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In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case. [ABSTRACT FROM AUTHOR]

In his CAV 2004 paper, Tiwari has proved that, for a class of linear programs over the reals, termination is decidable. And Tiwari has shown that the termination of a linear program P 1 whose assignment matrix A ˜ is not in the Jordan canonical form is equivalent to that of a linear program J 1 , whose assignment matrix A is in the Jordan Canonical Form. In most cases, the method of Tiwari provides only a so-called N -nonterminating point. In this paper, we propose two new methods to decide whether Program P 1 terminates or not over the reals. Our methods are based on the construction of a subset of the set NT of non-terminating points of Program J 1 . Any point in such a subset is a witness to non-termination of Program J 1 . Furthermore, it is shown that Program J 1 is non-terminating if and only if such a subset is nonempty. In terms of the property, the first method is given to check whether Program J 1 terminates or not. Different from the existing methods, the point obtained by our first method is a non-terminating point, rather than a N -nonterminating point. More importantly, such a subset is also proven to be A B ˆ -invariant for some positive integer B ˆ . This enables us to check directly the termination of Program J 1 by verifying the satisfiability of finitely many quantified formulas over the reals. This suggests our second method for checking the termination of Program J 1 . [ABSTRACT FROM AUTHOR]

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a methodology for optimizing the operation of compressors in parallel in process industries. Compressors in parallel can be found in many applications for example in compressor stations conveying gas through long pipelines and in chemical plants in which compressors supply raw or processed materials to downstream processes. The current work presents an optimization framework for compressor stations which describe integration of a short term and a long term optimization approach. The short-term part of the framework suggests the best distribution of the load of the compressors (where the time scale is minutes) and the long-term optimization provides the scheduling of the compressors for large time periods (where the time scale is days). The paper focuses on the short-term optimization and presents a Real Time Optimization (RTO) framework which exploits process data in steady-state operation to develop regression models of compressors. An optimization model employs the updated steady-state models to estimate the best distribution of the load of the compressors to reduce power consumption and therefore operational costs. The paper demonstrates the application of the RTO to a network of parallel industrial multi-stage centrifugal compressors, part of a chemical process in BASF SE, Germany. The results from the RTO application showed a reduction in power consumption compared to operation with equal load split strategy. [ABSTRACT FROM AUTHOR]

Initial thickness imperfections of thin membranes due to manufacturing are inevitable and commonly observed. This paper presents an experimental and numerical investigation on how thickness imperfections affect the wrinkling of stretched membrane structures. With the measured data from a limited number of samples, we model the uncertain deviation of membrane thickness from that of a perfect geometry as a non-probabilistic bounded field with spatial dependency. After representing the bounded field as a linear function of a reduced set of uncertain coefficients by using a series expansion, the effect of thickness imperfections on the membrane wrinkling is investigated. To this end, the upper and lower bounds of out-of-plane displacements for membranes with initial imperfections are evaluated by a mathematical programming. The proposed wrinkling evaluation problem is then solved efficiently by using the Kriging-based optimization algorithm, with validation done by Monte Carlo simulation. The results show that initial thickness imperfections play a critical role in the wrinkling behavior of membranes. [ABSTRACT FROM AUTHOR]

The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL algorithms fail to enforce rigorous operational constraints (e.g., power balance, ramping up or down constraints) limiting their implementation in real systems. To overcome this, in this paper, a DRL algorithm (namely MIP-DQN) is proposed, capable of strictly enforcing all operational constraints in the action space, ensuring the feasibility of the defined schedule in real-time operation. This is done by leveraging recent optimization advances for deep neural networks (DNNs) that allow their representation as a MIP formulation, enabling further consideration of any action space constraints. Comprehensive numerical simulations show that the proposed algorithm outperforms existing state-of-the-art DRL algorithms, obtaining a lower error when compared with the optimal global solution (upper boundary) obtained after solving a mathematical programming formulation with perfect forecast information; while strictly enforcing all operational constraints (even in unseen test days). • A deep reinforcement learning (DRL) algorithm is proposed for optimal energy systems scheduling. • The proposed DRL algorithm can strictly enforce all operational constraints in the action space. • A deployment procedure is introduced that exploit optimization theory for deep learning models. • The proposed DRL algorithm is compared with other models, such as DDPG, PPO, TD3. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of elaborating travel itineraries considering the visitors´ profiles, travel distances and costs. Other parameters such as how much the tourists value the attractions offered are particularly taken into account, as well as their preferences regarding the order of visiting these attractions. This problem can be seen as a traveling salesman problem with profits that also considers priority prizes. It is described by an optimization model based on mixed integer programming, which aims to generate itineraries that maximize the total value of the attractions visited and minimize the total travel cost involved. We present results for sets of instances based on randomly generated and real data by applying mathematical programming techniques and a tailored tabu search algorithm. To obtain the input parameters of the problem for the real cases, some statistical techniques are used to analyze the data collected, such as multivariate correspondence analysis. The resulting solutions illustrate the potential application of the proposed approach to support planning and formatting decisions of travel itineraries. [ABSTRACT FROM AUTHOR]

