Decision making under uncertainty is an important yet challenging task; a number of alternative paradigms which address this problem have been proposed. Stochastic Programming (SP) and Robust Optimization (RO) are two such modelling ap-proaches, which we consider; these are natural extensions of Mathematical Pro-gramming modelling. The process that goes from the conceptualization of an SP model to its solution and the use of the optimization results is complex in respect to its deterministic counterpart. Many factors contribute to this complexity: (i) the representation of the random behaviour of the model parameters, (ii) the interfac-ing of the decision model with the model of randomness, (iii) the difficulty in solving (very) large model instances, (iv) the requirements for result analysis and perfor-mance evaluation through simulation techniques. An overview of the software tools which support stochastic programming modelling is given, and a conceptual struc-ture and the architecture of such tools are presented. This conceptualization is pre-sented as various interacting modules, namely (i) scenario generators, (ii) model generators, (iii) solvers and (iv) performance evaluation. Reflecting this research, we have redesigned and extended an established modelling system to support modelling under uncertainty. The collective system which integrates these other-wise disparate set of model formulations within a common framework is innovative and makes the resulting system a powerful modelling tool. The introduction of sce-nario generation in the ex-ante decision model and the integration with simulation and evaluation for the purpose of ex-post analysis by the use of workflows is novel and makes a contribution to knowledge.

There is a potential as well as a growing interest amongst researchers to utilise simulation in optimising maintenance systems. The state of the art in simulation-based optimisation of maintenance was established by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. In general, approaches to optimise maintenance varied significantly in the literature. Overall, these studies highlight the need for a framework that unifies the approach to optimising maintenance systems. Framework requirements were established through two main sources of published research. Surveys on maintenance simulation optimisation were examined to document comments on the approaches authors follow while optimising maintenance systems. In addition, advanced and future maintenance strategies were documented to ensure it can be accommodated in the proposed framework. The proposed framework was developed using a standard flowchart tool due to its familiarity and ability to depict decision structures clearly. It provides a systematic methodology that details the steps required to connect the simulation model to an optimisation engine. Not only it provides guidance in terms of formulating the optimal problem for the maintenance system at hand but it also provides support and assistance in defining the optimisation scope and investigating applicable maintenance strategies. Additionally, it considers current issues relating to maintenance systems both in research and in practice such as uncertainty, complexity and multi-objective optimisation. The proposed framework cannot be applied using existing approaches for modelling maintenance. Existing modelling approaches using simulation have a number of limitations: The maintenance system is modelled separately from other inter-related systems such as production and spare parts logistics. In addition, these approaches are used to model one maintenance strategy only. A novel approach for modelling maintenance using Discrete Event Simulation is proposed. The proposed approach enables the modelling of interactions amongst various maintenance strategies and their effects on the assets in non-identical multi-unit systems. Using the proposed framework and modelling approach, simulation-based optimisation was conducted on an academic case and two industrial cases that are varied in terms of sector, size, number of manufacturing processes and level of maintenance documentation. Following the structured framework enabled discussing and selecting the suitable optimisation scope and applicable maintenance strategies as well as formulating a customised optimal problem for each case. The results of the study suggest that over-looking the optimisation of maintenance strategies may lead to sub-optimal solutions. In addition, this research provides insights for non-conflicting objectives in maintenance systems.

no / This chapter gives an overview of an integrated software suite, the Infobiotics Workbench, which is based on a novel spatial discrete-stochastic P systems modelling framework. The Workbench incorporates three important features, simu- lation, model checking and optimisation. Its capability for building, analysing and optimising large spatially discrete and stochastic models of multicellular systems makes it a useful, coherent and comprehensive in silico tool in systems and synthetic biology research. / EPSRC / The full text is unavailable due to publisher copyright restrictions on book chapters.

Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.

