A Methodology for Data-Driven Decision-Making in Last Mile Delivery Operations

Across all industries, from manufacturing to services, decision-makers must deal day to day with the outcomes from past and current decisions that affect their business. Last-mile delivery is the term used in supply chain management to describe the movement of goods from a hub to final destinations. This research proposes a methodology that supports decision making for the execution of last-mile delivery operations in a supply chain. This methodology offers diverse, hybrid, and complementary techniques (e.g., optimization, simulation, machine learning, and geographic information systems) to understand last-mile delivery operations through data-driven decision-making. The hybrid modeling might create better warning systems and support the delivery stage in a supply chain. The methodology proposes self-learning procedures to iteratively test and adjust the gaps between the expected and real performance. This methodology supports the process of making effective decisions promptly, optimization, simulation, and machine learning models are used to support execution processes and adjust plans according to changes in conditions, circumstances, and critical factors. This research is applied in two case studies. The first one is in maritime logistics, which discusses the decision process to find the type of vessels and routes to deliver petroleum from ships to villages. The second is in city logistics, where a network of stakeholders during the city distribution process is analyzed, showing the potential benefits of this methodology, especially in metropolitan areas. Potential applications of this system will leverage growing technological trends (e.g., machine learning in supply chain management and logistics, internet of things). The main research impact is the design and implementation of a methodology, which can support real-time decisions and adjust last-mile operations depending on the circumstances. The methodology allows taking decisions under conditions of stakeholder behavior patterns like vehicle drivers, customers, locations, and traffic. As the main benefit is the possibility to predict future scenarios and plan strategies for the most likely situations in last-mile delivery. This will help determine and support the accurate calculation of performance indicators. The research brings a unified methodology, where different solution approaches can be used in a synchronized form, which allows researches and other interested people to see the connection between techniques. With this research, it was possible to bring advanced technologies in routing practices and algorithms to decrease operating cost and leverage the use of offline and online information, thanks to connected sensors to support decisions

Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method

The logistics sector plays one of the most important roles in the supply chain with the aim of providing a fast, flexible, safe, economical, efficient, and environmentally acceptable performance of freight transport flows. In addition, the popularization of the concept of a circular economy (CE) used to retain goods, components, and materials at their highest usability and value at all times, illustrates the importance of the adequate performance of reverse logistics (RL) processes. However, traditional RL is unable to cope with the requirements of modern supply chains and requires the application of Industry 4.0 technologies, which would make it more efficient. The main aim of this study was to evaluate the applicability of various Industry 4.0 technologies in the RL sector in order to point out the most applicable ones. To solve the defined problem, a novel multi-criteria decision making (MCDM) model was defined by combining the best-worst method (BWM) to obtain the criteria weights, and the newly developed comprehensive distance-based ranking (COBRA) method to rank the technologies. Another aim of the study was to validate the newly established method. The results indicated that the most applicable technologies were the Internet of Things, cloud computing, and electronic-mobile marketplaces. These technologies will have a significant impact on the development of RL and the establishment of CE systems, thus bringing about all the related positive effects

Optimization problems in the postal sector

Supply chain optimization is a widely studied field of operations research. Nevertheless, adapting the existing solutions to the specifications of each company is an interesting and stimulating challenge. With this in mind, the project described herein, developed in partnership with CTT, looks to provide the company with precious tools to more efficiently manage the labour allocated to mail delivery and increase the productivity of the workforce as a whole. To achieve these objectives, it follows up on a previous work by Pereira[26], where an extension of the Vehicle Routing Problem (VRP) was proposed to optimize the last-mile delivery step of the mail distribution procedure, but this time giving particular relevance to the adequacy of the model developed to the intricacies imposed by the company and exploring suitable adaptations. One of the requirements, for standardization purposes, is the creation of segments, composed of sets of postal codes that serve as input to the optimization model. Finally, it was necessary to merge this work with the company’s workflow by integrating the model with SISMA, a productivity assessment tool already used by CTT.A otimização de uma cadeia de abastecimento é um campo vastamente estudado no âm- bito da investigação operacional. Contudo, adaptar as soluções existentes aos critérios de cada empresa é um desafio bastante interessante e estimulante. Tendo isto em consi- deração, este projeto, desenvolvido em parceria com os CTT – Correios de Portugal, S.A. (CTT), procura fornecer à empresa ferramentas que permitam uma gestão eficiente da força de trabalho afeta à distribuição de correio. Para atingir este propósito, este trabalho teve como ponto de partida uma proposta de- senvolvida por Pereira[26], onde uma adaptação do Vehicle Routing Problem (VRP) foi desenvolvida para otimizar a etapa last-mile do processo de distribuição. No presente trabalho, dá-se uma atenção redobrada à compatibilidade do modelo desenvolvido com as complexidades impostas pela empresa e explora-se algumas melhorias consideradas apropriadas. Um dos requisitos, para manter alguma estibilidade nos resultados, é a introdução de segmentos, compostos por conjuntos contíguos de códigos postais, que ali- mentam o modelo. Finalmente, para combinar este trabalho com o fluxo de trabalho da empresa, fez-se a integração do modelo de otimização com o SISMA, uma ferramenta de avalição de produtividade já utilizada pelos CTT

Essays in Measuring, Controlling, and Coordinating Supply Chain Inventory and Transportation Operations

Supply chain collaboration programs, such as continuous replenishment program (CRP), is among the most popular supply chain management practices. CRP is an arrangement between two partners in a supply chain to share information on a regular basis for lowering logistics costs while maintaining or increasing service levels. CRP shifts the replenishment responsibility to the upstream partner to avoid the bullwhip effect across the supply chain. This dissertation aims to quantify, measure, and expand the benefits of CRP for the purpose of reducing logistics cost and improving customer service. The developed models in this dissertation are all applied in different case studies supported by a group of major healthcare partners. The first research contribution, discussed in chapter 2, is a comprehensive data-driven cost approximation model that quantifies the benefits of CRP for both partners under three cost components of inventory holding, transportation and ordering processing without imposing assumptions that normally do not hold in practice. The second contribution, discussed in chapter 3, is development of a verifiable efficiency measurement system to ensure the benefits of CRP for all partners. Multi-functional efficiency metrics are designed to capture the trade-off in gaining efficiency between multiple functions of logistics (i.e. inventory efficiency, transportation efficiency, and order processing efficiency). In addition, a statistical process control (SPC) system is developed to monitor the metrics over time. We discuss suitable SPC systems for various time series behaviors of the metrics. The third contribution of the dissertation, discussed in chapter 4, is development of a multi-objective decision analysis (MODA) model for multi-stop truckload (MSTL) planning. MSTL is becoming increasing popular among shippers while is experiencing significant resistance from carriers. MSTL is capable of reducing the shipping cost of shippers substantially but it can also disrupt carriers’ operations. A MODA model is developed for this problem to incorporate the key decision criteria of both sides for identifying the most desirable multi-stop routes from the perspective both decision makers