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While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. An instant IoT network is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.

This paper studies when stochastic choices are consistent with behavior from Bayesian expected utility maximization and information acquisition. This is a limited dataset since other characterization require state-dependent stochastic choice data. The conditions that characterize this behavior are similar to the Wald-Bernheim-Pearce lemma and essentially requires that there is no random deviation rule that improves ex-ante expected utility for all possible information structures consistent with the observed stochastic choices. I extend the result to place bounds on an unknown prior and facilitate welfare comparisons in the presence of framing. These bounds are computable via linear programming.

Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the ``operator-as-a-consumer (OaaC),'' is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users' ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

U radu je opisana svrha modeliranja energetskog sustava i podjela modela za planiranje s obzirom na različitosti pristupa i metodologije. Pojava modernih računala i razvoj računalnih programa pojednostavnila je korištenje modela za planiranje. Ovakvi modeli koriste snažne matematičke algoritme i baze podataka koji u relativno kratkom vremenu mogu riješiti vrlo složene probleme. To je dalje omogućilo nastanak tzv. E3 modela (engl. energy-ecology-economy) koji imaju mogućnost istodobnog sagledavanja pitanja vezanih za energetiku, ali i za ekologiju i ekonomiju. Posebno su prikazane karakteristike modela MARKAL kroz primjer integriranog povezivanja s drugim modelima za planiranje. Pokazuje se da je primjena optimizacijskog modela MARKAL za planiranje energetskog sustava Republike Hrvatske od velikog značenja s obzirom na potrebne analize energetskog tržišta jugoistočne Europe, korištenja obnovljivih izvora energije, energetsku učinkovitost i trgovine emisijama., This article describes the purpose of energy system modeling and the classification of planning models according to approaches and methodologies. The advent of modern computers and computer programs has simplified the use of planning models. Such models employ powerful mathematical algorithms and databases which can solve highly complex problems in a relatively short time. This has led to energy-ecology-economy (E3) models, which are simultaneously able to consider questions in connection with energy supply, ecology and economics. The characteristics of a MARKAL model are presented separately through an example of integration with other planning models. It is demonstrated that the application of an optimizing MARKAL model for the planning of the energy supply system of the Republic of Croatia is of great significance for the analysis of the energy market of South East Europe, the use of renewable energy sources, energy efficiency and emission trading

One-level optimization methods have been proposed to optimize a single user’s load profile or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one case considering technical requirements (case 1) and another considering economic criterion (case 2). In the upper level, the supplier optimizes the objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on Genetic Algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulation results illustrate that both cases improve the demand profile and increase the retailer profit concerning an unscheduled case. However, when the supplier tries to maximize the profit, some users receive benefits to detriment of others, concluding that the technical approach is preferable to the economic one.

In this paper we present a simple technique to derive certificates of non-realizability for a combinatorial polytope. Our approach uses a variant of the classical algebraic certificates introduced by Bokowski and Sturmfels (1989), the final polynomials. More specifically we reduce the problem of finding a realization to that of finding a positive point in a variety and try to find a polynomial with positive coefficients in the generating ideal (a positive polynomial), showing that such point does not exist. Many, if not most, of the techniques for proving non-realizability developed in the last three decades can be seen as following this framework, using more or less elaborate ways of constructing such positive polynomials. Our proposal is more straightforward as we simply use linear programming to exhaustively search for such positive polynomials in the ideal restricted to some linear subspace. Somewhat surprisingly, this elementary strategy yields results that are competitive with more elaborate alternatives, and allows us to derive new examples of non-realizable combinatorial polytopes Gouveia was partially supported by the Centre for Mathematics of the University of Coimbra - UIDB/00324/2020 , funded by the Portuguese Government through FCT/MCTES . Macchia was supported by the Einstein Foundation Berlin under Francisco Santos grant EVF-2015-230 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 454595616 . Wiebe was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) [ PDF - 557980 - 2021 ], and by the Pacific Institute for the Mathematical Sciences (PIMS). The research and findings may not reflect those of the Institute.

This paper presents a practical optimum design solution of prismatic simply supported prestressed concrete girders that are subjected to uniformly distributed static loads. While assuming linear elastic behaviour and employing working stresses method, the optimum values of two design variables, namely the cross-sectional dimensions and the prestressing force, are formulated as a nonlinear optimization model. Optimum values of the design variables are computed to satisfy either a minimum weight or a minimum cost objective function. To solve the nonlinear programming models, a program named Non-Linear Programming Prestressed Concrete Optimizer (NLPRECO) was coded in MATLAB® programming environment. Some illustrative design problems were solved using NLPRECO and their results are demonstrated herein. Sensitivity analysis of the optimized design variables, to variations in design parameters, was also investigated and the results are presented. Results show substantial improvement over the regular un-optimized solutions and moderate improvement over a linear programming optimization solutions.