Les ports maritimes se confrontent à des exigences rigoureuses imposées par l'évolution de la taille de la flotte mondiale des porte-conteneurs et des zones de stockage qui arrivent à des niveaux de saturation élevés. Pour répondre à ces défis, plusieurs ports ont décidé de créer des terminaux multimodaux qui jouent le rôle de méga-hubs pour les terminaux maritimes, en vue de libérer les zones de stockage de ces terminaux, de développer la part du transport massifié de conteneurs et de réduire les émissions des gaz à effet de serre en utilisant des modes alternatifs à la route. Néanmoins, la gestion de ces nouveaux schémas logistiques est laborieuse. Cela s’explique par plusieurs facteurs, entre autres, la nature dynamique et distribuée de ces systèmes, la diversité des opérations et le manque des informations nécessaires au contrôle de flux. La finalité de cette thèse est de développer des approches capables de répondre aux besoins des opérateurs portuaires dans un terminal multimodal, avec prise en compte des différentes sources d’incertitudes. Deux problèmes d'optimisation sont principalement considérés dans cette thèse, à savoir : l'optimisation de tournées de navettes ferroviaires (The Rail Shuttle Routing Problem) et l'ordonnancement de grues de quai (The Quay Crane Scheduling Problem). En vue d'aborder la complexité et l’aspect incertain de ces problèmes, nous proposerons des modélisations mathématiques, ainsi que des approches de résolution basées sur l’optimisation par colonies de fourmis, l’optimisation robuste et le couplage Simulation-Optimisation. Les différents tests numériques effectués ont prouvé l’efficacité des algorithmes proposés et leur robustesse. / Today, seaports face increasingly stringent requirements imposed by the considerable growth of goods transited by sea. Indeed, the organization of the port sector has evolved rapidly and has caused several negative impacts, including pollution and congestion of terminals, which constitute today the major concerns of port operators. To address those challenges, several ports have decided to build multimodal terminals that act as mega-hubs for maritime terminals, in order to free the storage areas on the maritime terminals, to promote the use of consolidated container modes of transfer and to reduce greenhouse gas emissions by using alternative modes to the road. Nevertheless, the management of these new logistic systems is laborious. This is due to several factors, including the dynamic and distributed nature of these systems, the variety of operations, and the lack of information needed to control flow. The aim of this thesis is to develop approaches capable of meeting the needs of port operators in a multimodal terminal, taking into account the different sources of uncertainty. Two optimization problems are mainly considered in this thesis, namely : the Rail Shuttle Routing Problem(RSRP) and the Quay Crane Scheduling Problem(QCSP). To address the complexity and uncertainties of these problems, we propose new mathematical models, as well as some heuristics approaches based on ant colony optimization, robust optimization and Simulation-Optimization. The various numerical tests carried out proved the effectiveness and the robustness of the proposed algorithms.

Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.

