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A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

This paper concerns a positive $\mathcal {L}_{1}$ observer for positive nonlinear semi-Markovian switching systems (MSSs) via the expansion of Taylor formula and the fuzzy Lyapunov function approach, in which semi-Markovian switching parameters, positivity, Takagi–Sugeno (T–S) fuzzy, and external disturbance are all considered in a unified framework. A fuzzy Lyapunov function approach with less conservativeness is introduced into the research of positive systems. In the system under consideration, positive S-MSSs with the semi-Markovian process can describe more complex systems in a practical control process. The main motivation of this paper is that the practical system subject to positivity and abrupt changes can be described by positive nonlinear S-MSSs, which always needs to consider the external disturbance. First, by using the normalized membership function approach, positive nonlinear S-MSSs can be represented by local positive T–S fuzzy S-MSSs. Second, by constructing the fuzzy Lyapunov function, some sufficient conditions are proposed for stochastic stability and $\mathcal {L}_{1}$ -gain performance analysis, respectively. Then, a positive $\mathcal {L}_{1}$ observer in a novel standard linear programming condition is designed to guarantee the resulting closed–loop augmented system is positive and stochastically stable with a required $\mathcal {L}_{1}$ -gain performance. Finally, a practical example about the epidemiological model is introduced to show the effectiveness of the main theory. [ABSTRACT FROM AUTHOR]

This paper deals with real-time scheduling of homogeneous multicore platforms powered by a battery to be periodically recharged. A system is composed of reconfigurable real-time dependent and periodic tasks to be assigned to different cores interconnected by a network-on-chip (NoC). The system is subject to reconfigurations, which are automatic operations allowing the addition and/or removal of tasks as well as their exchanged messages on the NoC. Consequently, any reconfiguration can violate real-time and energy constraints on cores as well as the NoC when the energy is unavailable until the next recharge. A novel periodic task model based on elastic coefficients and a new scheduling strategy are proposed to compute useful temporal parameters allowing for tasks and messages to meet the related constraints while controlling the communication cost on the NoC. This strategy is compared with an integer linear programming-based optimal solution, and a tool named OptimalMappingTasks is developed to run different simulations that prove the originality of this paper’s contribution. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method. [ABSTRACT FROM AUTHOR]

Optimal control system has been considered as a valuable technique to enhance the reliability and security of wastewater treatment process (WWTP). However, due to the complex environments, it is difficult to realize the optimal control with changeable operational conditions in WWTP. To overcome this problem, an intelligent optimal control system with flexible objective functions (IOCS-FOF) was proposed in this paper. The main merits of this proposed IOCS-FOF were shown as follows. First, an optimal control system scheme was developed, where different time-scale operational objectives were detailed to describe the operational features. Second, a flexible objective strategy was designed to determine suitable objective functions, which could adapt to complex environments of WWTP. Third, a novel dynamic multiobjective optimization algorithm for flexible objectives (FO-DMOA) was developed to obtain the optimal solutions of IOCS-FOF. Finally, the proposed IOCS-FOF was applied in the benchmark simulation model No. 1 (BSM1) to confirm its technique capability. The results demonstrate that this proposed method can improve the operation performance of WWTP. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost. [ABSTRACT FROM AUTHOR]

With the advancement in information technology, datasets with an enormous amount of data are available. The classification task on these datasets is more time- and memory-consuming as the number of data increases. The support vector machine (SVM), which is arguably the most popular classification technique, has disappointing performance in dealing with large datasets due to its constrained optimization problem. To deal with this challenge, the variant SVM (VSVM) has been utilized which has the fraction (1/2)b2 in its primal objective function, where b is the bias of the desired hyperplane. The VSVM has been solved with different optimization techniques in more time- and memory-efficient fashion. However, there is no guarantee that its optimal solution is the same as the standard SVM. In this paper, we introduce the generalized VSVM (GVSVM) which has the fraction (1/2)b2 in its primal objective function, for a fixed positive scalar t. Further, we present the thorough theoretical insights that indicate the optimal solution of the GVSVM tends to the optimal solution of the standard SVM as t → ∞. One vital corollary is to derive a closed-form formula to obtain the bias term in the standard SVM. Such a formula obviates the need of approximating it, which is the modus operandi to date. An efficient neural network is then proposed to solve the GVSVM dual problem, which is asymptotically stable in the sense of Lyapunov and converges globally exponentially to the exact solution of the GVSVM. The proposed neural network has less complexity in architecture and needs fewer computations in each iteration in comparison to the existing neural solutions. Experiments confirm the efficacy of the proposed recurrent neural network and the proximity of the GVSVM and the standard SVM solutions with more significant values of t. [ABSTRACT FROM AUTHOR]

