Modelling and Performance Evaluation of Containerised Parcel Delivery

This paper investigates different factors that affect the performance of containerised transportation in parcel delivery networks. Motivated by a situation facing a postal delivery company in Australia, we study how container utilisation rate, sortation activities, and changes in cost parameters can affect the overall performance of a parcel delivery network. Mixed-integer programming and machine learning are employed to model a realistic parcel delivery network considering sortation activities and to evaluate the performance of this network using data from a major postal service provider. The findings of this study can help parcel delivery companies to make more informed investment decisions and introduce more effective performance improvement initiatives

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design

We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scenario bundles, we first calculate a vector of probabilities for every scenario, which measures the association strength of a scenario to each bundle center. This membership score calculation is based on existing soft clustering algorithms such as Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM). After obtaining the probabilistic membership scores, we propose a strategy to determine the scenario-to-bundle assignment. By contrast, almost all existing scenario bundling methods such as K-Means (KM) assume before the scenario-to-bundle assignment that a scenario belongs to exactly one bundle, which is equivalent to requiring that the membership scores are Boolean values. The probabilistic membership scores bring many advantages over Boolean ones, such as the flexibility to create various degrees of overlap between scenario bundles and the capability to accommodate scenario bundles with different covariance structures. We empirically study the impacts of different degrees of overlap and covariance structures on PHH performance by comparing PHH based on FCM/GMM with that based on KM and the cover method, which represents the state-of-the-art scenario bundling algorithm for stochastic network design. The solution quality is measured against the lower bound provided by CPLEX. The experimental results show that, GMM-based PHH yields the best performance among all methods considered, achieving nearly equivalent solution quality in a fraction of the run-time of the other methods

COMPACT FORMULATION OF MULTICOMMODITY NETWORK FLOWS WITH APPLICATIONS TO THE BACKHAUL PROFIT MAXIMIZATION PROBLEM AND FIXED CHARGE NETWORK FLOW PROBLEM

The triples formulation is a compact formulation of multicommodity network flow problems that provides a different representation of flow than the traditional and widely used node-arc and arc-path approaches. In the literature, the triples formulation has been applied successfully to the maximum concurrent flow problem and to a network optimization problem with piecewise linear convex costs. This dissertation applies the triples formulation to the backhaul profit maximization problem (BPMP) and the fixed charge network flow problem (FCNF). It is shown that the triples representation of multicommodity flow significantly reduces the number of variables and constraints in the mixed integer programming formulations of the BPMP and FCNF. For the BPMP, this results in significantly faster solution times. For dense problem instances, the triples-based formulation of FCNF is found to produce better solutions than the node-arc formulation early in the branch-and-bound process. This observation leads to an effective hybrid method which combines the respective advantages of the smaller size of the triples formulation and the stronger linear programming relaxation of the node-arc formulation. In addition to empirical studies, the dissertation presents new theoretical results supporting the equivalence of the triples formulation to the node-arc and arc-path formulations. The dissertation also proposes a multi-criteria Composite Index Method (CIM) to compare the performance of alternative integer programming formulations of an optimization problem. Using the CIM, the decision maker assigns weights to problem instance sizes and multiple performance measures based on their relative importance for the given application. The weighting scheme is used to produce a single number that measures the relative improvement of one alternative over the other and provides a method to select the most effective approach when neither one dominates the other when tested on different sizes of problem instances. The dissertation demonstrates a successful application of the CIM to evaluate a series of eleven techniques for improving the node-arc and triples formulations of the BPMP previously proposed in the literature

