Analysing the complexity of the model-based decision making processes within the industrial management context

PURPOSE The decision-making process plays a key role in organizations. Every decision-making process produces a final choice that may or may not prompt action. Recurrently, decision makers find themselves in the dichotomous question of following a traditional sequence decision-making process where the output of a decision is used as the input of the next stage of the decision, or following a joint decision-making approach where several decisions are taken simultaneously. The implication of the decision-making process will impact different players of the organization. The choice of the decision- making approach becomes difficult to find, even with the current literature and practitioners’ knowledge. The pursuit of better ways for making decisions has been a common goal for academics and practitioners. Management scientists use different techniques and approaches to improve different types of decisions. The purpose of this decision is to use the available resources as well as possible (data and techniques) to achieve the objectives of the organization. The developing and applying of models and concepts may be helpful to solve managerial problems faced every day in different companies. As a result of this research different decision models are presented to contribute to the body of knowledge of management science. The first models are focused on the manufacturing industry and the second part of the models on the health care industry. Despite these models being case specific, they serve the purpose of exemplifying that different approaches to the problems and could provide interesting results. Unfortunately, there is no universal recipe that could be applied to all the problems. Furthermore, the same model could deliver good results with certain data and bad results for other data. A framework to analyse the data before selecting the model to be used is presented and tested in the models developed to exemplify the ideas. METHODOLOGY As the first step of the research a systematic literature review on the joint decision is presented, as are the different opinions and suggestions of different scholars. For the next stage of the thesis, the decision-making process of more than 50 companies was analysed in companies from different sectors in the production planning area at the Job Shop level. The data was obtained using surveys and face-to-face interviews. The following part of the research into the decision-making process was held in two application fields that are highly relevant for our society; manufacturing and health care. The first step was to study the interactions and develop a mathematical model for the replenishment of the car assembly where the problem of “Vehicle routing problem and Inventory” were combined. The next step was to add the scheduling or car production (car sequencing) decision and use some metaheuristics such as ant colony and genetic algorithms to measure if the behaviour is kept up with different case size problems. A similar approach is presented in a production of semiconductors and aviation parts, where a hoist has to change from one station to another to deal with the work, and a jobs schedule has to be done. However, for this problem simulation was used for experimentation. In parallel, the scheduling of operating rooms was studied. Surgeries were allocated to surgeons and the scheduling of operating rooms was analysed. The first part of the research was done in a Teaching hospital, and for the second part the interaction of uncertainty was added. Once the previous problem had been analysed a general framework to characterize the instance was built. In the final chapter a general conclusion is presented. FINDINGS AND PRACTICAL IMPLICATIONS The first part of the contributions is an update of the decision-making literature review. Also an analysis of the possible savings resulting from a change in the decision process is made. Then, the results of the survey, which present a lack of consistency between what the managers believe and the reality of the integration of their decisions. In the next stage of the thesis, a contribution to the body of knowledge of the operation research, with the joint solution of the replenishment, sequencing and inventory problem in the assembly line is made, together with a parallel work with the operating rooms scheduling where different solutions approaches are presented. In addition to the contribution of the solving methods, with the use of different techniques, the main contribution is the framework that is proposed to pre-evaluate the problem before thinking of the techniques to solve it. However, there is no straightforward answer as to whether it is better to have joint or sequential solutions. Following the proposed framework with the evaluation of factors such as the flexibility of the answer, the number of actors, and the tightness of the data, give us important hints as to the most suitable direction to take to tackle the problem. RESEARCH LIMITATIONS AND AVENUES FOR FUTURE RESEARCH In the first part of the work it was really complicated to calculate the possible savings of different projects, since in many papers these quantities are not reported or the impact is based on non-quantifiable benefits. The other issue is the confidentiality of many projects where the data cannot be presented. For the car assembly line problem more computational power would allow us to solve bigger instances. For the operation research problem there was a lack of historical data to perform a parallel analysis in the teaching hospital. In order to keep testing the decision framework it is necessary to keep applying more case studies in order to generalize the results and make them more evident and less ambiguous. The health care field offers great opportunities since despite the recent awareness of the need to improve the decision-making process there are many opportunities to improve. Another big difference with the automotive industry is that the last improvements are not spread among all the actors. Therefore, in the future this research will focus more on the collaboration between academia and the health care sector.

