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Simple Summary: Cervidae represents a substantial family within the Artiodactyla, yet their phylogenetic relationship has not been fully clarified due to limited data available. With the increasing availability of genomic data, there is now an opportunity to further explore their phylogenetic relationship. In our study, we employed a comparative genomics approach focusing on the chromosome-level genomes of eight Cervidae species to explore their phylogenetic relationship and chromosomal evolutionary events. Furthermore, we conducted an analysis of gene families to identify those potentially involved in adaptive evolution. Cervidae represents a family that is not only rich in species diversity but also exhibits a wide range of karyotypes. The controversies regarding the phylogeny and classification of Cervidae still persist. The flourishing development of the genomic era has made it possible to address these issues at the genomic level. Here, the genomes of nine species were used to explore the phylogeny and chromosomal evolutionary events of Cervidae. By conducting whole-genome comparisons, we identified single-copy orthologous genes across the nine species and constructed a phylogenetic tree based on the single-copy orthologous genes sequences, providing new insights into the phylogeny of Cervidae, particularly the phylogenetic relationship among sika deer, red deer, wapiti and Tarim red deer. Gene family analysis revealed contractions in the olfactory receptor gene family and expansions in the histone gene family across eight Cervidae species. Furthermore, synteny analysis was used to explore the chromosomal evolutionary events of Cervidae species, revealing six chromosomal fissions during the evolutionary process from Bovidae to Cervidae. Notably, specific chromosomal fusion events were found in four species of Cervus, and a unique chromosomal fusion event was identified in Muntiacus reevesi. Our study further completed the phylogenetic relationship within the Cervidae and demonstrated the feasibility of inferring species phylogeny at the whole-genome level. Additionally, our findings on gene family evolution and the chromosomal evolutionary events in eight Cervidae species lay a foundation for comprehensive research of the evolution of Cervidae. [ABSTRACT FROM AUTHOR]

This study addresses the quay crane scheduling problem (QCSP), which involves scheduling a fixed number of quay cranes to load and unload containers from ships in a maritime container terminal. The objective is to minimize the completion time while adhering to precedence, safety margin, and noncrossing constraints. Efficient scheduling of quay cranes plays a crucial role in reducing the time vessels spend at terminals. To solve the QCSP, we explore different schedule directions for the quay cranes. Specifically, we consider three directions: unidirectional, where the quay cranes maintain a consistent movement direction from upper to lower bays or vice versa after initial repositioning; bidirectional, allowing the cranes to change direction once during operations; and multidirectional, permitting freely changing movement direction during operations. For the bidirectional QCSP, we propose a new compact mathematical formulation. To obtain valid lower bounds on the optimal completion time, we derive various relaxations of this new formulation based on the different schedule directions. Our solution framework employs logic-based Benders decomposition, decomposing the problem into an assignment master problem and operation-sequence slave subproblems. Extensive computational experiments using benchmark instances from existing literature and newly generated instances validate the efficiency and effectiveness of the lower bounds and the exact solution approach. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the Major Program of the National Natural Science Foundation of China [Grants 72192830 and 72192831], the National Natural Science Foundation of China [Grant 72102034], and the 111 Project [Grant B16009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0298 [ABSTRACT FROM AUTHOR]

Simple Summary: Livestock and poultry germplasm resources are national strategic resources related to the global revitalization of the seed industry. As an important part of livestock, velvet deer germplasm resources must be taken seriously. Our review focuses on the situation and utilization of velvet deer germplasm resources in China. We mainly introduce the situation of velvet deer germplasm resources in China, such as ecological distribution and the number of subspecies. We further elaborate on the domestication and breeding development of velvet deer in China and put forward suggestions on the scientific utilization of velvet deer germplasm resources. Velvet deer are not only a representative special economic animal but also an important part of livestock. With the increasing awareness of international competition for germplasm resources in China, more and more attention has been paid to the protection and utilization of germplasm resources. However, there is poor understanding about velvet deer resources. Therefore, we are providing a comprehensive introduction of Chinese velvet deer germplasm resources from the aspects of ecological distribution, domestication and breeding. [ABSTRACT FROM AUTHOR]

