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This work presents a Mixed Integer Programing (MIP) formulation for a new complex NP-hard combinatorial optimization problem, the Location Inventory Routing with Distribution Outsourcing (LIRPDO). Due to its computational complexity, only small problem instances can be solved by exact solvers. Therefore, a General Variable Neighborhood Search (GVNS)-based metaheuristic algorithm for solving large LIRPDO instances is presented. The proposed approach has been tested on 20 new randomly generated LIRPDO instances, 20 existing benchmark LIRP instances from the literature and 30 new large-scale random generated LIRP instances. An extended numerical analysis illustrates the efficiency of the underlying method, leading to acceptable solutions requiring limited computational effort.

This work, based on Stackelberg hypothesis, considers a conventional power producer exercising their dominant position in an electricity pool with high penetration of wind power production. A bi-level optimization model is used to provide optimal offer strategies for the aforementioned producer in a jointly cleared energy and reserve pool settled through an hourly auction process. The upper-level problem illustrates the expected profit optimization of the strategic producer while the lower-level problem represents the energy-only market clearing process through a two-stage stochastic program. The first stage clears the day ahead market, and the second stage presents the system operation in balancing time though a set of plausible wind power production realizations. The bi-level problem is recast into a mathematical program with equilibrium constraints which is then reformulated into a mixed integer linear program. These transformations occur using the Karush-Kuhn-Tucker optimality conditions and the strong duality theory. The suggested model provides optimal strategic offers and local marginal prices under different levels of wind penetration and network line transmission capacities.

The production scheduling problem of a real-life food industry is addressed in this work. An efficient MILP-based solution strategy is developed to optimize weekly schedules for a Spanish canned fish production plant. The multi-stage, multi-product facility under study consists of both continuous and batch operations resulting in an extremely complex scheduling problem. In order to reduce its computational complexity, an aggregated approach is cleverly proposed, in which the continuous processes are explicitly modeled, while valid feasibility constraints are introduced for the batch stage. Based on this approach, two MILP models are developed, using a mixed discrete-continuous time representation. All technical, operating and design constraints of the facility are considered, while salient characteristics of the canned-food industry, such as assurance of the end products’ microbiological integrity, are aptly modeled. Both the minimization of makespan and changeovers is studied. In order to meet the computational limits imposed by the industry, an order-based decomposition algorithm is further investigated. The method is successfully applied to real-life case studies, generating near-optimal solutions in short CPU times. The suggested solution strategy can be easily extended to consider other real-life scheduling problems from the process industries sector that share similar production characteristics.

This work proposes Adaptive General Variable Neighborhood Search metaheuristic algorithms for the efficient solution of Pollution Location Inventory Routing Problems (PLIRPs). A comparative computational study, between the proposed methods and their corresponding classic General Variable Neighborhood Search versions, illustrates the effectiveness of the intelligent mechanism used for automating the re-ordering of the local search operators in the improvement step of each optimization method. Results on 20 PLIRP benchmark instances show the efficiency of the proposed metaheuristics.

This work considers a power producer with dominant position in electricity market. A bi-level model is constructed to derive optimal offering strategies for this producer. The bi-level model is reformed into a mathematical programming with equilibrium constraints (MPEC) model which is then recast into a mixed integer linear program using strong duality theorem and Karush-Kuhn-Tacker first order optimality conditions. The proposed algorithm results in optimal scheduled thermal and wind energy production as well as reserve deployments for the strategic producer. It also provides endogenous formation of local marginal prices and optimal offers under network constraints and wind generation uncertainty.

This paper presents an optimization-based approach to address the problem of the optimal daily energy scheduling of interconnected power systems in electricity markets. More specifically, a Mixed Integer Linear Programming model (MILP) has been developed to address the specific challenges of the underlying problem. The main focus of the proposed framework is to examine the importance and the impacts of electricity interconnections and cross-border electricity trade on the scheduling of power systems, both at a technical and economic level. The applicability of the proposed approach has been tested on an illustrative case study including five power systems which can be interconnected (with a certain interconnection structure) or not. The proposed model determines in a detailed and analytical way the optimal power generation mix, the electricity trade among the systems, the electricity flows (in case of interconnection options), the marginal price of each system, as well as it investigates through a sensitivity analysis the effects of the available interconnection capacity on the resulting power production mix. The work demonstrates that the proposed optimization approach is able to provide important insights into the appropriate energy strategies followed by the market participants, as well as on the strategic long-term decisions to be implemented by investors and/or policy makers at a national and/or regional level, underlining potential risks and providing appropriate price signals on critical energy infrastructure projects under real market operating conditions.