This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem can be solved without the information exchange of coupled constraints. The convergence analysis of the proposed algorithm is presented with the convergence condition through which a diminishing step-size with an upper bound can be determined. The convergence rate can be achieved at $O({lnT}/{\sqrt {T}})$. Moreover, the equilibrium of this algorithm is proved to converge at the optimal solution of the global optimization problem. The effectiveness and the practicability of the proposed algorithm is demonstrated by the parameter optimization problem in smart building. [ABSTRACT FROM AUTHOR]

This paper investigates the resource allocation problem for a group of agents communicating over a strongly connected directed graph, where the total objective function of the problem is composted of the sum of the local objective functions incurred by the agents. With local convex sets, we first design a continuous-time projection algorithm over a strongly connected and weight-balanced directed graph. Our convergence analysis indicates that when the local objective functions are strongly convex, the output state of the projection algorithm could asymptotically converge to the optimal solution of the resource allocation problem. In particular, when the projection operation is not involved, we show the exponential convergence at the equilibrium point of the algorithm. Second, we propose an adaptive continuous-time gradient algorithm over a strongly connected and weight-unbalanced directed graph for the reduced case without local convex sets. In this case, we prove that the adaptive algorithm converges exponentially to the optimal solution of the considered problem, where the local objective functions and their gradients satisfy strong convexity and Lipachitz conditions, respectively. Numerical simulations illustrate the performance of our algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed adaptive optimization problem for nonlinear multiagent systems with external disturbances. The main goal is to optimize a global objective function by utilizing local and neighboring information while rejecting the external disturbance signals. Different from the existing results, the weight-balanced directed graph is considered and, by introducing the adaptive technique, the local objective functions are allowed only to be differentiable with locally Lipschitz gradients. Moreover, without requiring the system nonlinear functions to be globally Lipschitz, the global asymptotic convergence is obtained if the global objective function is strongly convex. Finally, simulation results are provided to verify the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance. [ABSTRACT FROM AUTHOR]

In machine learning and data mining applications, clustering is a critical task for knowledge discovery that attract attentions from large quantities of researchers. Generally, with the help of label information, supervised learning methods have more flexible structure and better result than unsupervised learning. However, supervised learning is infeasible for clustering task. In this paper, to fill the gap between clustering and supervised learning, the proposed clustering methods introduce the exemplars discriminative information into a supervised learning. To build the effective objective function, a strategy for reducing intracluster distance and increasing intercluster distance is introduced to form a unified optimization objective function. With initially setting the clustering centers, the data that near the centers are selected as exemplars to indicate the ground truth of different classes. Discriminative learning is then introduced to learn the partition hyperplane and classify all the data into different classes. New clustering centers are calculated for selecting new exemplars alternately. Using the proposed algorithms, the unsupervised $K$ -means clustering problem is effectively solved from the perceptive of optimization. Feature mapping is also introduced to improve the performance by reducing the intercluster distance. A novel framework for exploring discriminative information from unsupervised data is provided. The proposed algorithms outperform the state-of-the-art approaches on a wide range of benchmark datasets in terms of accuracy. [ABSTRACT FROM AUTHOR]

In the normal image thresholding methods based on two-dimensional histogram, the edge information of the regions is not maintained because of the local averaging activity used. Moreover, the computation time increases with the increase in the level of thresholds. This paper focusses on retaining more edge information by calculating the image entropy along the diagonal regions of the gray level co-occurrence matrix inspired from the partitioned design structure matrix, which is a novel idea. In addition, the key to our success is the theoretical investigation of a novel diagonal class entropy (DCE) concept that utilizes the minimum area for computation. The benefits of the proposed method are: 1) improved results; 2) efficient to preserve more precise shape of the edges; and 3) the computation time decreases with the increase in the threshold levels. The optimal thresholds are obtained by minimizing the DCE using coral reef optimization (CRO). A first hand fitness function for multilevel image thresholding is derived. The fight for space and the efficient reproduction characteristics of the CRO makes it attractive for this application. Benchmark images from the Berkley segmentation dataset are taken to experiment. Our results are compared with other state-of-the-art thresholding methods. The results obtained are encouraging and may set the path for further investigation in the domain of multilevel thresholding. [ABSTRACT FROM AUTHOR]

