Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city routes served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads there may exist opportunities for carriers to collaborate over such routes. That is, Carrier A will also deliver one or more shipments of Carrier B. This will improve vehicle asset utilization and reduce asset-repositioning costs, and may also lead to reduced congestion and pollution in cities. We refer to the above coordination as “collaborative routing”. In our framework for collaboration, we also propose that carriers exchange goods at logistics platforms located at the entry point to a city. This is referred to as “entry-point collaboration”. One difficulty in collaboration is the lack of facilities to allow transfer of goods between carriers. We highlight that the reduction in pollution and congestion under our proposed framework will give the city government an incentive to support these initiatives by providing facilities. Further, our analysis has shown that contrary to the poor benefits reported by previous work on vehicle routing with transshipment, strategic location of transshipment facilities in urban areas may solve this problem and lead to large cost savings from transfer of loads between carriers. We also present a novel integrated three-phase solution method. Our first phase uses either a modified tabu search, or a guided local search, to solve the vehicle routing problems with time windows that result from entry-point collaboration. The preceding methods use a constraint programming engine for feasibility checks. The second phase uses a quad-tree search to locate facilities. Quad-tree search methods are popular in computer graphics, and for grid generation in fluid simulation. These methods are known to be efficient in partitioning a two-dimensional space for storage and computation. We use this efficiency to search a two-dimensional region and locate possible transshipment facilities. In phase three, we employ an integrated greedy local search method to build collaborative routes, using three new transshipment-specific moves for neighborhood definition. We utilize an optimization module within local search to combine multiple moves at each iteration, thereby taking efficient advantage of information from neighborhood exploration. Extensive computational tests are done on random data sets which represent a city such as Toronto. Sensitivity analysis is performed on important parameters to characterize the situations when collaboration will be beneficial. Overall results show that our proposal for collaboration leads to 12% and 15% decrease in route distance and time, respectively. Average asset utilization is seen to increase by about 5% as well.

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city routes served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads there may exist opportunities for carriers to collaborate over such routes. That is, Carrier A will also deliver one or more shipments of Carrier B. This will improve vehicle asset utilization and reduce asset-repositioning costs, and may also lead to reduced congestion and pollution in cities. We refer to the above coordination as “collaborative routing”. In our framework for collaboration, we also propose that carriers exchange goods at logistics platforms located at the entry point to a city. This is referred to as “entry-point collaboration”. One difficulty in collaboration is the lack of facilities to allow transfer of goods between carriers. We highlight that the reduction in pollution and congestion under our proposed framework will give the city government an incentive to support these initiatives by providing facilities. Further, our analysis has shown that contrary to the poor benefits reported by previous work on vehicle routing with transshipment, strategic location of transshipment facilities in urban areas may solve this problem and lead to large cost savings from transfer of loads between carriers. We also present a novel integrated three-phase solution method. Our first phase uses either a modified tabu search, or a guided local search, to solve the vehicle routing problems with time windows that result from entry-point collaboration. The preceding methods use a constraint programming engine for feasibility checks. The second phase uses a quad-tree search to locate facilities. Quad-tree search methods are popular in computer graphics, and for grid generation in fluid simulation. These methods are known to be efficient in partitioning a two-dimensional space for storage and computation. We use this efficiency to search a two-dimensional region and locate possible transshipment facilities. In phase three, we employ an integrated greedy local search method to build collaborative routes, using three new transshipment-specific moves for neighborhood definition. We utilize an optimization module within local search to combine multiple moves at each iteration, thereby taking efficient advantage of information from neighborhood exploration. Extensive computational tests are done on random data sets which represent a city such as Toronto. Sensitivity analysis is performed on important parameters to characterize the situations when collaboration will be beneficial. Overall results show that our proposal for collaboration leads to 12% and 15% decrease in route distance and time, respectively. Average asset utilization is seen to increase by about 5% as well.