This work presents a Mixed Integer Linear Programming (MILP) model for the optimal interconnected power systems planning over a time horizon of one year. The power generation units include thermal units, hydroelectric units and renewable units (wind and solar power plants). Each system can produce power in order to satisfy its demand and/or supply energy to another system via interconnections. The time horizon of interest, consists of a representative day for each month of the year. The model considers the possibility of building new units selected from a set of proposed ones, as well as expanding the capacity of existing renewable energy units (generation expansion planning), stressing the flexibility that electricity trade provides to power systems. The possibility of expanding the existing interconnection capacity between systems is also considered (transmission expansion planning). The proposed optimization model relies on balance, design, operational and logical constraints. Environmental-related con...

This work presents a mathematical modeling framework for the simulation and optimization of pressure/vacuum swing adsorption (P/VSA) processes for post-combustion CO 2 capture. A single-stage P/VSA process for CO 2 capture from dry flue gas is considered using new zeolite 13X-based adsorbents resulting from perturbation on the 13X zeolite isotherm. A two-bed six-step P/VSA cycle configuration with light product pressurization is employed in systematic simulation and optimization studies. First a zeolite 13X, the current benchmark commercial adsorbent for CO 2 capture, is considered. Accordingly, the model is used to study and evaluate new zeolite 13X-based adsorbents for more efficient CO 2 capture. The results from systematic comparative simulation studies demonstrate that a modified zeolite 13X-based adsorbent appears to have better process performance compared with the original zeolite 13X. Furthermore, process optimization studies employing the above potential adsorbents are performed to minimize energy consumption for specified minimum requirements in CO 2 purity and recovery. The optimization results indicate that the minimum target of 95% in CO 2 purity and 90% in CO 2 recovery is easily met for the P/VSA process under consideration for both potential adsorbents under different operating conditions resulting in different energy requirements and CO 2 productivity.

This paper presents an optimization-based approach to address the problem of the optimal daily energy scheduling of interconnected power systems in electricity markets. More specifically, a Mixed Integer Linear Programming model (MILP) has been developed to address the specific challenges of the underlying problem. The main focus of the proposed framework is to examine the importance and the impacts of electricity interconnections and cross-border electricity trade on the scheduling of power systems, both at a technical and economic level. The applicability of the proposed approach has been tested on an illustrative case study including five power systems which can be interconnected (with a certain interconnection structure) or not. The proposed model determines in a detailed and analytical way the optimal power generation mix, the electricity trade among the systems, the electricity flows (in case of interconnection options), the marginal price of each system, as well as it investigates through a sensitivity analysis the effects of the available interconnection capacity on the resulting power production mix. The work demonstrates that the proposed optimization approach is able to provide important insights into the appropriate energy strategies followed by the market participants, as well as on the strategic long-term decisions to be implemented by investors and/or policy makers at a national and/or regional level, underlining potential risks and providing appropriate price signals on critical energy infrastructure projects under real market operating conditions.

This work presents a generic optimization framework to address the problem of the optimal design and operational scheduling of energy microgrids. The problem to be solved is formulated as a mixed-integer linear programming (MILP) model whose objective function concerns the total cost minimization of the energy microgrid. The energy generating units to be installed consist of technologies using fuel (natural gas) as a raw material (microturbines, fuel cells etc.), and renewable energy sources (wind and solar). The microgrid is divided into a certain number of zones, each of which is characterized by a given amount of electricity demand to be satisfied, while the system can exchange electrical energy with the main power grid by acquiring from and selling energy to the grid. The efficiency and applicability of the proposed model is illustrated using three case studies. The maximum allowable level of CO2 emissions, the price of electricity purchased from the main grid, and the price of electricity sold to the main grid constitute the parameters whose influences on the economic variables of the microgrid, on the quantities and the capacities of the installed technologies, as well as on the energy balance of the microgrid are investigated. The proposed model provides a systematic and analytical methodological framework for a detailed planning and scheduling of energy microgrids, highlighting potential risks and appropriate price signals on critical energy projects undertaken by investors and/or designed by policy makers at a national and/or regional level under realistic operating conditions.