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one’s experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which ${K}$ -means is utilized to accelerate particles’ convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, ${K}$ -means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search. [ABSTRACT FROM AUTHOR]

It is generally accepted that the essential goal of many-objective optimization is the balance between convergence and diversity. For constrained many-objective optimization problems (CMaOPs), the feasibility of solutions should be considered as well. Then the real challenge of constrained many-objective optimization can be generalized to the balance among convergence, diversity, and feasibility. In this paper, a tri-goal evolution framework is proposed for CMaOPs. The proposed framework carefully designs two indicators for convergence and diversity, respectively, and converts the constraints into the third indicator for feasibility. Since the essential goal of constrained many-objective optimization is to balance convergence, diversity, and feasibility, the philosophy of the proposed framework matches the essential goal of constrained many-objective optimization well. Thus, it is natural to use the proposed framework to deal with CMaOPs. Further, the proposed framework is conceptually simple and easy to instantiate for constrained many-objective optimization. A variety of balance schemes and ranking methods can be used to achieve the balance among convergence, diversity and feasibility. Three typical instantiations of the proposed framework are then designed. Experimental results on a constrained many-objective optimization test suite show that the proposed framework is highly competitive with existing state-of-the-art constrained many-objective evolutionary algorithms for CMaOPs. [ABSTRACT FROM AUTHOR]

Recently, research in the field of network disintegration, which includes controlling the spread of disease and collapsing terrorist organizations, has found broad applications and attracted increased attention. In this paper, we focus on the network disintegration with heterogeneous cost, in which there may be unequal disintegration costs associated with deleting different nodes. First, we present a cost model for a disintegration strategy with both cost-sensitive and cost-constraint parameters in complex networks. Then, we propose an optimization model for the disintegration strategy with heterogeneous cost and introduce the genetic algorithm to identify the optimal disintegration strategy. Extensive experiments in synthetic and real-world networks suggest that the heterogeneity of the disintegration cost and the tightness of the cost constraint significantly affect the optimal disintegration strategy. We demonstrate that, in contrast to the classical hub node strategy, low-cost nodes play a key role in the optimal disintegration strategies if the cost constraint is tight and the disintegration cost is strongly heterogeneous. [ABSTRACT FROM AUTHOR]

This paper studies a class of distributed convex optimization problems by a set of agents in which each agent only has access to its own local convex objective function and the estimate of each agent is restricted to both coupling linear constraint and individual box constraints. Our focus is to devise a distributed primal-dual gradient algorithm for working out the problem over a sequence of time-varying general directed graphs. The communications among agents are assumed to be uniformly strongly connected. A column-stochastic mixing matrix and a fixed step-size are applied in the algorithm which exactly steers all the agents to asymptotically converge to a global optimal solution. Based on the standard strong convexity and the smoothness assumptions of the objective functions, we show that the distributed algorithm is capable of driving the whole network to geometrically converge to an optimal solution of the convex optimization problem only if the step-size does not exceed some upper bound. We also give an explicit analysis for the convergence rate of the proposed optimization algorithm. Simulations on economic dispatch problems and demand response problems in power systems are performed to illustrate the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Two of the most dominant challenges in post disaster emergency resource allocation are: 1) understanding the exact utility, i.e., exigency, of emergency resources and 2) collecting and transmitting the need for these resources to the control station from where resources are allocated. Measuring the utility of resources with precision becomes tricky in a dynamic post disaster scenario, where demands are constantly evolving and supplies trickle in at an uncertain rate. Moreover, collection and transmission of resource needs of far-flung areas are easier said than done owing to the post disaster disruption of communication infrastructure. These result in the ad-hoc allocation of emergency resources to the shelters. In this paper, we first derive a utility function for dynamically enumerating the shelter specific utility of each emergency resource. Subsequently, we propose an opportunistic knowledge sharing scheme for gathering and disseminating resource needs to the control station using a smartphone-based delay tolerant network. Finally, based on these opportunistically transmitted needs, we formulate a utility driven optimal resource allocation model which minimizes overall resource deficit and total resource deployment time. The proposed system optimally assigns constrained emergency resources to different shelters, so that high-utility resources are deployed fast. The effectiveness of the proposed system is evaluated using ONE simulator and LINGO optimization modeling tool. Exhaustive simulation is done to evaluate the comparative performance of our system with a number of competing schemes. Results show that our system outperforms all these schemes in a fully connected scenario. It is also observed that even in an intermittently connected environment, the performance of our system is almost at par with the competing schemes. [ABSTRACT FROM AUTHOR]