A necessidade de redução de custos logísticos tem obrigado as empresas a colaborar entre si. O problema de logística colaborativa aqui enfocado é assim definido: identificar ciclos (ou seja, um percurso fechado) em um conjunto de rotas de carga de lotação (onde o caminhão coleta carga em um ponto e vai diretamente ao local de descarga, pois é completamente preenchido) de vários embarcadores de forma a minimizar o reposicionamento (isto é, viagens sem carga útil) de caminhões, dado que o subconjunto de rotas de um determinado embarcador pode conter rotas que complementam aquelas de outro. Desta maneira, vários embarcadores combinados podem oferecer aos transportadores um conjunto de ciclos com movimentação regular de veículos com carga completa e com mínimo reposicionamento. Esse problema pode ser modelado como um problema particular de cobertura de conjuntos com restrição de ciclos, o problema de cobertura de rotas com cardinalidade restrita (PCRCR), que é NP-Hard. Este estudo apresenta uma heurística alternativa que obtém resultados, em média, 1,74% melhores que a literatura existente, além de solucionar instâncias maiores. Ademais, o tempo de execução da heurística cresce de forma polinomial em função do tamanho do problema, ao contrário dos demais métodos aqui avaliados, que possuem comportamento exponencial. / Cost and sustainability imperatives are compelling reasons to make companies to collaborate with each other in order to operate more efficiently. The shipper collaboration problem can be defined as how to identify tours (i.e. a closed path) in a set of lanes from various shippers that minimize truck repositioning (deadheads), as the sub-set of routes from a single shipper may have lanes that complement the routes of another shipper. Thus, combined shippers may offer to carriers a set of tours with regularly executed truckload movements (where the truck loads at a point and go directly to the disposal location) with minimum asset repositioning. This problem can be modeled as a particular case of the set covering formulation with constrained cycles, the cardinality constrained lane covering problem (CCLCP), which is NP-hard. This work resents an alternative heuristic that obtains results about 1.74% better than the existing literature, and solves larger instances. Besides, the heuristics execution time presents polynomial growth, unlike other methods that have exponential behavior.

Empresas estão procurando reduzir seus custos e aumentar seu desempenho e competitividade. Neste cenário de redução de custos, a logística colaborativa pode ser uma aliada. Numa rede complexa, onde embarcadores muitas vezes nem sabem da existência de outros embarcadores com demandas complementares, existe um potencial de sinergia e redução de custos através da diminuição de deslocamentos de veículos sem carga, ou seja, deslocamentos para reposicionar os veículos. Visando essa redução, o Problema de Cobertura de Rotas (PCR), que tem como objetivo cobrir rotas no mínimo custo, une as demandas de frete de vários embarcadores e tenta minimizar os deslocamentos sem cargas (reposicionamentos), reduzindo assim o custo total de toda a rede dos embarcadores envolvidos. Esta pesquisa propõe um modelo e uma heurística para resolver, em grande escala através de programação paralela, uma expansão do PCR. / Companies are looking to reduce costs and improve performance and competitiveness. In this cost-cutting scenario, collaborative logistics can be an ally. In a complex network where shippers often do not know of the existence of other shippers with complementary demands, there is potential for synergy and cost savings by reducing unloaded travelling of vehicles, i.e, the distance and time to reposition the vehicles\'. Towards that reduction, the Lane Covering Problem (LCP), which aims to cover at least cost routeslinks the various shippers\' demands of freight and tries to minimize operations without loads (repositioning), thus reducing the total cost of the entire network for involved shippers. This research proposes a model and an heuristic to solve, in large-scale through parallel programming, an expansion of the PCR.

A crescente necessidade de buscar operações mais eficientes, com menor custo e mais sustentáveis tem feito com que empresas passassem a procurar oportunidades pelas quais estes objetivos pudessem ser atingidos. Na área de transportes encontrou-se na colaboração uma oportunidade para tal. Este trabalho trata o problema de cobertura rotas com cardinalidade restrita (PCRCR), onde empresas que realizam viagens de carga cheia se unem com o objetivo de reduzir o deslocamento vazio de veículos através da formação de ciclos. É chamado de problema de cardinalidade restrita uma vez que limitamos o número de máximo de viagens no ciclo, o que torna este problema NP-Hard. Existem na literatura duas heurísticas (construtivas) e um modelo por programação linear inteira para a solução deste problema. Este trabalho apresenta uma heurística baseada em um método de busca local que reduziu em média 3,19% os melhores resultados apresentados na literatura. Também são apresentados os tempos de execução de cada um dos algoritmos e a importância de escolher de uma boa solução inicial quando se deseja implantar uma Heurística com Busca Local. / The growing need to seek more efficient, lower cost and more sustainable operations has caused industries to seek opportunities in which these objectives could be achieved. In the area of transportation, collaboration is an opportunity for that. This work deals with the cardinality constrained lane covering problem (CCLCP), where companies who uses full truck loads join efforts in order to reduce empty vehicle travel through closed cycle formation. It is known as cardinality constraint problem as the maximum number of trips in the cycle is limited to an integer number, which makes this problem NP-Hard. There are two heuristics in the literature (constructive) and an integer linear programming model for solving this problem. This work presents a heuristic based on a local search method that reduced an average of 3.19% the better results in the literature. It also presents the execution times of each algorithm and the importance of choosing a good initial solution when you want to create a Local Search Heuristic.