Abstract This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima. Highlights • We describe three counterexample guided inductive optimization (CEGIO) approaches based on Satisfiability Modulo Theories. • CEGIO-Generalized is used for solving any constrained optimization problem. • CEGIO-Simplified is employed if information about the minima location is known. • CEGIO-Fast is employed for convex functions to speed up the optimization process. • Our proposed approaches outperform traditional optimization techniques employed for convex and non-convex functions. [ABSTRACT FROM AUTHOR]

We present a penalty method with trust-region technique for nonlinear bilevel optimization problem in this paper. This method follows Dennis, El-Alem, and Williamson active set idea and penalty method to transform the nonlinear bilevel optimization problem to unconstrained optimization problem. This method maybe simpler than similar ideas and it does not need to compute a base of the null space. A trust-region technique is used to globalize the algorithm. Global convergence theorem is presented and applications to mathematical programs with equilibrium constraints are given. [ABSTRACT FROM AUTHOR]

• This paper presents the optimal design of distillation columns using Set Trimming and Enumeration. • The Set Trimming algorithm reduces the number of design solution candidates to evaluate. • The smart enumeration algorithm guarantees global optimality and has a lower computational effort than complete enumeration. • The scheme Set Trimming followed by Enumeration is robust and does not fail. In this article, we present an alternative approach to the use of metaheuristic methods (GA, PSO, SA, etc.) or mathematical programming (MINLP solvers) for the optimal design of one-feed-two-products distillation columns. We propose the use of Set Trimming followed by candidate enumeration. For the evaluation of the performance of each candidate solution, the method relies on solving the associated system of equations. Three different enumeration procedures are tested: Exhaustive Enumeration, Smart Enumeration, and Segmental Smart Enumeration. Smart Enumeration is an optimization procedure that identifies the solution through a search in the set of candidates organized in ascending order of the objective function lower bound, while Segmental Smart Enumeration is introduced in this article. We compare the results of the proposed procedure with the results using an MINLP approach with different solvers. Numerical results indicate that the best alternative of the enumeration algorithms can identify the global optimum faster than a global solver of mathematical optimization. Numerical tests also showed local solvers which attained optimal solutions quickly but may be trapped in a local optimum. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider the problem of creating artificial training set containing instances of a given task with known meta-feature set. We focus on the Traveling salesman problem, however, the approach we present is applicable for any other optimization task. We modify the Smith-Miles approach for spanning of meta-feature space by introducing set diversity function. We use this quality function to reduce instance generation problem to an optimization problem. We can most fully span the whole meta-feature space by maximizing the set diversity quality function. Another method we present to sample an instance set is generating certain instances one by one, making them close to specific meta-feature vectors. In this case, we use Euclidian distance between the given meta-feature vector and obtained instance meta-features as the target function for minimization. We compare these two approaches with the na¨ıve baseline random instance generation method that did not take into account diversity of the obtained instance set. We use genetic algorithm and simulated annealing method as the optimization algorithms used on the meta-level. The value of the set diversity function for the naïve method is equal to 0.123, for the method that directly maximizes diversity by genetic algorithm: 0.468, and for the method that generates single instances by simulated annealing: 0.595. [ABSTRACT FROM AUTHOR]