The aim of this work is to demonstrate the response of advanced model-based predictive control (MPC) strategies for Polymer Electrolyte Membrane Fuel cell (PEMFC) systems. PEMFC are considered as an interesting alternative to conventional power generation and can be used in a wide range of stationary and mobile applications. An integrated and modular computer-aided Energy Management Framework (EMF) is developed and deployed online to an industrial automation system for monitoring and operation of a PEMFC testing unit at CERTH/CPERI. The operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. A dynamic model is utilized and different MPC strategies are online deployed (Nonlinear MPC, multiparametric MPC and explicit Nonlinear MPC). The response of the MPC strategies is assessed through a set of comparative experimental studies, illustrating that the control objectives are achieved and the fuel cell system operates economically and at a stable environment regardless of the varying operating conditions.

In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications.

This work investigates the interaction between power producers with conventional and wind generation portfolios participating in a network-constrained pool-based market. A stochastic bi-level problem is introduced to model the strategic behavior of each single producer. The upper-level problem maximizes the producers’ expected profits and the lower-level problem optimizes the jointly cleared energy and balancing market under economic dispatch. Market participants’ offers are modeled using linear stepwise curves, and the stochastic wind power generation is realized through a set of plausible wind scenarios. The bi-level problem is recast into a single-level mathematical problem with equilibrium constraints with primal-dual formulation using the Karush-Kuhn-Tacker first order optimality conditions and the strong duality theorem. The joint solution of all strategic producers’ problems constitutes an equilibrium problem with equilibrium constraints. The latter is reduced into an equivalent mixed integer linear program by using disjunctive constraints. Different objective functions are applied to the final program to define the range of market equilibria, and a single-iterate diagonalization process is used to justify those equilibria that are meaningful. The model addresses several cases considering different types of market competition, transmission line congestions, and different levels of wind power penetration and volatility.

Currently, there is an increasing clinical demand for bone implants for several orthopedic and maxillofacial surgical procedures. Meeting these demands can be achieved with stem cells differentiate...

This work considers the production scheduling of a real-life, consumer goods industry. The production facility consists of multiple continuous stages, and the packing stage constitutes the main pro...

This work, based on Stackelberg hypothesis, considers a conventional power producer exercising their dominant position in an electricity pool with high penetration of wind power production. A bi-level optimization model is used to provide optimal offer strategies for the aforementioned producer in a jointly cleared energy and reserve pool settled through an hourly auction process. The upper-level problem illustrates the expected profit optimization of the strategic producer while the lower-level problem represents the energy-only market clearing process through a two-stage stochastic program. The first stage clears the day ahead market, and the second stage presents the system operation in balancing time though a set of plausible wind power production realizations. The bi-level problem is recast into mathematical programming with equilibrium constraints (MPEC) which is then reformulated into an MILP. These transformations occur using the Karush-Kuhn-Tucker optimality conditions and the strong duality theory. The suggested model provides optimal strategic offers and local marginal prices under different levels of wind penetration and network line transmission capacities.

This work presents the development and application of an efficient solution strategy for the optimal production scheduling of a real-life food industry. In particular, the case of a canned fish production facility for a large-scale Spanish industry is considered. Main goal is to develop an optimized weekly schedule, in order to minimize the total production makespan. The proposed solution strategy constitutes the basis to develop an efficient and robust approach for this complex scheduling problem. A general precedence Mixed-Integer Linear Programming (MILP) model is utilized for all scheduling-related decisions (unit allocation, timing and sequencing). To solve the scheduling problem in a computational time accepted by the industry, a two-step decomposition algorithm is employed. Salient characteristics of the canned fish industry are aptly modelled, while valid industry-specific heuristics are incorporated. The suggested solution strategy is successfully applied to a real study case, corresponding to the most demanding week of the plant under study.

This work considers a strategic producer whose generation portfolio consists of conventional and wind power production. Based on the single leader-follower game, a bi-level complementarity model is constructed to derive optimal capacity withholding strategies for this portfolio in a pool-based market. The upper level of the model represents the maximization of the strategic producer’s expected profits while the lower level represents the security-constrained economic dispatch conducted by the independent system operator. The market clearing scheme refers to energy-only markets optimizing jointly scheduled energy and reserves through a two-stage stochastic programming. The first stage illustrates the day-ahead market clearing and the second stage illustrates the balancing market clearing taking into consideration the wind generation uncertainty. With the use of the Karush-Kuhn-Tacker optimality conditions the initial bi-level model is recast into a mathematical programming with equilibrium constraints model which is then reduced into an equivalent mixed integer linear programming using the strong duality theorem and disjunctive constraints. The proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments. It also provides optimal offers based on the endogenous formation of local marginal prices under network constraints and different wind energy penetration levels.