Optimização de rotas na recolha de leite

O presente relatório tem por objectivo apresentar e descrever de forma detalhada o projecto “Optimização de rotas na recolha de leite”. Este projecto foi conduzido no âmbito do estágio curricular, realizado na parte não-lectiva do Mestrado em Sistemas de Informação de Gestão, do Instituto Superior de Contabilidade e Administração de Coimbra, tendo decorrido na empresa Lacticoop entre 03 de Outubro de 2011 e 27 de Julho de 2012. O projecto surge da necessidade da empresa optimizar as suas rotas de recolha de leite. Essa optimização pode ser subdividida em duas partes distintas: i) a recolha de dados, características e processos relativos à empresa e aos sistemas com que labora; ii) e o desenvolvimento do software necessário para a optimização de uma componente desses sistemas, associada ao processo de recolha de leite. O primeiro ponto envolve a recolha de informação sobre a política interna da empresa, a recolha de dados acerca dos veículos, rotas, consumos e condutores, assim como o levantamento de procedimentos e tecnologias utilizadas. O segundo ponto envolve a modelação do problema em estudo, o levantamento das necessidades de software para implementar o sistema de informação, a avaliação de soluções de software e desenvolvimento/adaptação da aplicação informática, assim como a implementação do software desenvolvido / adaptado e testes. Actualmente a empresa não dispõe de qualquer automatismo para a definição de rotas, sendo o processo de escalonamento de rotas feito manualmente. Este processo é bastante moroso e complexo, envolvendo a troca de informações entre o gestor e os condutores das viaturas. É um processo gradual, numa sequência de detecção de erros e correcção desses mesmos erros. Esta metodologia conduz a soluções bastante ineficientes, desde logo pela desactualização das soluções relativamente à efemeridade dos dados, especialmente ao nível de quantidades de recolha do produto. A razão da escolha de um sistema informático que permita optimizar as rotas prende-se essencialmente com a rapidez na obtenção de soluções e na capacidade de integração de dados actualizados. Este processo recorre a técnicas e modelos de optimização que envolvem o problema de Rotas de Veículos (Vehicle Routing Problem), sendo, em geral, um problema de difícil resolução em função do número de clientes envolvidos. Todavia, trata-se de um sistema que traz enormes benefícios no apoio ao processo de decisão por parte do gestor. Neste estágio pretendeu-se, como objectivo principal, desenvolver uma aplicação que permita optimizar as rotas dos veículos envolvidos no processo de recolha de leite. Os benefícios do sistema na diminuição de distâncias percorridas pelas viaturas de recolha e no aumento da eficiência do sistema de transportes, serão evidenciados no trabalho desenvolvido. A aplicação foi criada no software Eclipse (utilizando a linguagem Java). Na primeira fase do projecto estava previsto monitorizar as rotas e consumos dos veículos através da tecnologia de geo-posicionamento por satélite (GPS), de forma a atribuir comissões sobre a poupança de combustível aos condutores dos veículos. Não foi possível concluir esta fase devido à inexistência dessa tecnologia nas viaturas e pelo facto de a empresa ter retirado essa prioridade a esse investimento.

Premium e-grocery:exploring value in logistics integrated service solutions

E-grocery is gradually becoming viable or a necessity for many families. Yet, most e-supermarkets are seen as providers of low value "staple" and bulky goods mainly. While each store has a large number of SKU available, these products are mainly necessity goods with low marginal value for hedonistic consumption. A need to acquire diverse products (e.g., organic), premium priced products (e.g., wine) for special occasions (e.g., anniversary, birthday), or products just for health related reasons (e.g., allergies, diabetes) are yet to be served via one-stop e-tailers. In this paper, we design a mathematical model that takes into account consumers' geo-demographics and multi-product sourcing capacity for creating critical mass and profit. Our mathematical model is a variant of Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which we extend by adding intermediate locations for trucks to meet and exchange goods. We illustrate our model for the city of Istanbul using GIS maps, and discuss its various extensions as well as managerial implications.