With the increase of transport demands, more pressure and challenges are being imparted into efficient transportation. As a conventional and direct congestion alleviation strategy, constructing new roads and lanes are increasingly restricted by limited land resources and high costs. Thus, making full use of existing transport network via appropriate management is critical to realize the sustainable development of transportation systems. As a flexible management strategy, lane reservation strategy has been widely adopted in real life. The reserved lanes can improve the efficiency of special transports, while they bring negative impact such as travel delay for general-purpose transports. In addition, the setting and operating of reserved lanes require a certain amount of cost. This paper proposes a new dual-objective integer linear programming model for optimally determining reserved lanes on a network for time-guaranteed special transports in order to simultaneously maximize the benefits and minimize the negative impact brought by reserved lanes, which incorporates road residual capacity and limited budget to the actual decision. Moreover, an iterative weighted sum-based method is proposed to solve it, in which a new relax-and-optimize algorithm is developed to exactly solve the single-objective optimization problems. Results of extensive numerical experiments show the effectiveness and efficiency of the proposed model and approach. [ABSTRACT FROM AUTHOR]

This paper addresses a new orthogonal packing and time scheduling problem—3D space-time optimization problem (3D-STO). Given a rectangular sheet and a set of rectangular items, each item needs to be continuously processed with a time length on the sheet, the 3D-STO consists in arranging each item’s loading time, location, and orientation for a certain period such that the total utilization time of the sheet, i.e., the makespan of the schedule, is minimized. The 3D-STO involves a series of 2D rectangular packing problem (2D-RPP) for the whole scheduling period. If the processing time of each item is simply regarded as a space dimension for the packing, then the 3D-STO can be reduced to an NP-hard packing problem—3D strip packing problem (3D-SPP). The 3D-STO differs from the 3D-SPP in that the position and orientation of items can be changed over the processing time (the third dimension) such that the 3D-STO has a much larger search space compared with the 3D-SPP. Hereby, the optimal solution of the 3D-STO is better than or equal to the optimal solution of the 3D-SPP, and a possibly better solution could be found for the 3D-STO model. As a new model, there is no algorithm or benchmark in the literature. Moreover, the 2D-RPP and 3D-SPP algorithms are not suitable for solving the 3D-STO which considers the packing and scheduling simultaneously. Hereby, we propose a caving-degree-based scheduling algorithm (CDS) for the 3D-STO. This is the first proposed 3D-STO algorithm. We also formalize the problem as a mixed integer programming model and solve it by ILOG CPLEX. For evaluation, we provide a synthesized method to generate a total of 195 various benchmark instances with guillotine and nonguillotine cut constraints. The comparative results show that CDS is more effective than the CPLEX solver for the 3D-STO. Also, when comparing CDS for the 3D-STO and the adapted CDS for the 3D-SPP, we see that the new 3D-STO model can enhance the flexibility of the item arrangement and make maximum utilization of the sheet and time. [ABSTRACT FROM AUTHOR]

Graph matching is a fundamental problem in theoretical computer science and artificial intelligence, and lays the foundation for many computer vision and machine learning tasks. Approximate algorithms are necessary for graph matching due to its NP-complete nature. Inspired by the usage in network-related tasks, random walk is generalized to graph matching as a type of approximate algorithm. However, it may be inappropriate for the previous random walk-based graph matching algorithms to utilize continuous techniques without considering the discrete property. In this paper, we propose a novel random walk-based graph matching algorithm by incorporating both continuous and discrete constraints in the optimization process. Specifically, after interpreting graph matching by random walk, the continuous constraints are directly embedded in the random walk constraint in each iteration. Further, both the assignment matrix (vector) and the pairwise similarity measure between graphs are iteratively updated according the discrete constraints, which automatically leads the continuous solution to the discrete domain. Comparisons on both synthetic and real-world data demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The spiral optimization (SPO) algorithm proposed by Tamura and Yasuda is a relatively novel and simple search concept inspired by natural spiral phenomena. This algorithm searches continuous space using no gradient and only spiral trajectories composed of spiral vectors generated by deterministic spiral models. The primary purpose of this paper is to propose conditions and settings that mathematically ensure the convergence of the SPO algorithm to a stationary point. The conditions relating to the sizes and directions of the spiral vectors and the initial search points are based on direct search theory and recent SPO algorithm theories. The presented convergence was numerically verified using test functions with different properties. [ABSTRACT FROM AUTHOR]