Consolidação de carga no transporte rodoviário : uma proposta on-line

Orientador: Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 27/05/2021Inclui referências: p. 85-89Resumo: A consolidação de cargas é a ação de agrupar carregamentos em um determinado ponto, para uma ou mais entregas, sendo a origem das cargas em um ou mais fornecedores e o destino em um ou mais clientes. Objetiva-se com a consolidação obter resultados que signifiquem maior aproveitamento da capacidade de carregamento dos veículos, em relação a peso e volume. No geral, embora tenhase a percepção que a consolidação de carga aumenta o custo devido a: aumento do manuseio da carga para trocar de veículo; necessidade de se ter um local adequado para essa operação; e possível aumento do tempo total de viagem de uma certa carga, o custo total terá uma grande probabilidade de redução. Esta redução pode ocorrer basicamente por dois fatores complementares entre si: 1) melhor utilização da capacidade dos veículos e 2) melhor aproveitamento de utilização da frota. Estimase que os custos de movimentação do veículo, como os custos com combustíveis, por exemplo, representam os maiores percentuais do custo logístico, assim, ao carregar melhor cada veículo, a utilização da frota tenderá a ser minimizada. Trabalhos relacionados à consolidação de cargas no modal rodoviário são abordados desde a década de 1970, pois considera-se uma estratégia diferencial para transportadoras. Este trabalho trata a consolidação de carga no transporte rodoviário do Brasil. Apresenta-se as diferentes abordagens utilizadas na literatura, evidenciando as técnicas de solução, as metodologias e seus resultados. A metodologia proposta é baseada no desenvolvimento de uma matheurística para obtenção da solução do problema de consolidação de carga no transporte rodoviário. Esta estratégia utiliza inicialmente uma heurística para criação de caminhos entre pontos de origem e destino em um grafo. Esses caminhos são parâmetros do modelo exato de Programação Linear Inteira Mista (PLIM), para diminuir a quantidade de combinações de deslocamento entre uma origem e um destino, em relação ao modelo utilizando o grafo completo. Para fins de comparação, com intenção inovadora e direcionada aos conceitos da Indústria 4.0, todo o framework de solução foi desenvolvido na plataforma de programação em nuvem OutSystems. O modelo exato foi resolvido pelo solver Gurobi, por meio de uma API para integração com o OutSystems. A programação do modelo exato foi desenvolvida em linguagem C#, com gerenciamento na nuvem. Os resultados indicaram pouca variação no valor da função objetivo para replicações dos cenários de teste. Foi possível solucionar uma instância com 40 demandas e 10 terminais para consolidação em até 38s, para um dos cenários resolvidos. Demonstrou-se pequeno impacto, em acréscimo de tempo, para resolver este problema de maneira on-line. Palavras-chave: Consolidação de Carga. Gestão da Cadeia de Suprimentos 4.0. Programação em Nuvem. Programação Linear Inteira Mista. Matheurística.Abstract: Cargo consolidation consists in grouping shipments at a given point, for one or more deliveries, with the origin of the cargo at one or more suppliers and the destination at one or more customers. The consolidation aims to obtain results that mean greater use of the truck's loading capacity, in terms of weight and volume. In general, although there is a perception that cargo consolidation increases the cost due to: increased cargo handling to change vehicles; the need to have an adequate location for this operation; and possible increase in the total travel time of a certain cargo, the total cost has a great probability of reduction. This reduction can occur basically due to two complementary factors: 1) better utilization of vehicle capacity and 2) better utilization of the fleet. It is estimated that vehicle handling costs, such as fuel costs, for example, represent the highest percentages of the logistical cost, thus, when loading each vehicle better, the use of the fleet will tend to be minimized. Work related to cargo consolidation in the road modal has been approached since the 1970s, as it is considered a differential technique for carriers. This work deals with cargo consolidation in Brazilian road transport. The different approaches used in the literature are presented, showing the solution techniques, the methodologies, and their respective results. The proposed methodology is based on the development of a matheuristic to obtain a solution to the problem of cargo consolidation in road transport. This strategy initially uses a heuristic to create several paths between points of origin and destination in a graph. These paths are parameters of the exact Mixed Integer Linear Programming (PLIM) model, to decrease the number of combinations between a source and a destination, in relation to using the complete graph. With an innovative intention and directed to the concepts of Industry 4.0, the entire solution framework was developed on the OutSystems cloud programming platform. The exact model was solved by the Gurobi solver, through an API for integration with OutSystems. The programming of the exact model was developed in C # language, with management in the cloud. The results indicated little variation in the value of the objective function for replicating the test scenarios. It was possible to solve an instance with 40 demands and 10 terminals for consolidation in up to 38s, for one of the solved scenarios. Little impact has been demonstrated, in addition to time, to solve this problem online. Keywords: Cargo Consolidation. Supply Chain Management 4.0. Cloud Programming. Mixed Integer Linear Programming. Matheuristics