The production scheduling of a real-life multi-product, mixed batch and continuous food industrial facility is considered in this work. More specifically, the scheduling of canned fish production in a large-scale Spanish industry is studied in detail. The deployment of an efficient solution strategy is proposed to handle the computationally challenging scheduling problem. In particular, a Mixed-Integer Linear Programming (MILP) model is used, in parallel with a decomposition technique. The problem under consideration focuses on two important stages of the plant, the sterilization and the packaging. The proposed strategy takes into account the specific characteristics of the canned fish production facility resulting in interesting results. It should be noted that the same methodology can be used with appropriate modifications in other food process industries with similar production characteristics.

This work presents a generic Mixed-Integer Linear Programming model that integrates a Mid-term Energy Planning model for the optimal integration of power plants into interconnected power generation systems. The time horizon consists of a representative day for each month of the year. The Unit commitment problem is modelled in details to optimally determine the optimal operational strategy in order to meet the electricity demand at a minimum total cost by utilizing the optimal combination of a set of available power generation plants. Furthermore, the model considers the possibility of building new units selected from a set of proposed ones, as well as expanding the capacity of existing renewable energy units. The possibility of expanding the existing interconnection capacity between systems is also considered. Environmental-related constraints for the production of CO2, NOX, SOX and PMX emissions are also taken into account. The main objective is the minimization of the total annualized cost. The applicability of the proposed model is illustrated in a case study including two interconnected power systems. Finally, a sensitivity analysis is performed in order to investigate the effect of key process parameters on the final power generation policies.

This study presents a general closed-loop control strategy for optimal insulin delivery in type 1 Diabetes Mellitus (T1DM). The proposed control strategy aims toward an individualized optimal insulin delivery that consists of a patient-specific model predictive controller, a state estimator, a personalized scheduling level, and an open-loop optimization problem subjected to patient-specific process model and constraints. This control strategy can be also modified to address the case of limited patient data availability resulting in an “approximation” control strategy. Both strategies are validated in silico in the presence of predefined and unknown meal disturbances using both a novel mathematical model of glucose–insulin interactions and the UVa/Padova Simulator model as a virtual patient. The robustness of the control performance is evaluated under several conditions such as skipped meals, variability in the meal time, and metabolic uncertainty. The simulation results of the closed-loop validation studies indicate that the proposed control strategies can potentially achieve improved glycaemic control.

This paper introduces a general mathematical programming framework that employs an innovative generalized supply chain network (SCN) composition coupled with forward and reverse logistics activities. Generalized echelon will have the ability to produce/distribute all forward materials/products and recover/redistribute simultaneously all the returned which are categorized with respect to their quality zone. The work addresses a multi-product, multi-echelon and multi-period Mixed-Integer Linear Programming (MILP) problem in a closed-loop supply chain network design solved to global optimality using standard branch-and-bound techniques. Further, the model aims to find the optimal structure of the network in order to satisfy market demand with the minimum overall capital and operational cost. Applicability and robustness of the proposed model are illustrated by using a medium real case study from a European consumer goods company whereas its benefits are valued through a comparison with a counterpart model that utilizes the mainstream fixed echelon network structure.

Supply chain network (SCN) design and operation is a strategic and value adding process for all synchronous companies. In the face of globalization, several issues should be contemplated and incorp...

The power sector faces a rapid transformation worldwide from a dominant fossil-fueled towards a low carbon electricity generation mix. Renewable energy technologies (RES) are steadily becoming a greater part of the global energy mix, in particular in regions that have put in place policies and measures to promote their utilization. This paper presents an optimization-based approach to address the generation expansion planning (GEP) problem of a large-scale, central power system in a highly uncertain and volatile electricity industry environment. A multi-regional, multi-period linear mixed-integer linear programming (MILP) model is presented, combining optimization techniques with a Monte Carlo (MCA) method and demand response concepts. The optimization goal concerns the minimization of the total discounted cost by determining optimal power capacity additions per time interval and region, and the power generation mix per technology and time period. The model is evaluated on the Greek power system (GPS), taking also into consideration the scheduled interconnection of the mainland power system with those of selected autonomous islands (Cyclades and Crete), and aims at providing full insight into the composition of the long-term energy roadmap at a national level.