The task assignment problem (TAP) is concerned with assigning a set of tasks to a set of agents subject to the limited processing and memory capacities of each agent. The objective to be minimized is the total assignment cost and total communication cost. TAP is a relevant model for many practical applications, yet solving the problem is computationally challenging. Most of the current metaheuristic algorithms for TAP adopt population-based search frameworks, whose search behaviors are usually difficult to analyze and understand due to their complex features. In this work, unlike previous population-based solution methods, we concentrate on a single trajectory stochastic local search model to solve TAP. Especially, we consider TAP from the perspective of a grouping problem and introduce the first probability learning-based local search algorithm for the problem. The proposed algorithm relies on a dual probability learning procedure to discover promising search regions and a gain-based neighborhood search procedure to intensively exploit a given search region. We perform extensive computational experiments on a set of 180 benchmark instances with the proposed algorithm and the general mixed integer programming solver CPLEX. We assess the composing ingredients of the proposed algorithm to shed light on their impacts on the performance of the algorithm. Note to Practitioners—This work is motivated by the problem of program modules designing and task allocation in parallel and distribution systems. It can also be applied to deal with job (task) grouping problems in practical industrial applications. This article presents a novel and effective learning-based local search algorithm to obtain high-quality solutions for the considered problem. The results of numerical experiments and comparisons show that our algorithm can achieve good search performances on problem instances of different scales and difficulties. Afterward, we use the proposed solution method to solve a real-life open-order slab assignment problem, which is derived from the production planning of silicon steel for an iron and steel company. The learning techniques of the proposed algorithm are of general interest and can be used in search algorithms for solving other real-life optimization problems with grouping features. For future research, we will design solution methods based on these learning techniques to address other practical optimization problems. [ABSTRACT FROM AUTHOR]

Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of nonlinear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling, and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this article, we propose a novel EDA which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective. Note to Practitioners—In many process industries, there exist black-box operation optimization problems and large-scale nonlinear optimization problems with variable coupling. For these problems, it is difficult to establish mechanism models between input and output. However, real-time data can be measured from the system through sensors. We can utilize this process information to optimize the system so as to attain the desired objective. In this article, we propose a novel estimation of distribution algorithm (EDA) which applies a filtering scheme to revise the modeling data and a learning strategy to improve sampling, which can solve the problems with the characteristics of nonlinearity, variable coupling, and large scale. Computational experiments show that the EDA is effective. In the future, the proposed algorithm can be applied to some practical optimization problems such as operation optimization in blast furnace, which is considered as a continuous production process with variable coupling. The algorithm has the potential to help optimizing the process control parameters. [ABSTRACT FROM AUTHOR]

The steelmaking process in the iron and steel industry involves complicated physicochemical reactions. The main aim of steelmaking is to adjust the quality of molten steel. During the steel-tapping process, the temperature and carbon content are the most essential quality indices for end-point prediction. This article presents a novel machine learning framework for the end-point prediction problems in the smelting process. Considering the importance of data representation in modeling, the original data are inputted to a stacked autoencoder (SAE) to extract the essential features in an unsupervised manner. The top layer is then designed as a sparse Bayesian regression (SBR) layer to obtain the predicted mean values and error bars that measure the uncertainty in the prediction. To improve the generalization ability of the prediction model, an intelligent optimization algorithm based on improved differential evolution (DE) is used to optimize the hyperparameters of the model. The main advantage of this model is that the underlying characteristics of the samples can be learned automatically layer by layer, instead of designing them manually. Finally, the effectiveness of the proposed method is verified using real data collected from two steel plants. The experimental results show that the proposed model gives a more precise prediction than other existing models and can provide error bars for the end-point prediction. Note to Practitioners—The end-point prediction is critical to the quality of products produced in the iron and steel production process. Because of the complexity of the physiochemical reactions during smelting, it is difficult to build mechanism models for the complex environment. Generally, expert knowledge and experience are needed for real production. The motivation behind this article was to establish a data-driven model through machine learning methods to solve end-point prediction problems. First, a stacked autoencoder (SAE) was used to extract the essential information from the original data. Second, sparse Bayesian regression (SBR) was applied to the top layer. Thus, the predicted mean values and error bars could be obtained to improve the robustness of the prediction model. Furthermore, an improved differential evolution (DE) algorithm was designed to adaptively optimize the hyperparameters of the model. In the experiments, the proposed method was applied to two real steelmaking production processes. The results verify the effectiveness of the proposed model. This article can be extended to other processes such as continuous casting and reheating furnace. The model can also be generalized for other practical industries to improve the product quality and enable safer production. [ABSTRACT FROM AUTHOR]