As a result of widespread and effective usage of internet, firms tend to collaborate to reduce their operating costs. This thesis analyzes collaboration opportunities for a group of small shippers. A transportation intermediary determining the optimal actions for arriving shippers and a mechanism allocating savings to the shippers is proposed in the thesis. The performance of the intermediary is assessed by using computational analyses. An experimental set is formed that is by changing the parameters that are expected to significantly affect the optimal policy structure and the surplus budget (or deficit) changes. It is seen that increasing variable costs like cross-assignment cost and waiting cost leads to the increase in comparative performance of the optimal policy compared to the na&iuml / ve policy, which is defined according to a simple rule, although increasing dispatching cost, which can be considered as a fixed cost, leads to an opposite result. The performance of the optimal policy is also assessed by using a myopic policy, in which shippers are trying to maximize their own benefit without considering the overall benefit of the grand coalition.

Empresas estão procurando reduzir seus custos e aumentar seu desempenho e competitividade. Neste cenário de redução de custos, a logística colaborativa pode ser uma aliada. Numa rede complexa, onde embarcadores muitas vezes nem sabem da existência de outros embarcadores com demandas complementares, existe um potencial de sinergia e redução de custos através da diminuição de deslocamentos de veículos sem carga, ou seja, deslocamentos para reposicionar os veículos. Visando essa redução, o Problema de Cobertura de Rotas (PCR), que tem como objetivo cobrir rotas no mínimo custo, une as demandas de frete de vários embarcadores e tenta minimizar os deslocamentos sem cargas (reposicionamentos), reduzindo assim o custo total de toda a rede dos embarcadores envolvidos. Esta pesquisa propõe um modelo e uma heurística para resolver, em grande escala através de programação paralela, uma expansão do PCR. / Companies are looking to reduce costs and improve performance and competitiveness. In this cost-cutting scenario, collaborative logistics can be an ally. In a complex network where shippers often do not know of the existence of other shippers with complementary demands, there is potential for synergy and cost savings by reducing unloaded travelling of vehicles, i.e, the distance and time to reposition the vehicles\'. Towards that reduction, the Lane Covering Problem (LCP), which aims to cover at least cost routeslinks the various shippers\' demands of freight and tries to minimize operations without loads (repositioning), thus reducing the total cost of the entire network for involved shippers. This research proposes a model and an heuristic to solve, in large-scale through parallel programming, an expansion of the PCR.

A necessidade de redução de custos logísticos tem obrigado as empresas a colaborar entre si. O problema de logística colaborativa aqui enfocado é assim definido: identificar ciclos (ou seja, um percurso fechado) em um conjunto de rotas de carga de lotação (onde o caminhão coleta carga em um ponto e vai diretamente ao local de descarga, pois é completamente preenchido) de vários embarcadores de forma a minimizar o reposicionamento (isto é, viagens sem carga útil) de caminhões, dado que o subconjunto de rotas de um determinado embarcador pode conter rotas que complementam aquelas de outro. Desta maneira, vários embarcadores combinados podem oferecer aos transportadores um conjunto de ciclos com movimentação regular de veículos com carga completa e com mínimo reposicionamento. Esse problema pode ser modelado como um problema particular de cobertura de conjuntos com restrição de ciclos, o problema de cobertura de rotas com cardinalidade restrita (PCRCR), que é NP-Hard. Este estudo apresenta uma heurística alternativa que obtém resultados, em média, 1,74% melhores que a literatura existente, além de solucionar instâncias maiores. Ademais, o tempo de execução da heurística cresce de forma polinomial em função do tamanho do problema, ao contrário dos demais métodos aqui avaliados, que possuem comportamento exponencial. / Cost and sustainability imperatives are compelling reasons to make companies to collaborate with each other in order to operate more efficiently. The shipper collaboration problem can be defined as how to identify tours (i.e. a closed path) in a set of lanes from various shippers that minimize truck repositioning (deadheads), as the sub-set of routes from a single shipper may have lanes that complement the routes of another shipper. Thus, combined shippers may offer to carriers a set of tours with regularly executed truckload movements (where the truck loads at a point and go directly to the disposal location) with minimum asset repositioning. This problem can be modeled as a particular case of the set covering formulation with constrained cycles, the cardinality constrained lane covering problem (CCLCP), which is NP-hard. This work resents an alternative heuristic that obtains results about 1.74% better than the existing literature, and solves larger instances. Besides, the heuristics execution time presents polynomial growth, unlike other methods that have exponential behavior.