Acquisition functions for surrogate-assisted many-objective optimization require a delicate balance between convergence and diversity. However, the conflicting nature between many objectives may lead to an imbalance between exploration and exploitation, resulting in a low efficiency in search for a set of optimal solutions that can well balance convergence and diversity. To meet this challenge, we propose an adaptive model management strategy assisted by two sets of reference vectors, one set of adaptive reference vectors accounting for convergence while the other set of fixed reference vectors for diversity. Specifically, we first propose a new acquisition function that calculates an amplified upper confidence bound (AUCB). Two optimization processes are performed in parallel to optimize the acquisition function, each based on one of the two sets of reference vectors. Then, we select one promising candidate solution according to diversity or convergence from the nondominated solutions obtained by the two optimization processes. The experimental results on four suites of test functions as well as six real-world application problems demonstrate the competitive performance of the proposed reference vector-assisted adaptive model management strategy, in comparison with seven state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs).

Given the considerable complexity of process plants, there has been a great deal of research focused on aiding the design of plant layout through mathematical optimisation, i.e. optimising the positioning of the equipment in the plant for space and cost efficiency. Recently, the use of modular approaches within the construction industry, whereby work is performed off-site before being assembled on-site, has become a popular and powerful way of reducing build schedules and costs. Modular approaches have many other real applications where items must be packed to minimise the connections between them (e.g. piping, wiring, modular office and factory layouts) and consider the modular layout of the system. In this paper, we provide a formulation of the problem that, in addition to the standard layout problem, considers a modular block layout to allow modular construction and transportation of the plant. The problem is represented as a directed network, with the aim to pack the items into predefined containers and minimise the rectilinear distance between the connected items. We propose mixed-integer linear programming (MILP) models for the 2-dimensional and 3-dimensional problems and solve them using the state-of-the-art mathematical programming solver, Gurobi. Because of the combinatorial nature of the problem, solutions involving a large number of items may not converge and a suboptimal solution must be considered. However, our results suggest that even in the case of optimising a large number of items, the suboptimal solutions found after a reasonable number of iterations where deemed, by a domain expert, to be a good enough starting point to continue the design process, especially in the early concept phase.

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top-k partial loss and convex top-k partial hinge loss. Based on the losses, we present a novel top-k partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches.

Recent technological breakthroughs have allowed unmanned aerial vehicles (UAVs) to be utilized in a broad range of new operations. Among these various applications, herein, we focus on the use of UAVs for search and rescue missions in emergency and postdisaster scenarios. In this context, self-charging technologies for drones create new challenges in the routing of UAVs with charging stations. We present a variant of the vehicle routing problem (VRP) to address the integrated use of UAVs and mobile charging stations and define the VRP with synchronized networks (VRPSN), a new class of VRPs involving the routing of UAVs whose recharge platforms can travel to different locations during an operation. This leads to two networks within the VRP that must be integrated and synchronized. This research develops a mixed-integer linear program model for the VRPSN that considers the use of UAVs and mobile charging stations in a synchronized manner. To overcome the computational limits of the MILP model, this research presents a construct-and-adjust heuristic method integrated with a genetic algorithm. As a numerical example, we test the proposed model on the Corrego do Feijao Mine located in Minas Gerais, Brazil, where a dam recently collapsed, killing many workers. Numerical tests show that the new methodology is an attractive planning method for providing efficient and rapid responses in search and rescue missions.

This work focuses on the stability analysis of positive Takagi-Sugeno (T-S) fuzzy descriptor systems with time-varying delays. An equivalent augmented system is constructed to investigate the positivity and stability of T-S fuzzy descriptor time-delay systems. By using this transformed system, a necessary and sufficient positivity condition is first derived for systems, which can be verified by linear programming (LP). Then, based on the positivity of T-S fuzzy descriptor systems, a sufficient condition is put forward for the asymptotic stability of systems with bounded and unbounded time-varying delays. Finally, several examples are provided to show the effectiveness of the obtained results.

Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches.