We study integrated production- and delivery-scheduling problems that arise in practical make-to-order settings in several industries. In these problems, make-to-order products are first processed in a plant and then delivered to customer sites through two stages of shipping: first, from the plant to a pool point (e.g., a port, a distribution, or a consolidation center) and, second, from the pool point to customer sites. The objective is to obtain a joint schedule of job processing at the plant and two-stage shipping of completed jobs to customer sites to optimize a performance measure that takes into account both delivery timeliness and total transportation costs. We consider two problems in which delivery timeliness is measured by total or maximum lead time of the jobs and study both offline and online versions of these problems. For the offline problems involving a single production line at the plant, we provide optimal dynamic programming algorithms. For the more general offline problems involving multiple production lines at the plant, we propose fast heuristics and analyze their worst-case and asymptotic performances. For the online problems, we propose online algorithms and analyze their competitive ratios. By comparing our offline heuristics with lower bounds using randomly generated test instances, it is shown that these heuristics are capable of generating near-optimal solutions quickly. Using real data from Baosteel's Meishan plant, we also show that our corresponding offline heuristic generates significantly better solutions than Baosteel's rule-based approach. In addition, our computational results on the performance of the online algorithms relative to the offline heuristics generate important methodological insights that can be used by practitioners in choosing a specific solution approach. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model’s accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a basic oxygen furnace steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy. Note to Practitioners—With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model’s performance, a differential evolution algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a basic oxygen furnace (BOF) steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation. [ABSTRACT FROM AUTHOR]

Abstract In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand. [ABSTRACT FROM AUTHOR]

Highlights • A mixed integer nonlinear program is formulated for a new integrated steelmaking scheduling problem that incorporates batching decisions and energy constraints. • An enhanced spatial branch and bound algorithm is proposed that outperforms the commercial solvers in solving the studied problem. • A three-phase MILP-NLP decomposition strategy is developed that is able to either work as a node heuristic or run as an independent heuristic. Abstract This paper investigates a practical steelmaking scheduling problem with batching decisions and energy constraints, in which batching is used to decide how to group and sequence a number of jobs to form job groups so as to meet batch production mode. Incorporating energy consideration into the scheduling is motivated by practical demand and the potential to reduce the energy bill through optimal scheduling. Based upon our proposed energy expressions, an MINLP model is formulated and solved using the spatial branch and bound (B&B) algorithm, which is enhanced by the proposed decomposition strategy working as a node heuristic. The benefits of the integrated scheduling and some sensitivity analysis experiments are reported on an illustrative example. Experiments on randomly generated instances show that the energy expressions are superior to the one of Hadera et al. (2015) , the B&B outperforms the state-of-the-art commercial solvers and the decomposition strategy performs well running as an independent heuristic. [ABSTRACT FROM AUTHOR]

Steel-making production is a dynamic process that has the characteristics of high temperature and heat, and a complex reaction mechanism that causes the mechanism model possibly to be unavailable and the system to be a black box. In this article, a dynamic operation optimization (DOO) problem is refined from the basic oxygen furnace (BOF) steel-making process, and the system model is formulated by a data analytics method. This prevents to solve the optimization problem with derivative-based optimization methods. To circumvent these difficulties, a surrogate-model-based derivative-free optimization algorithm is proposed for solving the DOO problem. In order to establish the surrogate model with the least number of function evaluations, a subset selection strategy is designed to find a sparse structure for the optimization problem, based on which a set of simple bases is determined to establish the surrogate model. Moreover, this also reduces the scale of the parameter optimization problem. Numerical experiments on actual production data verify the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The study proposes a stochastic optimisation approach based on discrete scenarios and the minimax criterion to deal with demand uncertainty in the two‐stage unit commitment problem. To avoid making an over‐conservative decision, the approach is designed to assign a probability‐based weight to each demand scenario and provide a unit commitment schedule that minimises the maximum possible‐weighted generation cost. Unit commitment is determined in the first stage and the economic dispatch under the scenario that corresponds to the maximum possible‐weighted generation cost is determined in the second stage. The problem is formulated as a type of min–max–min mixed integer programming model. By introducing an auxiliary variable, the model is further transformed into a minimisation problem. A Benders decomposition algorithm is developed to solve the problem. The Benders master problem determines the unit commitment decision, while the Benders subproblem determines the dispatch decision. Multiple Benders feasibility cuts are constructed when the Benders subproblem is infeasible. Valid inequalities are derived to improve the lower bound provided by the Benders master problem. Numerical results show the performance of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents scaled quadratic cuts based on scaling the second-order Taylor expansion terms for the decomposition methods Outer Approximation and Partial Surrogate Cuts for solving convex Mixed Integer Nonlinear Programing problems. The scaled quadratic cut is proved to be a stricter and tighter underestimation for convex nonlinear functions than classical supporting hyperplanes, which results in the improvement of Outer Approximation and Partial Surrogate Cuts based solution methods. We integrate the strategies of scaled quadratic cuts with multi-generation cuts for Outer Approximation and Partial Surrogate Cuts and develop six types of Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts. These cuts are incorporated in the master problem of the decomposition methods leading to a Mixed Integer Quadratically Constrained Programming problem. Numerical results of benchmark Mixed Integer Nonlinear Programming problems demonstrate the effectiveness of the proposed Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts. [ABSTRACT FROM AUTHOR]

