle routing, scheduling, or facility location, make such methodologies inapplicable in practice. Due to their ability to find pseudo-optimal solutions in almost real time, metaheuristic algorithms have received increasing attention from researchers and practitioners as efficient and reliable alternatives in solving numerous optimization problems in the creation of smart city logistics. Despite their success, traditional metaheuristic techniques fail to fully represent the complexity of most realistic systems. By assuming deterministic problem inputs and constraints, the uncertainty and dynamism experienced in urban transportation scenarios are left unaccounted for. Simheuristic frameworks try to overcome these drawbacks by integrating any type of simulation into metaheuristic-driven processes to account for the inherent uncertainty in most real-life applications. This thesis defines and investigates the use of simheuristics as a method of first resort for solving optimization problems arising in smart city logistics concepts. Simheuristic algorithms are applied to a range of complex problem settings including urban waste collection, integrated supply chain design, and innovative transportation models related to horizontal collaboration among supply chain partners. In addition to methodological discussions and the comparison of developed algorithms to state-of-the-art benchmarks found in the academic literature, the applicability and efficiency of simheuristic frameworks in different large-scaled case studies are shown

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]

Innovative Hybrid Approaches for Vehicle Routing Problems

This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs). The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality. The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community