Whereas interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension. This is in contrast with the simplex method that always admits an exponential bound. We introduce a new polynomial-time path-following interior point method where the number of iterations also admits a combinatorial upper bound $O(2^{n}n^{15}\log n)$ for an n-variable linear program in standard form. This complements previous work by Allamigeon, Benchimol, Gaubert, and Joswig (SIAGA 2018) that exhibited a family of instances where any path-following method must take exponentially many iterations. The number of iterations of our algorithm is at most $O(n^{15}\log n)$ times the number of segments of any piecewise linear curve in the wide neighborhood of the central path. In particular, it matches the number of iterations of any path following interior point method up to this polynomial factor. The overall exponential upper bound derives from studying the max central path’, a piecewise-linear curve with the number of pieces bounded by the total length of 2n shadow vertex simplex paths. From the existence of a line segment in the wide neighborhood we derive strong implications on the structure of the corresponding segment of the central path. Our algorithm is able to detect this structure from the local geometry at the current iterate, and constructs a step direction that descends along this segment. The bound $O(n^{15}\log n)$ that applies for arbitrarily long line segments is derived from a combinatorial progress measure. Our algorithm falls into the family of layered least squares interior point methods introduced by Vavasis and Ye (Math. Prog. 1996). In contrast to previous layered least squares methods that partition the kernel of the constraint matrix into coordinate subspaces, our method creates layers based on a general subspace providing more flexibility. Our result also implies the same bound on the number of iterations of the trust region interior point method by Lan, Monteiro, and Tsuchiya (SIOPT 2009).

International audience; The inventory routing problem (IRP) arises in a broad spectrum of real-life applications related to joint decisions of inventory and routing. In the basic IRP, a supplier has to make decisions about the delivery timing, delivered quantity of a single product and routing with a single vehicle to a set of retailers without backlog. It poses computational challenge due to its natural complexity. To tackle this problem, we propose a two-phase decomposition-based heuristic method. In Phase 1, a logic-based Benders like decomposition method is employed to first determine the retailers' replenishments, followed by the routing decisions individually for each period. Valid cuts, inequalities for diversification constraints and for greedy search are employed. Then, the solutions obtained in Phase 1 are improved with a restricted mixed integer linear programming (MILP) model in Phase 2. Computational experiments are conducted on 220 benchmark problem instances with up to 200 retailers and 6 periods. The results show the high performance of the proposed method and it is comparable to the state-of-the-art heuristics in terms of both efficiency and effectiveness.

This work proposes an optimal pricing scheme for provisioning sensors-as-a-service (Se-aaS) for catering to applications with multi-tenancy requirements in a sensor-cloud platform. The scheme orchestrates a trade-off analysis between communication range and price in a sensor-cloud platform with range-reconfigurable nodes. The proposed scheme consists of two phases -- (a) selection of a neighbor node of a source node, and determination of optimal price for the selected neighbor node. In the first phase, a source node adjusts its communication range and selects its best possible neighbor node using selectivity factor of all the neighbor nodes. In the second phase, we design a utility function to determine the optimal price of the selected neighbor node. We use the Lagrangian function to model the proposed problem as a mixed-integer linear program (MILP) and obtain the optimal solution using the Karush-Kuhn-Tucker (KKT) conditions. The existing works on pricing in sensor-cloud are deficient in considering the presence of reconfigurable communication range of sensor nodes. Extensive experimental results show that the proposed scheme performs better compared to the existing pricing schemes for sensor-cloud. In particular, the proposed scheme is capable of reducing the charged price by 10.55%, as compared to the existing pricing scheme, DOPH.

This paper proposes a method for enlarging the region of attraction of Linear Model Predictive Controllers (MPC) when tracking piecewise-constant references in the presence of pointwise-in-time constraints. It consists of an add-on unit, the Feasibility Governor (FG), that manipulates the reference command so as to ensure that the optimal control problem that underlies the MPC feedback law remains feasible. Offline polyhedral projection algorithms based on multi-objective linear programming are employed to compute the set of feasible states and reference commands. Online, the action of the FG is computed by solving a convex quadratic program. The closed-loop system is shown to satisfy constraints, be asymptotically stable, exhibit zero-offset tracking, and display finite-time convergence of the reference.

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU.

Reorganizing bus frequencies to cater for actual travel demands can signicantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specically, for the rst time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efciently, we rst present an index-based (1 1/e)-approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method for that achieves a (1)(1 1/e) approximation ratio. Then we propose a progressive partition-based greedy method for to further boost the efciency while achieving a (1)(1 1/e) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efciency, effectiveness, and scalability of our methods in addressing FAST and FASTCO.

Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels. This article addresses the runtime safety monitoring problem of dynamical systems embedded with neural-network components. A runtime safety state estimator in the form of an interval observer is developed to construct the lower bound and upper bound of system state trajectories in runtime. The developed runtime safety state estimator consists of two auxiliary neural networks derived from the neural network embedded in dynamical systems, and observer gains to ensure the positivity, namely, the ability of the estimator to bound the system state in runtime, and the convergence of the corresponding error dynamics. The design procedure is formulated in terms of a family of linear programming feasibility problems. The developed method is illustrated by a numerical example and is validated with evaluations on an adaptive cruise control system.