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product. [ABSTRACT FROM AUTHOR]

In many real-world domains, datasets with imbalanced class distributions occur frequently, which may confuse various machine learning tasks. Among all these tasks, learning classifiers from imbalanced datasets is an important topic. To perform this task well, it is crucial to train a distance metric which can accurately measure similarities between samples from imbalanced datasets. Unfortunately, existing distance metric methods, such as large margin nearest neighbor, information-theoretic metric learning, etc., care more about distances between samples and fail to take imbalanced class distributions into consideration. Traditional distance metrics have natural tendencies to favor the majority classes, which can more easily satisfy their objective function. Those important minority classes are always neglected during the construction process of distance metrics, which severely affects the decision system of most classifiers. Therefore, how to learn an appropriate distance metric which can deal with imbalanced datasets is of vital importance, but challenging. In order to solve this problem, this paper proposes a novel distance metric learning method named distance metric by balancing KL-divergence (DMBK). DMBK defines normalized divergences using KL-divergence to describe distinctions between different classes. Then it combines geometric mean with normalized divergences and separates samples from different classes simultaneously. This procedure separates all classes in a balanced way and avoids inaccurate similarities incurred by imbalanced class distributions. Various experiments on imbalanced datasets have verified the excellent performance of our novel method. [ABSTRACT FROM AUTHOR]

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents’ private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers’ coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making (GDM) context with interval attribute weight information. In GDM, the revision of the decision makers’ original attribute weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approach, with the mixed 0–1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context. [ABSTRACT FROM AUTHOR]

It is an important challenge to detect an overlapping community and its evolving tendency in a complex network. To our best knowledge, there is no such an overlapping community detection method that exhibits high normalized mutual information (NMI) and ${F}$ -score, and can also predict an overlapping community’s future considering node evolution, activeness, and multiscaling. This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment. First, according to a node’s dynamics such as link creation and destruction, we find node vitality by comparing consecutive network snapshots. Then, we combine it with the fitness function to obtain a new objective function. Next, by optimizing the objective function, we expand maximal cliques, reassign overlapping nodes, and find the overlapping community that matches not only the current network but also the future version of the network. Through experiments, we show that its NMI and ${F}$ -score exceed those of the state-of-the-art methods under diverse conditions of overlaps and connection densities. We also validate the effectiveness of node vitality for modeling a node’s evolution. Finally, we show how to detect an overlapping community in a real-world evolving network. [ABSTRACT FROM AUTHOR]

A neurodynamics-based algorithm is developed in this paper for solving multicoupled distributed optimization problems. In this formulation, each agent solves a local optimization problem with regard to its own cost function. The objective function is a sum of local convex subproblems, which are not necessarily strict convex and smooth. With communication between neighbors only, decision variables lie in a distributed manner and succeed to converge to the global optimum. The proposed method is suitable for solving large-scale problems in energy Internet management thanks to neural networks’ capability of real-time computation. To verify the algorithm, a power–heat–gas multienergy system model is constructed. The simulation results on a 5-energy-hub multienergy system are demonstrated to show the efficacy of the algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the identification problem of faulty behavior in a discrete event system, assuming that the fault-free model of a system is given in terms of Petri nets, where the set of transitions is divided into two disjoint subsets: 1) observable and 2) unobservable ones. The observed system output is defined as a transition-marking sequence, i.e., each transition is followed by a marking. First, a nonlinear integer programming model that characterizes the faults modeled by fault transitions is built according to the abnormal behavior extracted from the observed sequence. Then, it is converted into an integer linear programming (ILP) problem and a faulty net that preserves the structure of the fault-free one is obtained by solving this ILP model. In addition, an algorithm is developed to ensure acyclicity of the resulting unobservable subnet whose transition set is composed of the unobservable transitions of the fault-free net and the identified fault transitions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel decentralized on-line fault diagnosis approach based on the solution of some integer linear programming problems for discrete event systems in a Petri net framework. The decentralized architecture consists of a set of local sites communicating with a coordinator that decides whether the system behavior is normal or subject to some possible faults. To this aim, some results allow defining the rules applied by the coordinator and the local sites to provide the global diagnosis results. Moreover, two protocols for the detection and diagnosis of faults are proposed: they differ for the information exchanged between local sites and coordinator and the diagnostic capability. In addition, a sufficient and necessary condition under which the second presented protocol can successfully diagnose a fault in the decentralized architecture is proved. Finally, some examples are presented to show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a method for scheduling resources in complex systems that integrate humans with diverse hardware and software components, and for studying the impact of resource schedules on system characteristics. The method uses discrete-event simulation and integer linear programming, and relies on detailed models of the system’s processes, specifications of the capabilities of the system’s resources, and constraints on the operations of the system and its resources. As a case study, we examine processes involved in the operation of a hospital emergency department, studying the impact staffing policies have on such key quality measures as patient length of stay (LoS), number of handoffs, staff utilization levels, and cost. Our results suggest that physician and nurse utilization levels for clinical tasks of 70% result in a good balance between LoS and cost. Allowing shift lengths to vary and shifts to overlap increases scheduling flexibility. Clinical experts provided face validation of our results. Our approach improves on the state of the art by enabling using detailed resource and constraint specifications effectively to support analysis and decision making about complex processes in domains that currently rely largely on trial and error and other ad hoc methods. [ABSTRACT FROM AUTHOR]