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

Resource management and optimization in wireless mesh networks

A wireless mesh network is a mesh network implemented over a wireless network system such as wireless LANs. Wireless Mesh Networks(WMNs) are promising for numerous applications such as broadband home networking, enterprise networking, transportation systems, health and medical systems, security surveillance systems, etc. Therefore, it has received considerable attention from both industrial and academic researchers. This dissertation explores schemes for resource management and optimization in WMNs by means of network routing and network coding.^ In this dissertation, we propose three optimization schemes. (1) First, a triple-tier optimization scheme is proposed for load balancing objective. The first tier mechanism achieves long-term routing optimization, and the second tier mechanism, using the optimization results obtained from the first tier mechanism, performs the short-term adaptation to deal with the impact of dynamic channel conditions. A greedy sub-channel allocation algorithm is developed as the third tier optimization scheme to further reduce the congestion level in the network. We conduct thorough theoretical analysis to show the correctness of our design and give the properties of our scheme. (2) Then, a Relay-Aided Network Coding scheme called RANC is proposed to improve the performance gain of network coding by exploiting the physical layer multi-rate capability in WMNs. We conduct rigorous analysis to find the design principles and study the tradeoff in the performance gain of RANC. Based on the analytical results, we provide a practical solution by decomposing the original design problem into two sub-problems, flow partition problem and scheduling problem. (3) Lastly, a joint optimization scheme of the routing in the network layer and network coding-aware scheduling in the MAC layer is introduced. We formulate the network optimization problem and exploit the structure of the problem via dual decomposition. We find that the original problem is composed of two problems, routing problem in the network layer and scheduling problem in the MAC layer. These two sub-problems are coupled through the link capacities. We solve the routing problem by two different adaptive routing algorithms. We then provide a distributed coding-aware scheduling algorithm. According to corresponding experiment results, the proposed schemes can significantly improve network performance.^

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Problème de déploiement de ressources dans le cas des feux de forêts majeurs - Opérations aériennes

Chaque année le feu brûle quelques dizaines de milliers d’hectares de forêts québécoises. Le coût annuel de prévention et de lutte contre les feux de forêts au Québec est de l’ordre de plusieurs dizaines de millions de dollars. Le présent travail contribue à la réduction de ces coûts à travers l’automatisation du processus de planification des opérations de suppression des feux de forêts majeurs. Pour ce faire, un modèle mathématique linéaire en nombres entiers a été élaboré, résolu et testé; introduisant un nouveau cas particulier à la littérature des Problèmes de Tournées de Véhicules (VRP). Ce modèle mathématique concerne le déploiement aérien des ressources disponibles pour l’extinction des incendies. Le modèle élaboré a été testé avec CPLEX sur des cas tirés de données réelles. Il a permis de réduire le temps de planification des opérations d’extinction des feux de forêts majeurs de 75% dans les situations courantes.

Otimização da recolha de resíduos urbanos : caso de estudo de Aveiro

O elevado custo da operação de recolha de resíduos urbanos e a necessidade de cumprir metas dispostas em instrumentos legais são duas motivações que conduzem à necessidade de otimizar o serviço da recolha de resíduos. A otimização da recolha de resíduos é um problema de elevada complexidade de resolução que envolve a análise de redes de transporte. O presente trabalho propõe soluções de otimização da recolha de resíduos urbanos indiferenciados, a partir de um caso de estudo: o percurso RSU I 06 do município de Aveiro. Para este efeito, recorreu-se a uma aplicação informática de representação e análise geográfica: software ArcGIS denominada ArcMap e sua extensão Network Analyst, desenvolvida para calcular circuitos otimizados entre pontos de interesse. O trabalho realizado de aplicação do Network Analyst inclui a apresentação de duas das suas funcionalidades (Route e Vehicle Routing Problem). Em relação ao atual circuito de recolha e com base nos ensaios efetuados, foi possível concluir que esta aplicação permite obter circuitos de recolha otimizados mais curtos ou com menor duração. Contudo, ao nível da gestão permitiu concluir que, com a atual capacidade de contentorização, seria viável reduzir a frequência de recolha de seis vezes por semana para metade, dividindo a área de recolha em duas áreas, de acordo com as necessidades de cada local, reduzindo ainda mais o esforço de recolha. A aplicação do Network Analyst ao caso de estudo, permitiu concluir que é um software com muito interesse no processo de gestão da recolha de resíduos urbanos, apesar de apresentar algumas restrições de aplicação e que a qualidade/eficácia do procedimento de otimização depende da qualidade dos dados de entrada, em particular do descritivo geográfico disponível para os arruamentos e, em larga medida, também depende do modelo de gestão considerado.

O problema de alocação de berços: um estudo das heurísticas simulated annealing e algoritmo genético

Este trabalho apresenta um estudo de caso das heurísticas Simulated Annealing e Algoritmo Genético para um problema de grande relevância encontrado no sistema portuário, o Problema de Alocação em Berços. Esse problema aborda a programação e a alocação de navios às áreas de atracação ao longo de um cais. A modelagem utilizada nesta pesquisa é apresentada por Mauri (2008) [28] que trata do problema como uma Problema de Roteamento de Veículos com Múltiplas Garagens e sem Janelas de Tempo. Foi desenvolvido um ambiente apropriado para testes de simulação, onde o cenário de análise foi constituido a partir de situações reais encontradas na programação de navios de um terminal de contêineres. Os testes computacionais realizados mostram a performance das heurísticas em relação a função objetivo e o tempo computacional, a m de avaliar qual das técnicas apresenta melhores resultados.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

