Managing computational complexity through using partitioning, approximation and coordination

Problem: Complex systems are composed of many interdependent subsystems with a level of complexity that exceeds the ability of a single designer. One way to address this problem is to partition the complex design problem into smaller, more manageable design tasks that can be handled by multiple design teams. Partitioning-based design methods are decision support tools that provide mathematical foundations, and computational methods to create such design processes. Managing the interdependency among these subsystems is crucial and a successful design process should meet the requirements of the whole system which needs coordinating the solutions for all the partitions after all. Approach: Partitioning and coordination should be performed to break down the system into subproblems, solve them and put these solutions together to come up with the ultimate system design. These two tasks of partitioning-coordinating are computationally demanding. Most of the proposed approaches are either computationally very expensive or applicable to only a narrow class of problems. These approaches also use exact methods and eliminate the uncertainty. To manage the computational complexity and uncertainty, we approximate each subproblem after partitioning the whole system. In engineering design, one way to approximate the reality is using surrogate models (SM) to replace the functions which are computationally expensive to solve. This task also is added to the proposed computational framework. Also, to automate the whole process, creating a knowledge-based reusable template for each of these three steps is required. Therefore, in this dissertation, we first partition/decompose the complex system, then, we approximate the subproblem of each partition. Afterwards, we apply coordination methods to guide the solutions of the partitions toward the ultimate integrated system design. Validation: The partitioning-approximation-coordination design approach is validated using the validation square approach that consists of theoretical and empirical validation. Empirical validation of the design architecture is carried out using two industry-driven problems namely the a hot rod rolling problem’, ‘a dam network design problem’, ‘a crime prediction problem’ and ‘a green supply chain design problem’. Specific sub-problems are formulated within these problem domains to address various research questions identified in this dissertation. Contributions: The contributions from the dissertation are categorized into new knowledge in five research domains: • Creating an approach to building an ensemble of surrogate models when the data is limited – when the data is limited, replacing computationally expensive simulations with accurate, low-dimensional, and rapid surrogates is very important but non-trivial. Therefore, a cross-validation-based ensemble modeling approach is proposed. • Using temporal and spatial analysis to manage the uncertainties - when the data is time-based (for example, in meteorological data analysis) and when we are dealing with geographical data (for example, in geographical information systems data analysis), instead of feature-based data analysis time series analysis and spatial statistics are required, respectively. Therefore, when the simulations are for time and space-based data, surrogate models need to be time and space-based. In surrogate modeling, there is a gap in time and space-based models which we address in this dissertation. We created, applied and evaluated the effectiveness of these models for a dam network planning and a crime prediction problem. • Removing assumptions regarding the demand distributions in green supply chain networks – in the existent literature for supply chain network design, there are always assumptions about the distribution of the demand. We remove this assumption in the partition-approximate-compose of the green supply chain design problem. • Creating new knowledge by proposing a coordination approach for a partitioned and approximated network design. A green supply chain under online (pull economy) and in-person (push economy) shopping channels is designed to demonstrate the utility of the proposed approach

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

RESILIENT AND STRUCTURALLY CONTROLLABLE DESIGN OF MULTI-LEVEL INFRASTRUCTURE NETWORKS UNDER DISRUPTIONS

An infrastructure network comprises of different entities that are connected by the flow of materials, products, information or electricity. Disruptions could occur at any section of the network for a wide variety of reasons. Some examples include: company mergers (e.g., Halliburton’s impending purchase of Baker Hughes), labor union strikes (e.g., labor strike on the west coast of the United States in 2002), sanctions imposed or lifted (e.g., economic sanctions against Iran being lifted by the UN in July 2015), plantations being destroyed (banana plantations were destroyed by Hurricane Mitch in 1998), air traffic being suspended due to weather or terrorism, main suppliers put out of commission by natural disasters (e.g., the 1999 earthquake in Taiwan disrupted semiconductor fabrication facilities), etc. A resilient infrastructure network is one that has the ability to recover quickly from disruptions and ensure customers are minimally affected, while the simultaneous design of operational and strategic decisions in all levels of the network structure are considered. It becomes very important to design a resilient multi-level infrastructure network in order to manage disruptions using appropriate pre-disruption and post-disruption restoration strategies. The capability of structural controllability can help in recovering a disrupted infrastructure network and increasing its resilience before, during and after the occurrence of disruptions. In this dissertation, the problem of applying structural controllability in order to design a resilient multi-level infrastructure network under disruptions with the selection of appropriate restoration strategies and consideration of the trade-off between effectiveness and redundancy in the resilience analysis is considered. The aforementioned problem has four aspects worth of consideration: a) multi-level network structures, b) restorations strategies, c) resilience analysis, and d) structural controllability. In this regard, the primary research question is defined as: What methods are required for designing a resilient infrastructure network under disruptions through selecting appropriate restoration strategies in a manner of applying structural controllability? The primary research question is broken into four secondary questions in respect to each four aspects of the considered problem as follows. - What is a method to design a multi-level infrastructure network (e.g., node-level and network-level structures) considering both operational and strategic decisions? - What is a method to design a resilient infrastructure network through selecting appropriate pre-disruption (e.g., facility fortification, backup inventory) and post-disruption (e.g., reconfiguration, flexible production and inventory capacity) restoration strategies? - What is a method to evaluate network resilience as a function of time considering effectiveness and redundancy measures (e.g., service level and transportation time as effectiveness measures and control cost as redundancy measure)? - What is a method to determine the minimum number of driver nodes (i.e., driver nodes or controllers are required for controlling networks) to get structurally controllable infrastructure networks? In response to the primary research question, two methods are proposed in this dissertation. The first method is the multi-level infrastructure network (MLIN) method which refers to the first aspect of the problem. The second method is the resilient and structurally controllable infrastructure network (RCIN) method which refers to the second, third and last aspects of the problem. Based on these two proposed methods, the main created new knowledge in this dissertation is in tailoring and incorporating the structural controllability theory in the resilience analysis of disrupted infrastructure networks. The proposed MLIN and RCIN methods are verified and validated using two examples from the energy industry in the context of the validation square. An example of a network of electric charging stations for plug-in hybrid electric vehicles using renewable energy and power grid as sources of energy is used to demonstrate and validate the MLIN method. An example of a network of a multi-product European petroleum industry is used to demonstrate and validate the RCIN method. Although the proposed methods are solved for the two examples, both of them are generalizable to be applicable to any network-based complex engineered systems under disruptions

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic