Utility network optimization in eco-industrial parks by a multi-leader follower game methodology

A multi-leader-follower game (MLFG) model for the design of the utility network in an eco-industrial park (EIP) is studied and implemented by introducing the concept of an environmental authority. The methodology also considers the flowsheet simulation of each enterprise involved in the EIP in order to obtain utility consumption of each enterprise operating by itself. The approach is validated on a case study of a potential Norwegian EIP. In the latter, multi-leader-single-follower and single-leader-multi-follower game models are studied. Each enterprise's objective is to minimize the total annualized cost, while the EIP authority objective is to minimize the equivalent CO2 consumption related to utility consumption within the ecopark. The MLFG is transformed into a MOPEC and solved using GAMS® as an NLP. The methodology proposed is proven to be reliable in multi-criteria scenarios compared to traditional multiobjective optimization approaches, providing numerical Nash/Stackelberg equilibrium solutions and specifically in EIP planning and optimization

Bilevel optimization of Eco-Industrial parks for the design of sustainable resource networks

This work presents a bilevel programming framework for the design of sustainable resource networks in eco-industrial parks (EIP). First, multiobjective optimization methods are explored in order to manage the multi-criteria nature of EIP network design problems. Then, different case studies are modeled in order to minimize and maintain in equilibrium participating plants operating costs while minimizing resource consumption. Thus, the structure of the model is constituted by a bilevel programming framework where the enterprises’ plants play a Nash game between them while being in a Stackelberg game structure with the authority. This structure defines a model which, in order to be solved, has to be transformed into a MOPEC (Multiple Optimization Problems with Equilibrium Constraints) structure. Regarding the case studies, monocontaminant water networks in EIP are studied first, where the influence of plants operating parameters are studied in order to determine the most important ones to favor the symbiosis between plants. The water network is composed of a fixed number of process and water regeneration units where the maximal inlet and outlet contaminant concentrations are defined a priori. The aim is to determine which processes are interconnected and the water regeneration allocation. Obtained results highlight the benefits of the proposed model structure in comparison with traditional multiobjective approaches, by obtaining equilibrate different plants operating costs (i.e. gains between 12-25%) while maintaining an overall low resource consumption. Then, other case studies are approached by using the bilevel structure to include simultaneously energy networks in a multi-leader-multi-follower formulation where both environmental authorities are assumed to play a noncooperative Nash game. In the first case study, economic gain is proven to be more significant by including energy networks in the EIP structure. The second industrial case study explores a supply-demand utility network model where the environmental authority aims to minimize the total equivalent CO2 emissions in the EIP. In all cases, the enterprises’ plants are encouraged to participate in the EIP by the extremely favorable obtained results

Application of industrial symbiosis principles to the management of utility networks

Utility exchanges between different plants have shown to produce large energy savings, extending the potential advantages of Energy/Process Integration through Industrial Symbiosis principles. Systematic approaches to determine such exchanges in industrial networks have been already proposed, although some of them are only applicable to specific situations and some others introduce the figure of a central authority. However, assuming such a figure in non-cooperative situations may restrict the economic benefit of some companies involved, thus discouraging their participation and preventing eventual agreements. The aim of this work is to develop an optimization model that allows analyzing the different symbiosis alternatives in different conflicting situations, even without the presence of any authority. Scenarios inspired by Game Theory have been considered. The problem has been modelled using a Mixed Integer Linear Programming (MILP) formulation and its capacities are illustrated through a particular case from the literature. Results show that the method allows establishing utility exchanges between different plants, which can improve the energetic, economic and environmental efficiency of all of them, as well as the whole set. Considering cooperative scenarios may allow determining solutions producing total energy savings and cost reductions, but without taking the specific interests of individual companies into account. On the other hand, considering non-cooperative scenarios ensures desirable outcomes from the eventual agreements for each company. Furthermore, the model is able to identify the economic barriers of the companies for participating, thus, being a useful and applicable tool that may improve decision-making support for managing utility networks in such situations.Peer ReviewedPostprint (published version

Benefits analysis of optimal design of eco-industrial parks through life cycle indicators

Industrial symbiosis offers to companies the possibility to make economic benefits and to minimize environmental impacts by sharing flows and increasing inter-enterprise exchanges. However, even if some studies have demonstrated the benefits of the development of eco-industrial parks (EIP), there is no consensus to evaluate their benefits in a global point of view and there is a lack of integrated indicators for the assessment of EIPs. The aim of this study is to propose a holistic approach to evaluate the global impacts of an EIP. To reach this goal, the potential eco-industrial park of Mongstad in Norway has been chosen. Several steps are considered: a simulation through Aspen Properties®, then the superstructure optimization problem solved within GAMS® environment by minimizing the total cost of the EIP is done. Finally, an evaluation of the optimal solution through a life cycle approach is carried out. The results show that companies included in the EIP have environmental impacts reduced from 45% to 80% compared to the impacts of stand-alone companies