Clinical chemotherapy dosage strategies for leukemia rely on weight/height calculations theoretically correlated to patient drug tolerance. However, over- and under- dosage still exist in clinical practice, which could be overcome by quantifying the actual fraction of cancer cells susceptible to be eradicated. In this work, we show how choosing models that are accurate enough in simulating the biological processes ultimately affecting drug efficacy is critical in order to disentangle patient to patient heterogeneity. Incorporating heterogeneity from measurable sources in such a manner brings us a step closer in our path towards the development of personalized rational therapies.

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system’s performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable’s feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives.

This work presents a systematic model-based approach for the simulation and optimisation of an integrated two-stage pressure/vacuum swing adsorption (P/VSA) process used for the reduction of CO2 emissions from dry flue gas. According to systematic comparative simulation studies, all possible combinations of two promising adsorbents (zeolite 13X and Mg-MOF-74) have been considered, to study the effect of adsorbent type on key process performance indicators of the process under consideration at nearly atmospheric feed pressures without using deep vacuum. Furthermore, dynamic optimisation studies for different combinations of adsorbents in the first and second stage (zeolite 13X – Mg-MOF-74 and zeolite 13X – zeolite 13X) have been performed to minimize energy consumption for specified minimum requirements in CO2 purity and CO2 recovery. The optimisation results indicate that the minimum requirements of 95% in CO2 purity and 90% in CO2 recovery are met for the integrated two-stage P/VSA process, for both combinations of adsorbents under different operating conditions, thus resulting in different energy requirements and CO2 productivities.

Supply chain network (SCN) modeling has gain a great research interest as companies realized that scientific approaches in managing their SCN's, rather than “common-sense” heuristics, are the roads to achieve sustainability, profitability, growth, and competiveness. Ever since, the relevant literature is supplied with models that aim to optimize SCN's design and/or operation. The former is a strategic process that designs network's infrastructure and concerns long term investments which undertake huge amounts of capitals. SCN managers should be able to evaluate how these decisions contribute to the overall performance of the company and not assess them with only cost oriented indicators. By employing advance financial management methods, such as sale and leaseback (SLB), fixed assets could be the medium to improve liquidity and strengthen credit solvency. This paper aims to enrich the SCN design literature by introducing a Mixed-Integer Non Linear Programming (MINLP) model that integrates SLB technique with SCN design decisions. By exploiting the properties of the MINLP model it is reformulated into an exact Mixed-Integer Linear Programming (MILP) model that is solved to global optimality. A real case study from a consumer goods company is utilized in order to show model's functionality and to evaluate its adaptability, robustness, and benefit. The model could assist and support SCN managers in effective decision making in the strategic level.

Two binary integer programming discrete-time models and two precedence-based mixed integer programming continuous-time formulations are developed for the resource-constrained project scheduling problem. The discrete-time models are based on the definition of binary variables that describe the processing state of every activity between two consecutive time points, while the continuous-time models are based on the concept of overlapping of activities, and the definition of a number of newly introduced sets. Our four mathematical formulations are compared with six representative literature models in 3240 benchmark problem instances. A detailed computational comparison assesses the performance of the mathematical models considered.

Energy planning aims at improving the overall efficiency, economic viability and reducing the environmental impact of energy management systems. Deregulation of electricity markets along with technology development have increased the level of competition allowing energy consumers to select among a variety of energy technologies, fuels and/or suppliers. This work presents a linear mixed integer programming model for the optimal design and operational planning of energy networks based on combined heat and power generators. The studied area is divided into a number of sections, each of which is characterized by a specific heat and electricity demand. Various energy generation technologies and heat storage tanks are modeled, while interchange of electricity can take place among the sections of the network, which is connected to the main power grid for potential power trade with it. There is also the option of an external heat source (i.e., a refinery) constituting an alternative supplier of heat to the sectors of the network. The objective function represents the minimization of total cost under full heat and electricity demand satisfaction. The applicability of the proposed model is illustrated using two illustrative examples, including a residential and an urban energy network. Finally, Monte Carlo simulations have been utilized to capture the effect of uncertainty characterizing some varying parameters, such as the heat demand (residential energy network) as well as the available heat from the refinery (urban energy network).