This paper addresses the short-term scheduling problem for the ethylene cracking process with feedstocks and energy constraints. The cracking production of ethylene is a process with units that have decaying performance, requiring periodic cleanup to restore their performance. Under the condition of limited feedstocks, the production operating mode of the cracking furnaces is to keep yields constant by continuously increasing the coil temperature. We present a hybrid MINLP/GDP formulation based on continuous-time representation for the scheduling problem over a finite time horizon. In order to solve the proposed model, which is reformulated as an MINLP model, an improved outer approximation algorithm with multi-generation cuts and problem-dependent integer cuts are developed to solve real large-scale problems. Numerical examples are presented to illustrate the application of the model. Based on analyzing the optimal solution and sensitivity of the model, some conclusions are obtained to provide useful suggestions for real cracking process production. [ABSTRACT FROM AUTHOR]

This study is motivated by the practices of large iron and steel companies that have steady and heavy demands for bulk raw materials, such as iron ore, coal, limestone, etc. These materials are usually transported to a bulk cargo terminal by ships (or to a station by trains). Once unloaded, they are moved to and stored in a bulk material stockyard, waiting for retrieval for use in production. Efficient storage space allocation and ship scheduling are critical to achieving high space utilization, low material loss, and low transportation costs. In this article, we study the integrated storage space allocation and ship scheduling problem in the bulk cargo terminal. Our problem is different from other associated problems due to the special way that the materials are transported and stored. A novel mixed-integer programming model is developed and then solved using a Benders decomposition algorithm, which is enhanced by the use of various valid inequalities, combinatorial Benders cuts, variable reduction tests, and an iterative heuristic procedure. Computational results indicate that the proposed solution method is much more efficient than the standard solution software CPLEX. [ABSTRACT FROM AUTHOR]

We present a weak finite element method for elliptic problems in one space dimension. Our analysis shows that this method has more advantages than the known weak Galerkin method proposed for multi-dimensional problems, for example, it has higher accuracy and the derived discrete equations can be solved locally, element by element. We derive the optimal error estimates in the discrete H 1 -norm, the L 2 -norm and L ∞ -norm, respectively. Moreover, some superconvergence results are also given. Finally, numerical examples are provided to illustrate our theoretical analysis. [ABSTRACT FROM AUTHOR]

We study the superconvergence of the stable lowest equal-order finite element pair solving the Stokes problem. The superclose property is proved for the interpolation function; then a superconvergence rate of $$O(h^{\frac{3}{2}})$$ -order is obtained for the velocity gradient approximation by using the post-processing technique, and an $$O(h^{\frac{3}{2}})$$ -order error estimate is derived for the pressure approximation. In addition, the asymptotically exact a posteriori error estimate also is given by means of the superconvergence result. Finally, some numerical experiments are provided to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

We propose and analyze the finite volume method for solving the variational inequalities of first and second kinds. The stability and convergence analysis are given for this method. For the elliptic obstacle problem,we derive the optimal error estimate in the H1-norm. For the simplified friction problem,we establish an abstract H1-error estimate, which implies the convergence if the exact solution u ϵ H1(Ω) and the optimal error estimate if u ϵ H11+α(Ω)0 < ⩽ 2. [ABSTRACT FROM AUTHOR]

We present a new stabilized finite volume method for Stokes problem using the lowest order P − P element pair. To offset the lack of the inf - sup condition, a simple jump term of discrete pressure is added to the continuity approximation equation. A discrete inf - sup condition is established for this stabilized scheme. The optimal error estimates are given in the H- and L-norms for velocity and in the L-norm for pressure, respectively. [ABSTRACT FROM AUTHOR]