Power networks are an important infrastructure that requires close monitoring and recurring assessment during and following a catastrophic event. The drone’s unique aerial and unmanned nature has made it an efficient and powerful tool for damage assessment of the power networks. In this article, we propose a drone-routing framework to enable the systematic and automatic assessment of power networks considering wireless charging of the drone during the scanning. This article incorporates the resilience-oriented line priority index to periodically prioritize the power lines based on their specifications and conditions, and by doing so, this improves the efficiency of the assessment. A multiobjective mixed-integer linear programming model is developed to dynamically determine the drone’s optimal routing and speed in order to assure that the drone gathers sufficient data while completing the assessment mission in the shortest period of time. Since the proposed optimization algorithm needs to be solved multiple times for different sections of the power network, we propose a set of solution algorithms to reduce the computational burden and allow the proposed algorithm to reach the optimal solution quickly. Simulation results on a power network consisting of 77 nodes and 73 lines illustrate the effectiveness of the proposed approach in performing network-wide dynamic assessment using a drone.

In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

The work examines the problem of researching economic processes in conditions of uncertainty and conflict between parties participating in them. It is emphasized that, from the point of view of mathematics, such research is usually reduced to solving a discrete antagonistic game of two players. Examples of specific economic situations are given, which lead to the need to use mathematical models based on matrix games, including the production behavior of firms (enterprises) both at the level of the product and at the level of its production. Special attention is paid to the relationship between matrix games and linear programming problems. For the implementation of mathematical models of matrix games, an approach is proposed, the basis of which is the transition to a pair of dual problems of linear programming, the further solution of each of which is carried out in the environment of the table processor MS Excel using a special add-in Solver. The organization of data on the MS Excel worksheet is described in detail, all the necessary formulas and conditions for finding the optimal solution are given. The proposed approach is illustrated by an example of solving the problem of choosing optimal strategies for the production of various types of products by an enterprise in conditions of uncertain demand. The obtained results are in good agreement with the data of other authors obtained by analytical methods. At the same time, one of the most attractive features of the proposed approach is the ease of computer implementation, solving the problem in data manipulation mode without the need for programming. There is also no need to simplify the problem by discarding the subordinate strategies of the players. Solving dual problems of linear programming makes it possible to calculate the mixed strategies of two players and contributes to making a reasonable management decision among the set of alternative solutions. The considered approach can be used for any firms that are engaged in the production of products and wish to reduce risks in conditions of uncertain demand., У роботі розглядається проблема дослідження економічних процесів в умовах невизначеності та конфлікту між сторонами, що приймають в них участь. Наведені приклади конкретних ситуацій, які приводять до необхідності використання математичних моделей на основі матричних ігор. Запропонований підхід до комп’ютерної реалізації моделей, в основі якого лежить використання спеціальних інструментів табличного процесора MS Excel. Розв’язання задачі проводиться в режимі маніпулювання даними без необхідності програмування. Описаний підхід реалізований для випадку вибору підприємством оптимальних стратегій випуску різних видів продукції в умовах невизначеного попиту. Одержані результати добре узгоджуються з даними інших авторів.

Mass casualty incident (MCI) is an unpredictable situation where a great number of people have been injured after an accident or sudden disease. Survival of the injured in the MCI depends on the efficiency of the directed emergency system (DES). The organization and management of medical assistance is of paramount importance. The shortest possible time to provide medical services to injured persons is crucial. The medical service in the case of the MCI primarily requires decisions on the priority of the order of treatment of the injured, the choice of medical transport and the location of specialized emergency treatment. As part of this paper, the effectiveness of the DES has been analyzed, and criteria used to improve rescue operations have been formulated. A formalized mathematical description of the medical rescue operations in MCIs has been proposed, and the optimization problem as the mixed integer linear programming (MILP) task was formulated. Optimization of an example case of rescue operations in MCIs has been presented. A computer simulator for optimal decision-making in medical rescue operations (CSMRO) has been developed for this purpose. The CSMRO implements various multi-criteria optimization methods to solve the formulated problem of rescue operations optimization. The results of computations made with the developed CSMRO simulator significantly shorten the time of decision-making in mass casualty incident handling.