Optimizing last mile delivery using public transport with multiagent based control

The majority of research work carried out in the field of Operations-Research uses methods and algorithms to optimize the pick-up and delivery problem. Most studies aim to solve the vehicle routing problem, to accommodate optimum delivery orders, vehicles etc. This paper focuses on green logistics approach, where existing Public Transport infrastructure capability of a city is used for the delivery of small and medium sized packaged goods thus, helping improve the situation of urban congestion and greenhouse gas emissions reduction. It carried out a study to investigate the feasibility of the proposed multi-agent based simulation model, for efficiency of cost, time and energy consumption. Multimodal Dijkstra Shortest Path algorithm and Nested Monte Carlo Search have been employed for a two-phase algorithmic approach used for generation of time based cost matrix. The quality of the tour is dependent on the efficiency of the search algorithm implemented for plan generation and route planning. The results reveal a definite advantage of using Public Transportation over existing delivery approaches in terms of energy efficiency.

Architectural-Physical Co-Design of 3D CPUs with Micro-Fluidic Cooling

The performance, energy efficiency and cost improvements due to traditional technology scaling have begun to slow down and present diminishing returns. Underlying reasons for this trend include fundamental physical limits of transistor scaling, the growing significance of quantum effects as transistors shrink, and a growing mismatch between transistors and interconnects regarding size, speed and power. Continued Moore's Law scaling will not come from technology scaling alone, and must involve improvements to design tools and development of new disruptive technologies such as 3D integration. 3D integration presents potential improvements to interconnect power and delay by translating the routing problem into a third dimension, and facilitates transistor density scaling independent of technology node. Furthermore, 3D IC technology opens up a new architectural design space of heterogeneously-integrated high-bandwidth CPUs. Vertical integration promises to provide the CPU architectures of the future by integrating high performance processors with on-chip high-bandwidth memory systems and highly connected network-on-chip structures. Such techniques can overcome the well-known CPU performance bottlenecks referred to as memory and communication wall. However the promising improvements to performance and energy efficiency offered by 3D CPUs does not come without cost, both in the financial investments to develop the technology, and the increased complexity of design. Two main limitations to 3D IC technology have been heat removal and TSV reliability. Transistor stacking creates increases in power density, current density and thermal resistance in air cooled packages. Furthermore the technology introduces vertical through silicon vias (TSVs) that create new points of failure in the chip and require development of new BEOL technologies. Although these issues can be controlled to some extent using thermal-reliability aware physical and architectural 3D design techniques, high performance embedded cooling schemes, such as micro-fluidic (MF) cooling, are fundamentally necessary to unlock the true potential of 3D ICs. A new paradigm is being put forth which integrates the computational, electrical, physical, thermal and reliability views of a system. The unification of these diverse aspects of integrated circuits is called Co-Design. Independent design and optimization of each aspect leads to sub-optimal designs due to a lack of understanding of cross-domain interactions and their impacts on the feasibility region of the architectural design space. Co-Design enables optimization across layers with a multi-domain view and thus unlocks new high-performance and energy efficient configurations. Although the co-design paradigm is becoming increasingly necessary in all fields of IC design, it is even more critical in 3D ICs where, as we show, the inter-layer coupling and higher degree of connectivity between components exacerbates the interdependence between architectural parameters, physical design parameters and the multitude of metrics of interest to the designer (i.e. power, performance, temperature and reliability). In this dissertation we present a framework for multi-domain co-simulation and co-optimization of 3D CPU architectures with both air and MF cooling solutions. Finally we propose an approach for design space exploration and modeling within the new Co-Design paradigm, and discuss the possible avenues for improvement of this work in the future.

Integration of Social Concerns in Collaborative Logistics and Transportation Networks

Part 21: Mobility and Logistics

Improving robustness of rolling stock circulations in rapid transit networks

The rolling stock circulation depends on two different problems: the rolling stock assignment and the train routing problems, which up to now have been solved sequentially. We propose a new approach to obtain better and more robust circulations of the rolling stock train units, solving the rolling stock assignment while accounting for the train routing problem. Here robustness means that difficult shunting operations are selectively penalized and propagated delays together with the need for human resources are minimized. This new integrated approach provides a huge model. Then, we solve the integrated model using Benders decomposition, where the main decision is the rolling stock assignment and the train routing is in the second level. For computational reasons we propose a heuristic based on Benders decomposition. Computational experiments show how the current solution operated by RENFE (the main Spanish train operator) can be improved: more robust and efficient solutions are obtained