This paper presents a mixed-integer linear programming (MILP) model for the optimal long-term energy planning of a (national) power generation system. In order to capture more accurately the spatial and technical characteristics of the problem, the underlying geographical area (country) is divided into a number of individual networks that interact with each other. The proposed model determines the optimal planning of the power generation system, the selection of the power generation technologies, the type of fuels and the plant locations so as to meet the expected electricity demand, while satisfying environmental constraints in terms of CO2 emissions. Furthermore, the suggested model determines the electricity imports from neighbouring countries, the electricity transmission as well as the transportation of primary energy resources between domestic networks. A real case study concerning the Greek energy planning problem demonstrates the applicability of the proposed approach, which can provide policy makers with a systematic computer-aided tool to analyse various scenarios and technology options. Finally, a sensitivity analysis was conducted in order to capture the influence of some key parameters such as electricity demand, natural gas and CO2 emission price as well as wind power investment cost.

A distributed energy system refers to an energy system where energy production is close to end use, typically relying on small-scale energy distributed technologies. It is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. However, distributed energy systems such as micro-grids in residential applications may not be able to produce the potential benefits due to lack of appropriate system configurations and suitable operation strategies. The optimal design, scheduling and control of such a complex system are of great importance towards their successful practical realization in real application studies. This paper presents a short review and an energy systems engineering approach to the modeling and optimization of micro-grids for residential applications, offering a clear vision of the latest research advances in this field. Challenges and prospects of the modeling and optimization of such distributed energy systems are also highlighted in this work.

AML is a cancer of the blood and bone marrow which results from the combined effects of genetic mutations, aberrant interactions in the microenvironment and altered networks of complex chemical reactions at the molecular and cellular level, some of which can be targeted with anti-neoplastic drugs called chemotherapy. AML can be treated with chemotherapy, the types and doses of which are dependent on characteristics of the patient, the sub-type of the tumor and the use of other, often synergistic, anti-cancer drugs, and the doses for which are limited by toxic adverse effects of treatment. Current treatment protocols are designed based on pre-clinical animal experiments and on empirical clinical trials as well as the acquired experience of subspecialist physicians. Mathematical modeling can assist in improving chemotherapy effectiveness and limiting toxicity through a systematic approach in designing treatment protocols. Specifically, these mathematical models should enable a description of the normal and the leukemic cell populations as dependent on disease characteristics (cell cycle distribution into phases, proliferation rate, initial disease and normal population state) and on physiological characteristics of the patient such as age, sex, body surface area that control and define the drug kinetics (concentration profile in tumor site). Such a model can then lead to an optimal management of the available drug kinetics in order to effectively eradicate the maximum possible tumor volume while limiting toxicity of the normal cell population and that will be maintained within certain defined limits. Herein, a model is presented for the first cycle of chemotherapy induction treatment for AML using daunorubicin (DNR) and cytarabine (Ara-C) anti-leukemic agents, a standard intensive treatment protocol for AML. The proposed model combines critical targets of drug actions on the cell cycle, together with pharmacokinetic (PK) and pharmacodynamic (PD) aspects providing a complete description of drug diffusion and action after administration. Tumor-specific characteristics, such as tumor burden and cell cycle times, as well as patient-specific characteristics, such as gender, age, weight and height, are incorporated into the model in an attempt to gain insights into the personalized cell dynamics during treatment. Moreover, an optimal control problem is formulated and solved so as to obtain the chemotherapeutic schedule which would maximize leukemic cell kill (therapeutic efficacy) while minimizing death of the normal cell population, thereby reducing toxicities. Simulation results for a standard treatment protocol are obtained for a patient case study; an optimized treatment schedule is also obtained and the cell populations are analyzed and compared in detail for both the standard and the optimized treatment protocols.

The aim of this work is to develop and deploy an advanced model-based control framework for a polymer electrolyte membrane (PEM) fuel cell system. The framework relies on nonlinear model predictive control (NMPC) using a reliable and efficient dynamic optimization approach which discretizes both manipulated and state variables. The optimization is performed using a direct transcription method that handles the optimal control problem as a nonlinear programming (NLP) problem. The motivation for the control is to ensure optimum power generation following a variable load demand with acceptable response time while avoiding oxygen starvation and minimizing hydrogen consumption. To validate the applicability, efficiency and robustness of the NMPC scheme a small-scale fully automated unit was used and an experimentally validated semi-empirical dynamic model was utilized at the core of the optimization scheme. The on-line application of the multivariable controller shows that the proposed framework can accomplish the desired objectives for power demand in the context of a safe operating region. Furthermore the controller exhibits excellent performance in terms of computational requirements and can follow load changes with a negligible error in its response, even at varying operating conditions.