Supply chain management has attracted much attention in the last decade. There has been a noticeable shift from a traditional individual organisation-based management to an integrated management across the supply chain network since the end of the last century. The shift contributes to better decision making in the supply chain context, as it is necessary for a company to cooperate with other supply chain members by utilising relevant information such as inventory, demand and resource capacity. In other words, information sharing and coordinated management are essential mechanisms to improve supply chain performance. Supply chains may differ significantly in terms of industry sectors, geographic locations, and firm sizes. This study was based on case studies from small and medium sized manufacturing supply chains in People Republic of China. The study was motivated by the following facts. Firstly, small and medium enterprises have made a big contribution to China’s economic growth. Several studies revealed that most of the Chinese manufacturing enterprises became aware of the importance of supply chain management, but compared to western firms, the supply chain management level of Chinese firms had been lagging behind. Research on supply chain management and performance optimisation in Chinese small and medium sized enterprises (SMEs) was very scarce. Secondly, there had been plenty of studies in the literature that focused on two or three level supply chains whilst considering a number of uncertain factors (e.g. customer demand) or a single supply chain performance indicator (e.g. cost). However, the research on multiple stage supply chain systems with multiple uncertainties and multiple objectives based on real industrial cases had been spared and deserved more attention. One reason was due to the lack of reliable industrial data that required an enormous effort to collect the primary data and there was a serious concern about data confidentiality from the industry aspect. This study employed two SME manufacturing companies as case studies. The first one was in the Aluminium industry and another was in the Chemical industry. The aim was to better understand the characteristics of the supply chains in Chinese SMEs through performing in-depth case studies, and built models and tools to evaluate different strategies for improving their supply chain performance. The main contributions of this study included the following aspects. Firstly, this study generalised a supply chain model including a domestic supply chain part and an international supply chain part based on deep case studies with the emphasis on identifying key characteristics in the case supply chains, such as uncertainties, constraints and cost elements in association with flows and activities in the domestic supply chain and the international supply chain. Secondly, two important SCM issues, i.e. the integrated raw material procurement and finished goods production planning, and the international sales planning, were identified. Thirdly, mathematical models were formulated to represent the supply chain model taking into account multiple uncertainties. Fourthly, several operational strategies utilising the concepts of just-in-time, safety-stock/capacity, Kanban, and vendor managed inventory, were evaluated and compared with the case company's original strategy in various scenarios through simulation methods, which enabled quantification of the impact of information sharing on supply chain performance. Fifthly, a single objective genetic algorithm was developed to optimise the integrated raw material ordering and finished goods production decisions under (s, S) policy (a dynamic inventory control policy), which enabled the impact of coordinated management on supply chain performance to be quantified. Finally, a multiple objectives genetic algorithm considering both total supply chain cost and customer service level was developed to optimise the integrated raw material ordering and finished goods production with the international sales plan decisions under (s, S) policy in various scenarios. This also enabled the quantification of the impact of coordinated management on supply chain performances.

Malgré la prédominance actuelle du mode routier, pour le transport de marchandises en milieu urbain, une alternative durable est nécessaire, au vu des enjeux environnementaux et sociétaux. Dans cette thèse, nous proposons l’étude d’une des perspectives possibles, pour absorber une partie de ce flux de marchandises toujours plus dense, en utilisant le réseau ferroviaire urbain, initialement dédié aux voyageurs. Une méthodologie intégrant le fret dans ce dernier est proposée, avec comme première étape, l'identification et la classification de tous les niveaux de mixité fret / voyageurs possibles. Le niveau le plus contraint est retenu, car sa faisabilité induira celle des autres. Notre seconde contribution est relative à une approche par décomposition du problème d’insertion du flux de fret en plusieurs sous-problèmes interdépendants, selon les trois horizons temporels (long, moyen et court). Dans le but d’évaluer la capacité du système global, à absorber un flux supplémentaire de nature différente, le problème de détermination du meilleur plan de transport des marchandises est identifié comme central et critique. La troisième contribution concerne la simulation du système de transport, puis sa formalisation par un PL en variables mixtes, pour affecter chaque commande à un train, en déterminant le moment auquel elle sera chargée et en minimisant les temps d’attente cumulés des commandes. Plusieurs variantes de colonies de fourmis sont développées, pour la résolution d’instances de grande taille. La quatrième contribution concerne le couplage du modèle de simulation, qui permet l’évaluation des performances de cette nouvelle solution de transport, avec les différents algorithmes optimisant le plan de transport. Enfin, nous proposons une approche de replanification par horizon glissant, pour absorber les perturbations de la demande, en minimisant les changements du plan de transport / Urban freight transport is almost exclusively carried out by truck. Beyond the drawbacks caused in the city, this transport mode is nearly saturated. This study discusses an alternative way of transporting freight by using urban rail infrastructure. The first contribution deals with the identification and classification of all different sharing possibilities of mixing freight with passenger’s traffic using rail network. The second contribution is the definition of global freight/passenger transport problem, which is decomposed into several optimization interdependent sub-problems with different temporal decision horizon. In order to show the capacity of the global system to absorb an additional flow with different nature, the Freight Rail Transport Schedule Problem “FRTSP” is identified as the bottleneck of transportation system and is formalized with MIP model. As third contribution, this problem determines train and loading time for each demand to be assigned respecting several constraints while minimizing total waiting time. The fourth contribution deals with a discrete event simulation approach, which studies this alternative and validates several proposed decision algorithms. Finally, the fifth contribution consists in a dynamic approach based on a rolling horizon, which is proposed in order to update the initial plan. The updated plan allows to determine a new assignment regarding new demand such as the modifications from the previous plan are minimized