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time. [ABSTRACT FROM AUTHOR]

This paper investigates a new lowcarbon iron-making supply chainplanning problem in the steel industry under the carbon cap and trademechanism, in which a steel company uses carbon emission quota toproduce iron to meet determined demands over the planning horizon,and buys or sells the rights to emit carbon in the carbon tradingmarket. The problem decides optimal carbon trade, raw material purchasing,raw materials and sinters inventory levels, as well as sintering andiron making production schemes, so as to minimize the total cost.A novel mixed integer programming model incorporating carbon emissionreduction into operational decision making is developed and we proposea branch and price algorithm to solve it. The model is decomposedequally into a master problem and two subproblems. The branch andprice algorithm is enhanced by two tailored techniques. First, therelaxation of the master problem is strengthened by introducing twofamilies of valid inequalities. Second, we use a dynamic programmingprocedure to eliminate variables by path reduced cost. We test ouralgorithm on randomly generated data that simulate the practical production,and the computational results over the instances illustrate the effectivenessof the proposed algorithm. In addition, we derive the optimal operationaldecisions, and examine the impacts of carbon cap and carbon priceanalytically and numerically on total cost and carbon emissions. Wemake interesting observations based on numerical results and providemanagerial insight to highlight the opportunity to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

This article studies a single-crane scheduling problem in a warehouse where steel coils are stored in two levels. A given set of coils is to be retrieved from their designated places. If a required coil at the lower level is blocked by one or two coils at the upper level, to retrieve it, the blocking coils must be first moved to other positions. The considered problem is to determine the new positions and the required crane movements so that all coils are retrieved in the shortest possible time. A mixed-integer linear programming model is formulated for the problem and a sequential solution approach is implemented. A dynamic programming algorithm is proposed for optimally solving a restricted case. Based on the analysis of a special case, a heuristic algorithm is proposed for the general case and its worst-case performance is analyzed. The average performances of the solution approaches are computationally evaluated. The results show that the proposed heuristic algorithms are capable of generating good quality solutions. [ABSTRACT FROM AUTHOR]

We investigate a logistics facility location problem to determine whether the existing facilities remain open or not, what the expansion size of the open facilities should be and which potential facilities should be selected. The problem is formulated as a mixed integer linear programming model (MILP) with the objective to minimize the sum of the savings from closing the existing facilities, the expansion costs, the fixed setup costs, the facility operating costs and the transportation costs. The structure of the model motivates us to solve the problem using Benders decomposition algorithm. Three groups of valid inequalities are derived to improve the lower bounds obtained by the Benders master problem. By separating the primal Benders subproblem, different types of disaggregated cuts of the primal Benders cut are constructed in each iteration. A high density Pareto cut generation method is proposed to accelerate the convergence by lifting Pareto-optimal cuts. Computational experiments show that the combination of all the valid inequalities can improve the lower bounds significantly. By alternately applying the high density Pareto cut generation method based on the best disaggregated cuts, the improved Benders decomposition algorithm is advantageous in decreasing the total number of iterations and CPU time when compared to the standard Benders algorithm and optimization solver CPLEX, especially for large-scale instances. [ABSTRACT FROM AUTHOR]

A steam cracking furnace plays animportant role in the naphthapyrolysis process that produces ethylene and propylene. These twoproducts are the key monomers in the petrochemical industry. Thisarticle investigates the multiobjective operation optimization ofthe naphtha pyrolysis process to maximize the yields of ethylene andpropylene. To solve this problem efficiently, multiobjective paralleldifferential evolution with competitive evolution strategies (MOPDE-CES)is employed. In MOPDE-CES, the population is divided into four parts,each part having its own evolution strategy. During evolution, thefour parts of the population compete with each other to survive andevolve. At the same time, they share search information with eachother by communicating with an external archive that stores the obtainednondominated solutions. The MOPDE-CES approach is compared to otherpowerful multiobjective algorithms, and the computational resultsshow that MOPDE-CES is superior based on benchmarking and practicalproblems. In addition, the computational results of MOPDE-CES indicatethat the operation of an ethylene plant can be improved by increasingthe yields of ethylene and propylene. [ABSTRACT FROM AUTHOR]