Optimal design of exchange networks with blind inputs and its application to Eco-industrial parks

Motivated by the design and optimization of the water exchange networks in Eco-Industrial Parks (EIP), we investigate the abstract Blind-Input model for general exchange networks. This abstract model is based on a Game Theory approach, formulating it as a Single-Leader-Multi-Follower (SLMF) game: at the upper level, there is an authority (leader) that aims to minimize the consumption of natural resources, while, at the lower level, agents (followers) try to minimize their operating costs. We introduce the notion of Blind-Input contract, which is an economic contract between the authority and the agents in order to ensure the participation of the latter ones in the exchange networks. More precisely, when participating in the exchange network, each agent accepts to have a blind input in the sense that she controls only her output fluxes, and the authority commits to guarantee a minimal relative improvement in comparison with the agent’s stand-alone operation. The SLMF game is equivalently transformed into a single mixed-integer optimization problem. Thanks to this reformulation, examples of EIP of realistic size are then studied numerically

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version

Aggregation in Game Theoretical Situations

The thesis deals with the class of Aggregative Games, namely strategic form games where each payoff function depends on the corresponding player's strategy and on some aggregation among strategies of all involved players. The first part of the thesis is devoted to the multi-leader multi-follower equilibrium concept for the class of aggregative games: the considered game presents aymmetry between two groups of players, acting noncooperatively within the group and one group is the leader in a leader-follower hierarchical model. Moreover, as it happens in concrete situations, the model is affected by uncertainty and the game is considered in a stochastic context. Assuming an exogenous uncertainty affecting the aggregator, the multi-leader multi-follower equilibrium model is presented and existence results for the stochastic resulting game are obtained in the smooth case of nice aggregative games, where payoff functions are continuous and concave in own strategies, as well as in the general case of aggregative games with strategic substitutes. These results apply to the global emission game and the teamwork project game. Then, an investment in Common-Pool Resources is studied: the situation of many agents interested in a common-pool resource, like water resource, is modeled as an aggregative game and existence results of Nash equilibria are obtained with or without convexity-like assumptions. In the special case of quadratic return functions, the game is also considered under uncertainty i.e. when the possibility of a natural disaster with a given probability may occur. In the second part of the thesis, in line with the literature on additively separable aggregative games, a class of non cooperative games, called Social Purpose Games, is introduced. In this class of games the payoff of each player depends separately on his own strategy and on a function of the strategy profile, the aggregation function, which is the same for all players, weighted by an individual benefit parameter which enlightens the asymmetry between agents toward the social part of the benefit. The two parts of the payoff function represent respectively the individual and the social benefits. For the class of social purpose games it has been showed that they have a potential, providing also a comparison between the Nash equilibrium strategies and the social optimum strategies, namely when all the players agree in maximizing the aggregate profit. For social purpose games we study the existence of the so called coalition leadership equilibrium: it is a multi-leader multi-follower model where a cooperative behaviour is assumed between players of the leading group and they decide to maximize the aggregation of their payoffs. The rest of the players act noncooperatively. This kind of equilibrium presents a mixture of cooperative and noncooperative behaviour, situation that often occurs in many applicative examples. The weights affecting the aggregation function allow to derive explicit conditions under which the leading coalition is stable. An application to a water resource game is illustrated

Designing Customised Bus Routes for Urban Commuters with the Existence of Multimodal Network – A Bi-Level Programming Approach

Customised bus (CB) is a cutting-edge mean of transportation and has been implemented worldwide. To support the spread of the CB system, methodologies for CB network design have been conducted. However, a majority of them cannot be adopted directly for multi-modal transportation environment. In this paper, we proposed a bi-level programming model to fill this gap. The upper-level problem is to maximise the usage of the CB system with the limitation of operation constraints. Meanwhile, the lower-level problem is to capture the traveller’s choice by minimising traveller’s generalised cost during travel. A solving procedure via genetic algorithm is further proposed and validated via the metro data at Shanghai. The results indicated that the proposed CB route network would attract nearly 5,000 users during morning peak period under the given metro transaction data. We further studied the features of the selected routes and found that the CB network mainly served residence to commercial or industrial parks travellers and would provide travel service with fewer stops, and higher travel efficiency by travelling through expressway