Le concept de chaîne logistique numérique regroupe l'ensemble des modèles, méthodes et outils qui permettent de planifier les décisions sur des prototypes numériques de chaîne logistique. Dans ce travail de thèse, nous proposons deux contributions à la chaîne logistique numérique. Nos résultats se destinent en particulier aux réseaux de Petites et Moyennes Entreprises/Industries. D'une part, nous étudions deux nouveaux problèmes liés à la conception de réseaux logistiques en circuits courts et de proximité pour les produits agricoles frais. Pour chacun d'eux nous proposons une formulation en Programme Linéaire à Variables Mixtes. De plus des méthodes de résolution fondées sur des décompositions du modèle nous permettent de résoudre des instances de grande taille. Pour chaque problème, cette approche est mise en œuvre sur une étude de cas menée avec plusieurs collectivités territoriales. D'autre part, nous étudions le problème de planification tactique des activités de production, de transport et de stockage. Contrairement aux approches classiques centralisées, nous considérons que les décisions des différents acteurs sont prises de manière décentralisée. Nous étudions la manière de décomposer les décisions entre les acteurs ainsi que leurs comportements individuels. Nous analysons aussi des protocoles de concertation basés sur un échange limité d'informations. Afin de répondre à la double complexité du problème, nous proposons un outil innovant qui couple une simulation à base de multi-agents à des approches d'optimisation par programmation mathématique.

Le concept de chaîne logistique numérique regroupe l'ensemble des modèles, méthodes et outils qui permettent de planifier les décisions sur des prototypes numériques de chaîne logistique. Dans ce travail de thèse, nous proposons deux contributions à la chaîne logistique numérique. Nos résultats se destinent en particulier aux réseaux de Petites et Moyennes Entreprises/Industries. D'une part, nous étudions deux nouveaux problèmes liés à la conception de réseaux logistiques en circuits courts et de proximité pour les produits agricoles frais. Pour chacun d'eux nous proposons une formulation en Programme Linéaire à Variables Mixtes. De plus des méthodes de résolution fondées sur des décompositions du modèle nous permettent de résoudre des instances de grande taille. Pour chaque problème, cette approche est mise en œuvre sur une étude de cas menée avec plusieurs collectivités territoriales. D'autre part, nous étudions le problème de planification tactique des activités de production, de transport et de stockage. Contrairement aux approches classiques centralisées, nous considérons que les décisions des différents acteurs sont prises de manière décentralisée. Nous étudions la manière de décomposer les décisions entre les acteurs ainsi que leurs comportements individuels. Nous analysons aussi des protocoles de concertation basés sur un échange limité d'informations. Afin de répondre à la double complexité du problème, nous proposons un outil innovant qui couple une simulation à base de multi-agents à des approches d'optimisation par programmation mathématique. / The concept of digital supply chain gathers models, methods and tools to plan decisions on digital prototypes of supply chains. This doctoral dissertation proposes two contributions to digital supply chain. Mainly, our results address small and medium enterprises/industries. Firstly, we study two new problems related to service network design for short and local fresh food supply chains. For each of them we propose a Mixed Integer Linear Programming formulation. Decomposition-based methods are implemented in order to solve large scale instances. For each problem this approach is applied on a case study conducted with several local institutions. Secondly, we address the tactical supply chain planning problem: how to plan production, transportation and storage activities. As opposed to the classic centralized version, the decision making process is considered decentralized. We study how to decompose the decisions between actors as well as their individual behaviour. We also analyze negotiation processes based on limited information sharing. In order to address the double complexity of the problem, we propose an innovative tool coupling a multi-agent based simulation approach with optimization approaches based on mathematical programming.