Heat pumps (HPs) are a promising option for decarbonizing the residential heating sector. Their use leads to an increased demand for electricity, which can burden the grid in uncontrolled operation, while controlled HPs can provide additional flexibility to the system. In this paper, the integration of HPs through a receding horizon approach into a power market model is presented to study the behavior of HPs considering electricity market prices and network constraints of a low-voltage grid. In a case study with centralized and self-consumption optimized control strategies, the effects of HPs are analyzed. Furthermore, the authors investigate how the aggregation of several households into energy communities affects the system. The results show that for the considered grid, the additional power demand of HPs is usually uncritical and a strictly cost-minimizing operation is often possible from a grid perspective. Typically, critical grid situations occur only at times of high PV feed-in with low HP flexibility. The formation of energy communities compared to individual households differs primarily in the allocation of cost savings to individual consumers. The difference in the impact of the two control strategies on the grid is negligible in this case., IET Smart Grid

This paper introduces the multiphase course timetabling problem and presents mathematical formulations and effective solution algorithms to solve it in a real case study. Consider a pool of lessons and a number of students who are required to take a subset of these lessons to graduate. Each lesson consists of a predetermined and consecutive sequence of phases with possibly different resource requirements and duration. Students who take a lesson must be present in certain phases of the lesson. Similarly, resources that are allocated to the lesson must be present in certain phases of the lesson depending on their roles. The objective is to maximize the weighted sum of student-lesson allocations subject to some capacity, availability and prerequisite constraints. The problem is formulated as an integer linear program called the session-index formulation. The formulation is then extended by introducing various side constraints that capture business requirements of a pilot training program. An enhanced branch-and-check algorithm is proposed to solve the problem more efficiently. Large instances of the problem are solved by utilizing an effective fix-and-optimize matheuristic. Results of a computational study on a set of real-world instances of the problem demonstrate the efficacy of the proposed exact and matheuristic algorithms. Due to the novelty and complexity of the problem, the efficacy of the proposed methodology to solve its real-world instances, and the implementation and realization of these contributions within a decision support system, this work was recognized as a semi-finalist of INFORMS Franz Edelman Award (2021).

This paper presents an optimization model to help distribution system planners to site and size battery energy storage systems (BESSs) and to site isolation devices. The purpose is twofold: (i) to enhance system reliability and (ii) to generate revenue by taking advantage of energy arbitrage. Our work fills a research gap regarding the optimal coordination of the siting of BESSs and isolation devices. Input data for the model includes utility historical load and fault data. This data is used to generate multiple scenarios pertaining to normal and faulted operating conditions. The proposed optimization model is recast as a mixed-integer linear programming (MILP) problem and solved using a commercial solver. It is successfully applied in a real-world case study.

Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification that can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods, both of which have their own benefits and drawbacks. This paper introduces two methods that exploit randomness to different degrees: the optimization-free Hybrid-Corner-Random (), and the direct-search method Line-search Falsification (). combines randomly chosen parameter values with extreme parameter values, which performs surprisingly well on benchmark evaluations. The gradient-free optimization-based LSF optimizes over line segments through a vector of inputs in the n-dimensional parameter space. The two methods are compared to the Nelder-Mead and SNOBFIT methods, using a well-known set of benchmark problems and shows better performance than any of the evaluated methods. Falsification can be done using random search methods or optimization methods. This paper proposes a method based on combining randomly chosen parameter values with parameter extreme values. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization-based methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search Falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification. .

Federated learning (FL) offers a promising paradigm that empowers numerous IoT devices to implement distributed learning on the premise of ensuring user privacy and data security. However, since FL adopts a synchronous distributed training mode, the heterogeneity of participating IoT devices and limited communication resources make FL encounter serious issues of low training efficiency in actual deployment. In this paper, we propose an excellent FL policy for the heterogeneous IoT-edge FL system to improve distributed training efficiency. Specifically, first of all, by borrowing the idea of clustering, we explore an iterative self-organizing data analysis techniques algorithm (ISODATA)-based heterogeneous-aware client scheduling strategy to alleviate the issue of low training efficiency incurred by the heterogeneity of clients. Subsequently, to tackle the challenge of limited communication resources in FL, we first analyze the characteristics of the optimal resource block allocation solution theoretically, and then introduce a mixed-integer linear programming (MILP)-based strategy to judiciously allocate resource blocks for scheduled clients. Comprehensive experimental results demonstrate that, compared with benchmarking strategies, our proposed FL policy can achieve up to 55.22% accuracy improvement in a relaxed time scenario, and attain up to 3.62× acceleration for reaching the specific expected accuracy.