A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply uncertainty poses significant challenges in terms of both modelling and corresponding solution strategies. This paper proposes a two-stage stochastic programming model for the optimal design of distributed energy systems. A two-stage decomposition based solution strategy is used to solve the optimization problem with genetic algorithm performing the search on the first stage variables and a Monte Carlo method dealing with uncertainty in the second stage. The model is applied to the planning of a distributed energy system in a hotel. Detailed computational results are presented and compared with those generated by a deterministic model. The impacts of demand and supply uncertainty on the optimal design of distributed energy systems are systematically investigated using proposed modelling framework and solution approach.

Analytical equations were derived to determine the undrained lateral bearing capacity of rigid piles in cohesive soil. Piles in level ground and piles placed at a distance from the crest of a slope were examined, taking account of the effect of the adhesion at the pile–soil interface. The derived analytical solutions were used to develop charts relating the lateral pile capacity to the pile length/diameter ratio, the pile–soil adhesion, the distance of the point of load application from the ground to the pile diameter ratio, the inclination of the slope and the distance of the pile from the crest of the slope to the pile diameter ratio. They were also used to derive a reduction factor which, when multiplied by the lateral bearing capacity for level ground, gives the bearing capacity of the same pile near a slope. In addition, a critical non-dimensional distance between the pile and the crest of the slope, at which the bearing capacity approaches that for a level ground, was determined. The bearing capacity charts obtained for level ground were compared to the classic Broms' charts and to others derived using several different lateral earth pressure distributions along the pile. Comparisons were also made between the results of the proposed method for piles near slopes and those obtained from charts based on upper bound calculations. Finally, the proposed new method was validated through a comparison with the results of a large number of pile load tests, in which a remarkable agreement was observed between the analytical results and the measurements.

A promising and shortly emerging energy supply chain network based on residential-scale microgeneration through micro combined heat and power systems is proposed, modeled and optimized in this work. Interchange of electrical energy can take place among the members of this domestic microgrid, which is connected to the main electrical grid for potential power interchange with it. A mathematical programming framework is developed for the operational planning of such energy supply chain networks. The minimization of total costs (including microgeneration system’s startup and operating costs as well as electricity production revenue, sales, and purchases), under full heat demand satisfaction, constitutes the objective function in this study. Additionally, an alternative microgrid structure that allows the heat interchange within subgroups of the overall microgrid is proposed, and the initial mathematical programming formulation is extended to deal with this new aspect. An illustrative example is presented in order to highlight the particular significance of selecting a proper optimization goal that thoroughly takes into account the major operational, technical and economic driven factors of the problem in question. Also, a number of real-world size case studies are used to illustrate the efficiency, applicability and the potential benefits of the microgeneration energy supply chain networks suggested in this study. Finally, some concluding remarks are drawn and potential future research directions are identified.

Optimal design and operation of combined cooling, heating and power (CCHP) systems are complicated due to the fluctuating energy demands. Many mathematical models for the design and/or operation of CCHP systems have been developed. Most of them adopt a constant efficiency assumption, whilst others take equipment off-design characteristics into account. In this paper, we present two mathematical models for the optimal design and operation of CCHP systems with the target of minimising the total annual cost. One model is formulated using the constant efficiency assumption. In the other model, off-design characteristics of all equipments are considered. Comparative studies using different models were performed to examine the impacts of equipment off-design characteristics on the accuracy of the optimal design of CCHP systems. Results show that introduction of thermal storage facilities, connection to power grid and well designed operation strategies can diminish the negative impacts of adopting the constant efficiency assumption.

Inherent uncertainties and risks in the economic environment are diffused to all vital operations of a supply chain network (SCN). However, the great impact of this contagion is on the financial operation due to its interdependence with financial markets and business conditions. Economic uncertainty poses uncertainty in the financial status of SCNs and this in turns leads to sustainability and growth risks. Financial performance and credit solvency are two essential pillars of financial status capable of providing the necessary capitals to a SCN. As each of these pillars focuses on a different aspect of investment attractiveness, underlined trade-offs exist, under various economic conditions, and challenge further investigation. This paper aims to enrich the SCN design literature by introducing a mathematical model that integrates financial performance and credit solvency modelling with SCN design decisions under economic uncertainty. The proposed multi-objective mixed integer non linear programming (moMINLP) model enchases financial performance through economic value added (EVA™) and credit solvency through a valid credit scoring model (Altman's Z-score). The applicability of the model is illustrated by using a real case study. The model could be used as an effective strategic decision tool by managers responsible for strategic SCN design.