Abstract Previous publications presented an implementation of coarse-grained parallel Branch-and-Bound (B&B) method. It supports two strategies: decomposition of feasible domain into a set of sub-problems; "multisearch" (or concurrent) solving the same problem with different settings of the BNB-method. In both cases several B&B-solver's processes exchange best values of goal function on feasible solutions they found. Earlier, it was found that this approach gives a noticeable speed-up of solving the original optimization problem. The method has been implemented as an Everest-application called DDBNB (Domain Decomposition B&B). It is based on Everest distributed computing platform, everest.distcomp.org, and open source solvers: CBC, projects.coin-or.org/Cbc; SCIP, scip.zib.de. Originally, DDBNB has been tested on discrete optimization problems (Traveling Salesman and Load Balancing). The paper presents application of DDBNB to global optimization problems with polynomial functions (solver SCIP supports solving of such class of problems). Well known problems concerning optimal placements of points on 3D-sphere are considered: Tammes and Thomson problems. The first problem is to maximize minimal distance between any two of N points. The second - minimize electrostatic Coulomb energy of unit charges put in N points. Both may be represented as mathematical programming problems with polynomial functions. Feasible domains of both problems are direct products of 3D-spheres and different decompositions of this domains by linear inequalities into hundreds of subproblems have been tested. Tests performed gave promising results and crucial acceleration for Tammes problems, N =7,8,9, and Thomson problem, N =5. One should note that Thomson problem got computer aided proof (substantially based on the problem's specifics) less than a decade ago, when proposed approach is rather generic and does not go into details of the problem to be solved. Formulation of original problems and decomposition strategies have been implemented via Pyomo, pyomo.org. All sub-problems have been passed to solvers as AMPL-stubs, (also known as NL-files). Usage of Everest platform enables to involve in experiments HPC resources of NRC Kurchatov Institute. [ABSTRACT FROM AUTHOR]

In this paper, we study the weighted two-stage stochastic fault-tolerant facility location problem. We present a deterministic LP-rounding 5-approximation algorithm by exploiting both of its stochastic and fault-tolerant structures. We further offer an improved randomized LP-rounding 3.8617-approximation algorithm along with the corresponding derandomized version with the same approximation ratio. [ABSTRACT FROM AUTHOR]

This paper proposes a bi–level formulation for a coupled microgrid power and reserve capacity planning problem, cast within the jurisdiction of a distribution system operator(DSO). The upper level problem of the proposed bi–level model represents a microgrid planner whose goal is to minimize its planning and operational cost, while the lower level problem represents a DSO whose primary duty is to ensure reliable power supply. The microgrid planner, pursues its interest by co–optimizing the design configuration and power output of individual distributed energy resources (DERs), while the DSO maximizes the capacity of flexible reserve resources. The proposed model is recast as a mathematical program with equilibrium constraints (MPEC) wherein the decision variables of the two problems are independently controlled. Application of the proposed approach to the energy infrastructure of a Canadian utility network is discussed. Results obtained through its application are compared to an alternative multi–objective planning model and the improved benefits are assigned to the corresponding stakeholders. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study. [ABSTRACT FROM AUTHOR]

To the best of our knowledge, there is no method to find the product of unrestricted LR-type intuitionistic fuzzy numbers (IFNs) as well as the optimal solutions of LR-type intuitionistic fuzzy linear programming problems (IFLPPs) in which some or all the decision variables are unrestricted. Therefore it is necessary to pay attention to this issue and there is need to find the product of unrestricted LR-type IFNs as well as the optimal solutions of such programming problems. In this paper, the product of unrestricted LR-type IFNs based on the α -cut, β -cut and ( α, β )-cut is proposed. Then with the help of proposed product, a new method is proposed to find the optimal solutions of unrestricted LR-type IFLPPs. Finally, an illustrative example is given to support the proposed method and investigated the applicability of existing approaches exist in the literature. [ABSTRACT FROM AUTHOR]

A novel computational method, namely the unified perturbation mathematical programming (UPMP) approach, for hybrid uncertainty analysis of engineering structures is proposed in this paper. The presented study considers a mixture of random and interval system parameters which are frequently encountered in engineering applications. Within the UPMP approach, matrix perturbation theory is adopted in combination with the mathematical programming approach. The proposed computational method provides a non-simulative hybrid uncertainty analysis framework, which is competent to offer the extreme bounds of the statistical characteristics (i.e., mean and variance) of any concerned structural responses in computationally tractable fashion. In order to thoroughly explore various intricate aspects of the engineering system involving hybrid uncertainties, systematic numerical experiments have also been conducted. Diverse statistical analyses are implemented to identify the bounded probability profile of the uncertain structural responses. Both academic and practical engineering structures are investigated to justify the applicability, accuracy and efficiency of the proposed UPMP approach. [ABSTRACT FROM AUTHOR]