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

Hottopixx, proposed by Bittorf et al. at NIPS 2012, is an algorithm for solving nonnegative matrix factorization (NMF) problems under the separability assumption. Separable NMFs have important applications, such as topic extraction from documents and unmixing of hyperspectral images. In such applications, the robustness of the algorithm to noise is the key to the success. Hottopixx has been shown to be robust to noise, and its robustness can be further enhanced through postprocessing. However, there is a drawback. Hottopixx and its postprocessing require us to estimate the noise level involved in the matrix we want to factorize before running, since they use it as part of the input data. The noise-level estimation is not an easy task. In this paper, we overcome this drawback. We present a refinement of Hottopixx and its postprocessing that runs without prior knowledge of the noise level. We show that the refinement has almost the same robustness to noise as the original algorithm., Comment: 32 pages, 2 figures

Interactions between different policy instruments in real markets can lead to a mismatch between stakeholders striving for individual benefits and the attainment of an overall system optimum. These distortion effects are of particular relevance for sector-integrated systems with high prosumer shares. Distributed battery storage systems, in particular, could constitute a pivotal source of flexibility in close to 100% renewable energy systems. However, depending on their operation mode, these battery systems can either serve individual profit maximization or the benefit of the system as a whole. We quantify the resulting differences by means of a fundamental linear optimization model of the electricity market, which we stringently link to a stakeholder's complete cost-of-energy analysis including relevant regulatory price components. We demonstrate that the effects encompass both system cost changes and distributional effects for certain stakeholder groups. We quantify the effects and apply an impact variation analysis to demonstrate that they could be relevant in view of the overall acceptance of the energy system transformation.

We develop phase limitations for the discrete-time Zames-Falb multipliers based on the separation theorem for Banach spaces. By contrast with their continuous-time counterparts they lead to numerically efficient results that can be computed either in closed form or via a linear program. The closed-form phase limitations are tight in the sense that we can construct multipliers that meet them with equality. We discuss numerical examples where the limitations are stronger than others in the literature. The numerical results complement searches for multipliers in the literature; they allow us to show, by construction, that the set of plants for which a suitable Zames-Falb multiplier exists is non-convex.

This paper presents a novel optimization formulation to solve the problem of variable speed limit control on road networks modeled by the Lighthill-Whitham-Richards (LWR) partial differential equation. It also presents some mathematical rules that allow for a reduction in the size and computational time of the optimization problem. Using the analytical solutions to the LWR model, an optimization problem is formulated for the variable speed limit and ramp metering control of traffic on highway networks using the Lax-Hopf algorithm. The resulting problem, which is non-linear in the decision variables, is transformed into a Mixed Integer Linear Program. An example is presented to show the effectiveness of the approach, including its application to a real-world highway network with multiple ramp connections. The possibility of linear relaxation of integer variables in the problem is also considered. Lastly, the method is compared to a classical Link Transmission Model formulation of the variable speed limit control problem.

An Autonomous Vehicle (AV) is believed to be the next generation transport that can enhance safety and efficiency for smart mobility. In an AV-based public transportation system, the full autonomy of AVs enables high-efficiency transport services and the potential ride-sharing feature of AV system enhances the utilization. The system manages a fleet of AVs, determines their assignments to transport requests, sends instructions concerning the optimized plans, and recommends the parking locations for the unoccupied AVs. The parking location of an empty AV is crucial in the sense that rebalancing AVs to areas with high potential service demand can curtail the unnecessary waiting time for passengers. As AVs are generally electric, proper parking locations can also facilitate vehicle-to-grid (V2G) support. In this paper, we propose a joint rebalancing and V2G coordination strategy for AV-based public transportation system. We formulate the problem as an integer linear program and propose a heuristic based on Genetic Algorithm and Model Predictive Control to solve the problem in low time complexity. Extensive experiments are performed with the real taxi service data from New York City. The formulated integer linear program is solved dynamically where each problem instance contains 3 to 5 AVs and 3 to 8 requests in 30s time interval. Compared with the transport system without rebalancing, the results show that the coordination strategy is efficient and effective in reducing unnecessary waiting time for passengers while satisfying V2G support. Compared to computational time with the standard solver, the proposed heuristic dramatically reduces the computational time.

In complex systems that can be found in semiconductor manufacturing, linear programming production planning models must consider many products with hundreds of production steps to be performed on hundreds of machines. To deal with this complexity and solve problems with flexible lead times in a reasonable CPU time, the new concept of timed route is introduced. In a timed route, each production step of a product is associated with a specific time period. A new formulation relying on timed routes is then proposed. Because the number of feasible timed routes can grow exponentially, a column generation approach is presented. Algorithms to generate relevant timed routes are given, and their complexity analyzed. Computational experiments on industrial data with different lead time profiles, fixed lead times and flexible lead times, show that computational times are very significantly reduced when using our approaches, by 92% on average and even divided by more than 1,000 in some cases. The advantages of timed routes are also discussed.