A crescente necessidade de buscar operações mais eficientes, com menor custo e mais sustentáveis tem feito com que empresas passassem a procurar oportunidades pelas quais estes objetivos pudessem ser atingidos. Na área de transportes encontrou-se na colaboração uma oportunidade para tal. Este trabalho trata o problema de cobertura rotas com cardinalidade restrita (PCRCR), onde empresas que realizam viagens de carga cheia se unem com o objetivo de reduzir o deslocamento vazio de veículos através da formação de ciclos. É chamado de problema de cardinalidade restrita uma vez que limitamos o número de máximo de viagens no ciclo, o que torna este problema NP-Hard. Existem na literatura duas heurísticas (construtivas) e um modelo por programação linear inteira para a solução deste problema. Este trabalho apresenta uma heurística baseada em um método de busca local que reduziu em média 3,19% os melhores resultados apresentados na literatura. Também são apresentados os tempos de execução de cada um dos algoritmos e a importância de escolher de uma boa solução inicial quando se deseja implantar uma Heurística com Busca Local. / The growing need to seek more efficient, lower cost and more sustainable operations has caused industries to seek opportunities in which these objectives could be achieved. In the area of transportation, collaboration is an opportunity for that. This work deals with the cardinality constrained lane covering problem (CCLCP), where companies who uses full truck loads join efforts in order to reduce empty vehicle travel through closed cycle formation. It is known as cardinality constraint problem as the maximum number of trips in the cycle is limited to an integer number, which makes this problem NP-Hard. There are two heuristics in the literature (constructive) and an integer linear programming model for solving this problem. This work presents a heuristic based on a local search method that reduced an average of 3.19% the better results in the literature. It also presents the execution times of each algorithm and the importance of choosing a good initial solution when you want to create a Local Search Heuristic.

Dans la collaboration entre transporteurs, plusieurs transporteurs forment une alliance pour échanger leurs demandes de transport dans le but d'améliorer la rentabilité. Dans cette thèse, nous avons étudié la planification collaborative de transport entre transporteurs de charges partielles. Plus concrètement, nous avons étudié trois sous-problèmes soulevés dans cette planification collaborative: le problème de ramassage et de livraison avec fenêtres de temps, profits et demandes réservées, le problème de détermination de gagnants dans l'échange combinatoire, et le problème de génération d'enchère.Ces trois sous-problèmes sont les problèmes clés pour la planification collaborative de transport parmi des transporteurs, et ils sont peu étudiés dans la littérature. Nous avons établi les nouveaux modèles de programmation mathématique pour ces problèmes et développé des heuristiques efficaces pour trouver des solutions très proches de leurs optimums dans un temps de calcul raisonnable. Les heuristiques proposées sont plus performantes que les solveurs commerciaux (GUROBI, CPLEX) non seulement en termes de la qualité de solution, mais aussi en termes du temps de calcul. / In carrier collaboration, multiple carriers form an alliance to exchange their delivery requests for the purpose of improving profitability. In this thesis, we have studied the collaborative transportation planning (CTP) among less-than-truckload (LTL) carriers. More concretely, we have studied three sub-problems raised in this collaborative planning: the pickup and delivery problem with time windows, profits, and reserved requests (PDPTWPR), the winner determination problem (WDP) in carrier collaboration via combinatorial exchange (CE), and the bid generation problem (BGP).These sub-problems are the key issues for collaborative transportation planning among carriers, and they are rarely studied in the literature. We have established new mathematical programming models for these problems and developed efficient heuristics to find solutions close to their optimums in a reasonable computational time. The heuristics proposed are more efficient than commercial solvers (GUROBI, CPLEX) not only in terms of solution quality, but also in terms of computation time.