Role assignment is a critical element in the role-based collaboration process. There are many constraints to be considered when undertaking this task. This paper formalizes the group role assignment problem when faced with the constraint of conflicting agents, verifies the benefits of solving the problem, proves that such a problem is a subproblem of the extended integer linear programming (x-ILP) problem, proposes a practical approach to the solution, and assures performance based on the results of experiments. The contributions of this paper include: 1) formalization of the proposed problem; 2) verification of the benefit achieved by avoiding conflicts in role assignment through simulation; 3) theoretical proof that conflict avoidance is a subproblem of the x-ILP problem that is nonpolynomial-complete; and 4) a practical solution based on the IBM ILOG CPLEX optimization package (ILOG) and verification of the scale of problems that can be solved with ILOG. The proposed approach is validated by simulation experiments. Its efficiency is verified by comparison with the previous exhaustive search-based approach. [ABSTRACT FROM PUBLISHER]

With a combinatorial nature and hybrid characteristics, it is very challenging to schedule crude oil operations in a refinery and, up to now, there is no software tool for practical use in this field. To solve this problem, based on the two-level architecture in our previous work, for cases where crude oil mixing is required, this paper investigates the refining scheduling problem at the upper level. Based on the schedulability conditions, a three-stage approach is proposed. First, a linear programming model is developed to maximize productivity. Second, another linear programming model is built and solved to optimally obtain the mixed crude oil types and their amounts, and assign them into the distillers. Finally, this paper simply sequences and merges the oil parcels obtained at the second stage to form a desired schedule. Because the proposed approach mainly solves two linear programming problems, it is very efficient. An industrial case study is presented to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

A fault tree (FT) defines an undesired top event, characterizing it using logic combinations of lower-level undesired events. In this paper, we focus on coherent FTs, i.e., the logic is restricted to AND/OR formulas. FT analysis is used to identify and assess the minimal cut sets (MCSs) of an FT, which define the minimal set of events leading to the undesired state. The dual of MCS is minimal path set (MPS). MCS and MPS are commonly used for qualitative evaluation of FTs in safety and reliability engineering. This paper explores computation of the MCS/MPS of an FT by means of structural analysis (namely, computation of minimal p -semiflows) of a Petri net (PN) that represents the FT. To this end, we propose a formal definition of a coherent FT and a transformation from this model to a PN subclass (namely, structurally persistent nets). We also prove the relationship between minimal p -semiflows and MCS/MPS in an FT. In addition, we propose an algorithm that uses linear programming techniques to compute the MCS/MPS in an FT. Finally, we put our findings into practice by qualitatively evaluating the FT of a pressure tank system. [ABSTRACT FROM PUBLISHER]