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an Integer Linear Programming (ILP) problem to take advantages of popular Mixed-Integer Linear Programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data pre-processing stage, then a solution can be obtained by using linear programming methods without much difficulty. The tightening process, however, is NP hard because of the existence of integer variables. In this paper, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data pre-processing stage. It is a major extension from our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by binary variables. With binary variables and continuous only, the vertices of the convex hull can be obtained based on the vertices of the linear problem after relaxing binary requirements with proved tightness. These vertices are then converted to tight constraints for general use. This approach significantly improves and extends our previous results on tightening single-operation parts without actually achieving tightness. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other ILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

The integrated process planning and scheduling (IPPS) problem is studied in this article, in which operation sequencing, process plan selection, and machine selection are decided simultaneously. For different scenarios, three mixed-integer linear programming (MILP) models are designed. Then, in view of the workload of machines and processing times of jobs, two machine selection techniques are introduced to simplify the optimization of these MILP models. By exploring the structural properties of the MILP models, we put forward a novel lower bound to act as a measurement for the performance of the related algorithms. Considering the real-time requirement and complexity of instances in practice, we design a hybrid greedy heuristic based on a new decision structure of the problem and dispatching rules. Furthermore, in order to create effective dispatching rules to improve the hybrid greedy heuristic, enhanced genetic programming (GP)-based iterative approach is proposed. Experimental results indicate that our approaches are better than other available approaches for the IPPS problem and can reduce the computational time while providing high-quality solutions.

Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in intermittent on-off behaviour. Traditional computing schedulers can not handle this intermittency, and in this paper we propose a first step towards an energy-aware task scheduler for constrained batteryless devices. We present a new energy-aware task scheduling algorithm that is able to optimally schedule application tasks to avoid power failures, and that will allow us to provide insights on the optimal look-ahead time for energy prediction. Our insights can be used as a basis for practical energy-aware scheduling and energy availability prediction algorithms. We formulate the scheduling problem as a Mixed Integer Linear Program. We evaluate its performance improvement when comparing it with state-of-the-art schedulers for batteryless IoT devices. Our results show that making the task scheduler energy aware avoids power failures and allows more tasks to successfully execute. Moreover, we conclude that a relatively short look-ahead energy prediction time of 8 future task executions is enough to achieve optimality.

An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues.

In this paper, we present the results of a new approach to the splitting factor preconditioner, which is a preconditioner based on the Incomplete Cholesky factorization and the splitting preconditioner. In previous work for small linear programming problems, the preconditioner was applied in all iterations of the interior point method and compared with the splitting preconditioner also applied in all iterations. In this paper, we will do a hybrid approach, in which in the first iterations the preconditioner is the Incomplete Cholesky Factorization, and in the last iterations, the preconditioner used is the splitting factor preconditioner or the splitting preconditioner. The results obtained show that even in the hybrid approach, the splitting factor preconditioner achieved better performance.

In this work, we propose two lightweight access point (AP) localization and selection schemes, AP-Cov and IAP-Cov, in IEEE 802.11-enabled IoT networks for reducing handover delay and maintaining uninterrupted connectivity during movement. The proposed scheme AP-Cov consists of two phases – localization of APs and a priori selection of APs in the direction of mobility. In the localization phase, an IoT device exploits the features of Neighbor Report Request (NRR) and Beacon Report Request (BRR) of IEEE 802.11k to get the nearby APs’ informations along with their location and uses simple algebra to compute the location of next APs beforehand, instead of using heavyweight processing in resource-constrained IoT networks. In the AP selection phase, a linear optimization model is formulated to select the optimal AP for subsequent association. In IAP-Cov, a broadcast sequence of neighbor IoTs is proposed for reducing the number of broadcasts. We implement the schemes in NS-3 and compare performance with the baseline standard, which shows that AP-Cov detects the availability of APs with 98.625%, 96.5% accuracy, improves accuracy over message complexity by 23%, 78% and reduces handover by 36.37%, 67.52% in constant velocity and random velocity mobility model, respectively. The evaluation of computational complexity shows that the proposed schemes are lightweight too. IAP-Cov reduces broadcasts and delay by 49%, 77.36% and 6%, 25% compared to AP-Cov using the constant and random velocity mobility models, respectively.

This article presents a new model to handle the cast break problem caused by small daily disruptions in the processing time of the steelmaking and continuous casting (SCC) production process. In this model, the exact distribution of the uncertain parameters is unknown, and support set, mean, and covariance information is used to describe the uncertain processing time. The problem aims to determine the assignments, sequences, and time points of the charges to be processed on corresponding machines. The main goal is to minimize the expected value of the production objective while reducing the number of cast break occurrences. The problem is solved in two steps. First, a subproblem is developed by fixing the sequences and the assignments of the charges. This subproblem is formulated as a distributionally robust chance-constrained (DRCC) model, in which the constraints are established with certain probabilities even when the uncertain processing times are in their worst cases. A dual approximation method is proposed to convert the model into a semidefinite programming problem so that it can be solved by standard solvers. Additionally, a linear programming approximation method is used to accelerate the solving procedure. A Tabu search algorithm incorporated with a speed-up strategy is also designed to determine the assignments and sequences of the charges. Both simulated data generated from different distributions and actual production data are used to test the efficacy of our model. Results of the numerical experiments show that the schedule obtained from the DRCC model is more robust, i.e., it causes fewer cast breaks than the nominal schedule obtained from a deterministic model.