In the context of the "smart city", information and communication technologies (ICT) have become indispensable in the planning and design of modern municipal solid waste management. Due to more waste varieties, there are urgent calls to implement waste classification worldwide, which promotes resource recycling to achieve sustainable development. In this paper, we present an ICT-based smart waste classification and collection system (SWCCS) that is abstracted as a bi-objective mathematical programming model to optimize the waste collection problem. To implement the proposed SWCCS effectively, we design a novel multi-objective hybrid algorithm based on the whale optimization and genetic algorithms (MOGWOA) with an improved convergence factor and a fast, non-dominated sorting method. A comparison of our algorithm with two classical multi-objective algorithms on generated test instances and on a real-world case shows that the proposed MOGWOA is more effective for optimizing the established model. This paper demonstrates how the ICT-based SWCCS works and how it can help sanitation companies improve waste collection both economically and environmentally. • A sustainable smart waste classification and collection system is investigated. • A dual-objective mathematical model is developed to minimize cost and workload. • A hybrid multi-objective whale optimization algorithm is presented. • Effectiveness of the model and algorithm proposed in this paper is proved. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) is a low-carbon technology aiming to prevent carbon dioxide (CO 2 ) generated in large industrial facilities (e.g. power plants) from entering the atmosphere, thus mitigating human-caused climate change. CCS is deemed to be one of the most promising approaches to reduce industrial CO 2 emissions on a global scale, in addition to energy efficiency enhancement and increased use of renewables. This paper presents a mathematical programming model for multi-period planning of power plant retrofits with carbon capture (CC) technologies. The model allows for energy penalties due to CC retrofits and the need for compensatory power generation, as well as variations in technological parameters (such as electricity costs) over time. Furthermore, the model is formulated as a mixed integer linear programme (MILP), for which global optimality is guaranteed if a solution exists. Two case studies on carbon-constrained energy sector planning are presented to illustrate the proposed approach. Further analysis is carried out to examine the effect of the cost limit on the total increase in power generation cost. [ABSTRACT FROM AUTHOR]

The refined oil transportation problem investigated in this paper lies on the intersection of the scheduling and routing of tramp shipping and the petroleum supply chain, with unprecedented large-scale and complex rules. Two mixed-integer linear programming formulations are developed for the assignment between tasks, vessels, and timing issues. The first model uses a time-slot concept under a continuous-time representation, where the constraints that deal with vessel assignment, capacity, timing, demand, and slack stock control are considered. The second model uses a discrete-time representation with time assignment, portal counting, and strict stock control constraints. By virtue of the data collected from an oil company, this modeling approach is validated and used to generate feasible scheduling solutions with lower costs than are currently achieved in the real situation. Finally, the impact of the model parameters is analyzed under different optimization scenarios. [ABSTRACT FROM AUTHOR]

Node placement problems, such as the deployment of radio-frequency identification systems or wireless sensor networks, are important problems encountered in various engineering fields. Although evolutionary algorithms have been successfully applied to node placement problems, their fixed-length encoding scheme limits the scope to adjust the number of deployed nodes optimally. To solve this problem, we develop a flexible genetic algorithm in this paper. With variable-length encoding, subarea-swap crossover, and Gaussian mutation, the flexible genetic algorithm is able to adjust the number of nodes and their corresponding properties automatically. Offspring (candidate layouts) are created legibly through a simple crossover that swaps selected subareas of parental layouts and through a simple mutation that tunes the properties of nodes. The flexible genetic algorithm is generic and suitable for various kinds of node placement problems. Two typical real-world node placement problems, i.e., the wind farm layout optimization and radio-frequency identification network planning problems, are used to investigate the performance of the proposed algorithm. Experimental results show that the flexible genetic algorithm offers higher performance than existing tools for solving node placement problems. [ABSTRACT FROM AUTHOR]