This work presents the use of advanced control methodologies for a Polymer Electrolyte Membrane (PEM) fuel cell system. Two Model Predictive Control (MPC) strategies, a multi-parametric and a nonlinear MPC, are combined into a framework (explicit Nonlinear MPC - exNMPC) in order to systematically exploit their synergistic benefits and enhance the behaviour of the system. The new controller solves online a dynamic simultaneous optimization problem using a warm-start procedure that considers a piecewise affine (PWA) approximation defined offline over the partition of the system's feasible space. The proposed control scheme is deployed on an industrial Supervisory Control and Data Acquisition (SCADA) system and its response is explored. Experimental studies demonstrate that the multivariable exNMPC controller exhibits a very good performance in terms of computational requirements while achieving an excellent convergence rate even at varying operating conditions.

This work presents a methodology for the scheduling of networks that consist of various energy cogeneration units (i.e., generate both electrical energy and heat) under high energy demand uncertainties of bounded form. First, a mathematical framework for the problem in question is developed, and then we present a reactive scheduling approach that relays on multiparametric programming and a receding horizon. In brief, the original mathematical scheduling model is reformulated into a multiparametric programming problem, wherein we consider as uncertain parameters: the energy demands as well as the initial and the final state of the overall system. The resulting parametric optimization problem is solved once and off-line, and afterwards a receding horizon-like approach is applied for the reactive scheduling –that acts on the fly in the energy demand realizations– by performing just function evaluations and avoiding the on-line optimization of such systems. A couple of problem instances are solved to shed light on the potential benefits of the suggested method. The proposed concept has highly practical interest and is very promising since could find application in several other optimization problems that can involve: large-scale optimization problems and process scheduling problems under uncertainty.

Fuel cell systems are a promising alternative to traditional power sources for a wide range of portable, automotive and stationary applications and have an increasing potential for wider use as the demand for clean energy is increasing and the focus is shifting towards renewable energy generation. This chapter has a multidisciplinary scope, the design of a computer-aided framework for monitoring and operation of integrated fuel cell systems and the development of advanced model-based control schemes. The behavior of the framework is experimentally verified through the online deployment to an automated small-scale fuel cell unit, demonstrating excellent response in terms of computational effort and accuracy with respect to the control objectives.

In this work, we propose a mixed integer programming (MIP) model based on a new State-Task Network (STN) representation for the modeling and optimization of energy supply chains. A unified modeling framework for the operational planning of a broad range of energy supply chains is presented addressing an extensive range of conversion processes and technology, material and energy resources. An example problem of an energy supply chain network is used to illustrate the salient features and the applicability of the proposed Energy State-Task Network (ESTN) modeling approach and determine the optimal design and operational planning of the energy network.

This work presents a new optimization framework for the optimal operational planning of a large-scale power system taking into consideration the penetration of electric vehicles. This is a systematic approach in order to explore potential synergies among different energy sectors, i.e., the power and transport sectors. An illustrative real case study of the Greek power system is used to highlight the salient features and the applicability of the proposed modelling approach. The results determine the optimal production scheduling of the system integrating simultaneously the impacts of electric vehicles’ penetration into the grid.

This Special issue deals with the Energy Systems Engineering as a part Computer Aided Process Engineering. In this editorial introduction, the editors are highlighting the individual articles presented and discussed in this issue. Main areas of this issue can be summarised as follows: Process Integration including Total Sites, exergy, distributed energy production and consumption, energy production and generation control and energy related environmental impact.

This work presents new mixed-integer linear programming models for the deterministic single- and multi-mode resource constrained project scheduling problem with renewable and non-renewable resources. The modeling approach relies on the Resource-Task Network (RTN) representation, a network representation technique used in process scheduling problems, based on continuous time models. First, we propose new RTN-based network representation methods, and then we efficiently transform them into mathematical formulations including a set of constraints describing precedence relations and different types of resources. Finally, the applicability of the proposed formulations is illustrated using several example problems under the most commonly addressed objective, the makespan minimization.