This paper tackles a large-scale and uncertain scheduling problem of semiconductor wafer fabrication system. Two kinds of uncertainties are considered here, including the machine breakdowns and the fluctuations of processing times. Instead of using the traditional rigid schedule, we propose a novel concept of operation-group-based soft schedule which is used as the new decision variable of the studied problem. The inherent idea of this concept is to make some critical scheduling decisions at the beginning of the scheduling horizon, and allow the remaining decisions to be made during the execution of the initial soft schedule. Furthermore, an operation-group-based soft scheduling approach (OGSSA) is proposed to deal with the uncertainties, which contains an offline optimization layer and an online dispatching layer. In the offline optimization layer, a prediction-based decomposition scheduling method is proposed to generate a soft schedule, and a global scheduling objective prediction model is constructed to evaluate the soft schedule. Then, the initial soft schedule is released to the online dispatching layer, and an online heuristic rule is designed to decide in real time, which operations and when to process. The computational results on the practical production data demonstrate the effectiveness of OGSSA under uncertain production environments. [ABSTRACT FROM AUTHOR]

In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A . This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. [ABSTRACT FROM AUTHOR]

The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants’ tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms. [ABSTRACT FROM PUBLISHER]

So far, the majority of deadlock control policies for automated manufacturing systems (AMSs) are based on the assumption that no resource fails; while for AMSs with unreliable resources, the main concerns are deadlock avoidance problems. This paper focuses on the robust deadlock prevention problem for AMSs with a type of unreliable resources, and assumes that at most one of unreliable resources fails at a time. Petri net is introduced to model the considered AMS. Deadlock can be characterized in terms of maximal perfect resource transition-circuits (MPRT-circuits). To develop robust Petri net deadlock controllers with small structures for the system, a new concept of strong transition covers is presented, which is a special kind of transition covers. By designing a control place with a proper control variable to each MPRT-circuit in the strong transition cover, a 1-robust Petri net controller is obtained, whereas the control variables can be determined by an integer linear programming. Such a 1-robust controller ensures that the system can process all types of parts infinitely even if one of unreliable resources fails. Since the number of MPRT-circuits in a strong transition cover is much less than that of all MPRT-circuits, our Petri net controller is of small structural size. Each AMS with a type of unreliable resources has at least a transition cover. An algorithm is presented for checking the strongness of transition covers, and transforming weak transition covers into strong ones. Finally, some examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Interval fuzzy preference relations (IFPRs) that can simply denote the lower and upper bounds of decision makers’ uncertain judgments are keenly studied by researchers and are widely used in practical decision-making problems. Consistency analysis is vitally important to avoid illogical ranking orders. First, this paper analyzes issues in previous additive consistency concepts for IFPRs. Then, it presents a new additive consistency definition that overcomes issues in previous ones. A linear programming model to judge the additive consistency of IFPRs is constructed, and a method to derive additive consistent IFPRs is proposed. Furthermore, a goal programming model to determine missing values in an incomplete IFPR is built, which have the highest consistent level with respect to known values. Regarding group decision making, a group consensus index is defined to measure the consensus of individual IFPRs, and an approach to improve the consensus level is introduced. Finally, a method for group decision making with IFPRs is developed, which can address incomplete and inconsistent cases. Associated examples are given to show the efficiency and feasibility of the developed theoretical results and comparative analysis is made. [ABSTRACT FROM PUBLISHER]

In this paper, distributed optimization is addressed based on a continuous-time multiagent system in the presence of time-varying communication delays. First, the relationship between optimal solutions and the equilibrium points of the multiagent system with time delay is revealed. Next, delay-dependent and delay-independent sufficient conditions in form of linear matrix inequality are derived for ascertaining convergence to optimal solutions, in the cases of slow-varying delay and fast-varying delay. Furthermore, a set of conditions are also obtained for the delay-free case. In addition, a sampled-data communication scheme is presented based on the conditions for the fast varying delay systems. Simulation results are presented to substantiate the theoretical results. An application for distributed parameter estimation is also given. [ABSTRACT FROM PUBLISHER]