Optimising decisions around the location and operation of tower cranes can improve the workflow in construction projects. Traditionally, the location and allocation problems involved in tower crane operations in the literature have been solved separately from the assignment of material supply points to demand points and the scheduling of the crane's activity sequence across supply and demand points on a construction site. To address the gap, this paper proposes a binary integer programming problem, where location of the tower crane, allocation of supply points to material-demanding regions, and routing of hook of the crane based on activity sequencing of the hook across supply and material-demanding regions on site are optimised. The novelty in this work is in the way the crane's activity scheduling is modelled via mathematical programming, based on routing the hook movement to meet material demand, through minimising tower crane operating costs. A realistic case study is solved to assess the validity of the model. The model is contrasted with results obtained from other solving algorithms commonly adopted in the literature, along with a solution proposed by an experienced practitioner. Results indicate that all instances can be solved when compared to other meta -heuristics that fail to achieve an optimum solution. Compared to the solution proposed by the practitioner, the results of the proposed model achieve a 46% improvement in objective function value. Planners should optimise decisions related to the location of the crane, the crane's hook movement to meet service requests, and supply points' locations and assignment to material-demanding regions simultaneously for effective crane operations. [ABSTRACT FROM AUTHOR]

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust/chance constrained optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty of interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models. [ABSTRACT FROM AUTHOR]

Gas emissions are a major source of the air pollution that causes global warming, climate changes and ozone layer depletion. A large portion of these pollutants come from crude oil refining in the form of nitrogen oxides (NOx), sulfur oxides (SOx) and volatile organic compounds (VOC). Gas emissions can be mitigated during crude oil refining using different methods associated with different investment costs. The aim of this paper is to develop an optimization model that identifies the best mitigation technology with minimum cost. A case study is presented for a refinery in Saudi Arabia that has three mitigation alternatives for gas emissions reduction, namely, balancing, fuel switching and specialized technologies. The effect on the plant's profitability is studied with different reduction targets (20%–90% cut in emissions). The profit margin of the refinery for each scenario is formulated as a mixed integer nonlinear programming model. The model enables the plant's management to correlate emission reduction to its effect on the refinery's profitability. The results of the model urge the revision of legislation to offer incentive packages for plants that achieve higher pollutant reduction. Also, a universal curve is obtained for the fractional loss of profitability as a function of percent reduction of specific pollutant emissions. This is achieved by relating the loss in profitability to that of an equivalent “zero-emissions” refinery. [ABSTRACT FROM AUTHOR]

In this paper, we survey mathematical programming methods for the management of pressure in water distribution systems through optimal placement and operation of control valves. The optimization framework addresses the minimization of average zone pressure under multiple demand scenarios, enforcing hydraulic equations as nonlinear constraints. Binary variables are used to model the placement of valves. The derived nonlinear optimization problem is a non-convex MINLP. We implement and evaluate a direct MINLP solver and two different reformulation methods for MINLPs that solve sequences of regular NLPs. Moreover, we investigate the solutions under different design and operation loading conditions. [ABSTRACT FROM AUTHOR]

This paper discusses a kind of mathematical programs with equilibrium constraints (MPEC for short). By using a complementarity function and a kind of disturbed technique, the original (MPEC) problem is transformed into a nonlinear equality and inequality constrained optimization problem. Then, we combine a generalized gradient projection matrix with penalty function technique to given a generalized project metric algorithm with arbitrary initial point for the (MPEC) problems. In order to avoid Mataros effect, a high-order revised direction is obtained by an explicit formula. Under some relative weaker conditions, the proposed method is proved to possess global convergence and superlinear convergence. [ABSTRACT FROM AUTHOR]