Best-worst method (BWM) has attracted increasing attention. It has been generalized to different fuzzy environments and applied to various real-life decision problems. This paper develops a new intuitionistic fuzzy (IF) best-worst method (IFBWM) for multi-criteria decision-making (MCDM). When a decision maker (DM) makes comparisons, there may be some hesitancies. Thus, the reference comparisons are represented as intuitionistic fuzzy values (IFVs), the Best-to-Others vector and the Others-to-Worst vector are IF vectors. According to the multiplicative consistency of intuitionistic fuzzy preference relation, this paper gives the consistency equations and views them as IF equations. The derivation of optimal IF weights of criteria is formulated as an IF decision-making problem. Thereby, a mathematical programming model is constructed to assure that the derived optimal IF weights of criteria is a normalized IF weight vector. Depending on the risk preference of DM, four linear programming models are presented to obtain the optimal IF weights based on the constructed mathematical programming model for the optimistic DM, the pessimistic DM and the neutral DM, respectively. Furthermore, this paper investigates the process of improving the consistency. Several examples are demonstrated to show the application and effectiveness of the proposed IF BWM.

Convergence, diversity and feasibility are three important issues when solving constrained multiobjective optimization problems (CMOPs). To deal with the balance among convergence, diversity and feasibility well, this article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) for solving CMOPs. CMOEA-PP has two populations, including propulsive population and normal population, and these two populations work cooperatively. Specifically, propulsive population focuses on convergence. Normal population gives priority to feasibility and is obligated to maintain diversity. To cross through the infeasible region and reach the Pareto front (PF), propulsive population does not consider constraints in the early stage and only considers constraints in the later stage. To further accelerate the speed of convergence, propulsive population only searches for corner solutions and center solutions, while normal population searches for the whole PF. As a result, propulsive population can cross through the infeasible region because of the lack of attention to feasibility. In addition, propulsive population also can guide and accelerate the convergence of the evolutionary process. Comprehensive experiment results on several sets of benchmark problems demonstrate that CMOEA-PP is better than existing state-of-the-art competitors.

This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited channel resources. Since the effects of the time delay and switching parameters, traditional finite-time techniques cannot be directly utilized to the FTBS. By constructing a novel multiple Lyapunov functional (MLF), a sufficient criterion formulated by linear programming (LP) is established for the FTBS and the estimation of the settling time. To further improve the accuracy of the settling time, another MLF is designed by dividing the dwell time. With the aid of convex combination, a new LP is provided, which removes the requirement that the increment coefficient of the MLF at switching instants has to be larger than 1. In addition, to obtain the more precise settling time, an optimal algorithm is provided. Two numerical examples are put forward to demonstrate the reasonableness of the theoretical analysis.

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are taken into account by the proposed framework. The initial configurations of islands are formed just ahead of the storm event (first stage), and their borders are changed in the second stage, which is associated with the storm event and its aftermath. The final configurations of islands are determined by the third stage once the uncertainties of floodwater depths are revealed. In the proposed framework, the emergency generators (EGs) are assumed to be prone to flooding, and a novel approach is proposed for quantifying the flood-related failure probability of EGs. Likewise, overhead distribution structures are recognized as vulnerable components to storms. The proposed framework is implemented on a test system, and its effectiveness is investigated and verified through seven case studies.

Natural disasters and accidents have underscored the need for operative solutions that can enhance the resiliency of power distribution network (PDN). This article proposes novel methods of scheduling of battery energy storage system (BESS) and loads of microgrid to enhance the resiliency of PDN. A mixed-integer linear programming model is developed to minimize the energy mismatch between available resources and critical loads during outage period. The proposed model is aimed at minimizing the shedding of high critical loads (hcl) and maximizing the serving time of hcl during outages. In order to increase the longevity of the load serving, the fuzzy-based approach is developed to control the C_rate of the BESS. Moreover, to increase the resiliency of hcl, partitioning of BESS capacity is proposed, which provides more flexibility for charging/discharging control of the BESS. The concept of variable dispatch interval is introduced to reduce the frequent switching of loads. By considering the reactive power, an apparent power resiliency metric is proposed to quantify the resiliency of PDN. The IEEE 33-bus network is tested under various restoration periods in order to verify the performance of the proposed methodology in the MATLAB environment.