Sparse representation-based methods have been successfully applied to visual tracking. However, complex and inefficient optimization limits their deployment in practical tracking scenarios. In this paper, we propose a temporal consistency dictionary learning tracking algorithm to enable efficient dictionary learning and tracking executive. First, we present an objective function which introduces the fixed dictionary and variance dictionary to reconstruct the object’s appearance. In particular, the proposed method takes the temporal consistency into account by adding a regularization term into the objective function to constrain the difference of object appearance at adjacent frames. Then the optimization problem is solved in an iteration way. Moreover, the proposed method can encode the object’s local structural information, and the local patches from the same candidate altogether for a global appearance representation. Second, we develop an effective observation likelihood function based on the proposed model. It takes the influence of patches with large reconstruction errors into consideration, thereby, alleviating the drifting of the object. Finally, we present an appearance updating strategy to adapt to the object’s appearance variations by the online dictionary learning. Experimental evaluations on the TB50 and TB100 datasets show that the proposed tracking method outperforms sparse representation related visual tracking as well as other state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the use of simulation optimization to handle collaborative design problems where more than one decision-maker is involved. We assume that the designers cannot enumerate all their considerations in closed-form, precise mathematical expressions but they can examine the merits of solutions with respect to their preferences and can compare candidate solutions with one another. We propose a three-stage framework to take the decision-makers’ such considerations into account. The first step is to obtain a diverse set of designs that can all be considered efficient in terms of a performance metric (i.e., the objective function values of the simulation optimization model). These solutions are then passed on to the decision-makers to be analyzed in terms of their preferences that could not have been previously considered. Finally, the most appropriate solution is chosen. We address the problem encountered in the first step as a multimodal optimization problem. We address the second and the third subproblems as a preference aggregation problem in the social choice theory. We also illustrate the effectiveness of the proposed approach through a supply chain design problem inspired from the literature. We use the crowding clustering genetic algorithm as an example to demonstrate the first step. We use a multiplicative variant of the popular analytic hierarchy process to illustrate how the second and the third steps can be handled. [ABSTRACT FROM PUBLISHER]

Dimension reduction is frequently adopted as a data preprocessing technique to facilitate data visualization, interpretation, and classification. Traditional dimension reduction methods such as linear discriminant analysis focus on maximizing the overall discrimination between all classes, which may be easily affected by outliers. To overcome this disadvantage, this paper proposes a novel method for multiclass dimension reduction, named dimension reduction by minimum error minimax probability machine (DR-MEMPM). It elaborately ensures that each pair of classes is well separated in the projected subspace by utilizing the separation probability between different pairwise classes. Therefore, it can put more emphasis on those less distinguishable classes, and the learned projection will not be dominated by some “outlier” classes which lie far away from other classes. We evaluate the proposed DR-MEMPM on a number of synthetic and real-world data sets, and show that it outperforms other state-of-the-art dimension reduction methods in terms of visual intuition and classification accuracy, especially when the distances between classes are unevenly distributed. [ABSTRACT FROM AUTHOR]

The ship stowage planning problem (SSPP) is a very complex and challenging problem in the logistics industries because it affects the benefits of both shipping lines and port terminals. In this paper, we investigate a multiobjective SSPP, which aims to optimize the ship stability and the number of rehandles simultaneously. We use metacentric height, list value, and trim value to measure the ship stability. Meanwhile, the number of rehandles is the sum of rehandles by yard cranes and quay cranes and all necessary rehandles at future ports. To solve this problem, a variant of the nondominated sorting genetic algorithm III (NSGA-III) combined with a local search component is proposed. The algorithm can produce a set of nondominated solutions. Decision makers can then choose the most promising solution for practical implementation based on their experience and preferences. Extensive experiments are carried out on two groups of instances. The computational results demonstrate the effectiveness of the proposed algorithm compared to the NSGA-II and random weighted genetic algorithms, especially when it is applied in solving the six-objective SSPP. [ABSTRACT FROM PUBLISHER]

This paper studies a lane reservation problem for large sport events in big cities. Such events require organizers to deliver certain people and materials from athlete villages to geographically dispersed venues within a given travel duration. A lane reservation strategy is usually adopted in this circumstance to ensure that time-critical transportation tasks can be completed despite heavy urban traffic congestion. However, it causes negative impact on normal traffic. The problem aims to optimally select and reserve some lanes in a transportation network for the exclusive use of the tasks such that the total traffic impact is minimized. To solve the problem, we first develop an improved integer linear program. Then, its properties are analyzed and used to reduce the search space for its optimal solutions. Finally, we develop a fast and effective quantum-inspired evolutionary algorithm for large-size problems. Computational results on instances with up to 500 nodes in the network and 50 tasks show that the proposed algorithm is efficient in yielding high-quality solutions within a relatively short time. [ABSTRACT FROM PUBLISHER]

Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transition’s firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach. [ABSTRACT FROM PUBLISHER]