Recognizing human actions in video sequences has been a challenging problem in the last few years due to its real-world applications. A lot of action representation approaches have been proposed to improve the action recognition performance. Despite the popularity of local features-based approaches together with “Bag-of-Words” model for action representation, it fails to capture adequate spatial or temporal relationships. In an attempt to overcome this problem, a trajectory-based local representation approaches have been proposed to capture the temporal information. This paper introduces an improvement of trajectory-based human action recognition approaches to capture discriminative temporal relationships. In our approach, we extract trajectories by tracking the detected spatio-temporal interest points named “cuboid features” with matching its SIFT descriptors over the consecutive frames. We, also, propose a linking and exploring method to obtain efficient trajectories for motion representation in realistic conditions. Then the volumes around the trajectories’ points are described to represent human actions based on the Bag-of-Words (BOW) model. Finally, a support vector machine is used to classify human actions. The effectiveness of the proposed approach was evaluated on three popular datasets (KTH, Weizmann and UCF sports). Experimental results showed that the proposed approach yields considerable performance improvement over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Nowadays, there is an increasing dependence on metaheuristic algorithms for solving combinatorial optimization problems. This paper discusses various metaheuristic algorithms, their similarities and differences and how Ant Colony Optimization algorithm is found to be much more suitable for providing a generic implementation. We start with the solution for Travelling Salesman Problem using Ant Colony Optimization (ACO) and show how Polynomial Turing Reduction helps us solve Job Shop Scheduling and Knapsack Problems without making considerable changes in the implementation. The probabilistic nature of metaheuristic algorithms, especially ACO helps us to a greater extent in avoiding parameter fine-tuning. Through Sensitivity analysis we find that ACO exhibits better resilience to changes in parameter values in comparison to other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This work studies the supply allocation problem, using a Stackelberg game, for an established timberlands supply chain with an additional decision of new biorefinery investments. In a timberlands system, harvester and manufacturer decision makers have separate objectives to maximize their respective profits. This interaction is represented with a turn based Stackelberg game. The harvesters decide first on the quantity harvested, and the manufacturers decide on how much to utilize. This game is modeled with a bilevel mathematical program. The novel feature of this paper's bilevel formulation is the inclusion of parametric uncertainty in a two stage model. The first stage problem involves logistical decisions around biorefinery investments, such as location and capacity, while the second stage problem involves a bilevel timberlands model with parameter uncertainty. Studying this problem formulation revealed interesting insights for solving multiperiod problems with bilevel stages as well as the decision maker's behavior for the timberlands model. [ABSTRACT FROM AUTHOR]

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs' number P and local evolution numbers iPart were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]

Safety and performance are important factors in the design and operation of chemical plants. This paper describes the formulation of a mixed integer nonlinear programming model for the optimization of plant layout with safety considerations. The model considers a quantitative risk analysis to take safety into account, and a bowtie analysis is used to identify possible catastrophic outcomes. These effects are quantified through consequence analyses and probit models. The model allows the location of facilities at any available point, an advantage over grid-based models. Two case studies are solved to show the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: This article describes the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains. It reviews the major energy pathways from terrestrial and aquatic biomass to bioenergy/biofuel products as well as power and heat with an emphasis on “drop-in” liquid hydrocarbon fuels. Key components of the bioenergy supply chains are then presented, along with a comprehensive overview and classification of the existing contributions on biofuel/bioenergy supply chain optimization. This paper identifies fertile avenues for future research that focuses on multi-scale modeling and optimization, which allows the integration across spatial scales from unit operations to biorefinery processes and to biofuel value chains, as well as across temporal scales from operational level to strategic level. Perspectives on future biofuel supply chains that integrate with petroleum refinery supply chains and/or carbon capture and sequestration systems are presented. Issues on modeling of sustainability and the treatment of uncertainties in bioenergy supply chain optimization are also discussed. [Copyright &y& Elsevier]

In recent years, distributed energy systems (DES) have received an extensive attention, but they also face technical limitations in system architecture, operational strategies, and optimization methods. In this paper, for two typical DES, Natural Gas Cooling and Heating Triple Supply System (CCHP) and Hybrid Energy Distributed System (HDES), multi-objective optimization and evaluation have been carried out. In the optimization phase, for the two systems, a multi-objective mathematical programming (MOMP) model was developed to minimize the annual total cost and annual carbon emissions under three different operational strategies (heat load following (FTL), electrical load following (FEL), and load characteristic matching following (FCL)). Further, operational strategies were transformed into operational constraints, and models were solved using the augmented ε-constrain method. In the evaluation stage, the reference cost growth rate (CSR) and the reference CO2 emission reduction rate (CRR) evaluation indices were developed, and subsequently for two DES systems, comprehensive evaluations were conducted by incorporating with the weight distributed TOPSIS multi-objective decision-making method. Moreover, an office building in Nanjing was chosen as the baseline building, and the two DES systems were simulated. The results show that, under different operating strategies, CCHP has the advantage of lower cost, while the HDES has the advantage of the lower CO2 emission. Under the proposed FCL operation strategy, the maximum environmental benefits can be obtained. Compared to the FEL operation strategy, the CO2 emissions of CCHP and HDES have been reduced by approximately 4.2% and 2.9%, respectively. Compared to the FTL operating strategy, the CO2 emissions of CCHP and HDES have been reduced by approximately 15.2% and 13.7%, respectively. [ABSTRACT FROM AUTHOR]