CO2 emission reduction is important to the sustainable development of the electric power industry. In this paper, we propose a CO2 emission reduction policy for the thermal generation scheduling problem in the deregulated electricity market. By introducing a variable penalty factor, the policy is designed to apply a different penalty mode according to the range of the emissions quantity. The objective of the scheduling is to maximize the generation profits, which are determined by the electricity sales revenue, the generating cost, and the emissions penalty over the planning horizon. Using a piecewise linear function to express the variable penalty factor, the problem is formulated as a mixed integer nonlinear programming model. A variable splitting-based Lagrangian relaxation algorithm is developed to solve the problem. The numerical results for test cases of different sizes show that the proposed algorithm can find near-optimal solutions in a reasonable time. Additionally, the choice of the number of the penalty modes and the effectiveness of the proposed emission reduction policy are discussed. [ABSTRACT FROM PUBLISHER]

Abstract: The scheduling problem of open shop to minimize makespan with release dates is investigated in this paper. Unlike the usual researches to confirm the conjecture that the tight worst-case performance ratio of the Dense Schedule (DS) is 2−1/m, where m is the number of machines, the asymptotic optimality of the DS is proven when the problem scale tends to infinity. Furthermore, an on-line heuristic based on DS, Dynamic Shortest Processing Time-Dense Schedule, is presented to deal with the off-line and on-line versions of this problem. At the end of the paper, an asymptotically optimal lower bound is provided and the results of numerical experiments show the effectiveness of the heuristic. [Copyright &y& Elsevier]

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We investigate the corrective unit commitment problem to deal with disruption in power system operations caused by an unforeseen unit breakdown with stochastic duration. Since the original unit schedule is no longer feasible when a unit breaks down during operation, a corrective scheduling that provides an immediate response to such a disruption is needed to update the original schedule in time. The objective of the corrective scheduling is to minimize the generation cost and the deviation from the original schedule. The corrective scheduling problem is formulated as a mixed integer nonlinear programming model where the stochastic duration is expressed by tree-structured duration scenarios. The proposed variable splitting-based Lagrangian relaxation algorithm decomposes the problem into multiple single-unit subproblems and a linear programming-type artificial variable subproblem. Each single-unit subproblem is solved by a two-stage procedure. In the first stage, the generating level of the unit in each committed period is determined optimally. In the second stage, before the dynamic programming is called, an effective pre-processing technique based on optimality conditions is applied to speed up the procedure. The dual problem is solved by a bundle method. The numerical results show that the algorithm can find solutions very close to optimums within a reasonable time. [ABSTRACT FROM PUBLISHER]

This paper focuses on a hub reentrant shop scheduling problem which is motivated by actual packing production line in the iron and steel industry. There are five operations for processing a job where three operations must be processed on a hub machine, and between any two consecutive operations on the hub machine, two operations must be processed on other two secondary machines, respectively. We mainly consider two types of the problem. The first is basic hub reentrant shop (BHRS) problem where only one machine in each secondary machine center. The second is hybrid hub reentrant shop (HHRS) problem where multiple machines in each secondary machine center. The objective is to minimize the makespan. For BHRS problem, we show that the problem is NP-hard in the strong sense. Some properties are derived for identifying an optimal schedule. Furthermore, a heuristic algorithm is proposed with the worst performance ratio 6:5, and this ratio is demonstrated tight. For HHRS problem, two well-solvable cases are proposed, respectively. Under a split condition, HHRS is equivalent to a two-stage hybrid flow shop problem. The worst case of a class of proposed algorithms is analyzed. The performance ratio is demonstrated relatively with the secondary machine numbers. [ABSTRACT FROM AUTHOR]

In this article we investigate the parallel machine scheduling problem with job release dates, focusing on the case that machines are dissimilar with each other. The goal of scheduling is to find an assignment and sequence for a set of jobs so that the total weighted completion time is minimised. This type of production environment is frequently encountered in process industry, such as chemical and steel industries, where the scheduling of jobs with different purposes is an important goal. This article formulates the problem as an integer linear programming model. Because of the dissimilarity of machines, the ordinary job-based decomposition method is no longer applicable, a novel machine-based Lagrangian relaxation algorithm is therefore proposed. Penalty terms associated with violations of coupling constraints are introduced to the objective function by Lagrangian multipliers, which are updated using subgradient optimisation method. For each machine-level subproblem after decomposition, a forward dynamic programming algorithm is designed together with the weighted shortest processing time rule to provide an optimal solution. A heuristics is developed to obtain a feasible schedule from the solution of subproblems to provide an upper bound. Numerical results show that the new approach is computationally effective to handle the addressed problem and provide high quality schedules. [ABSTRACT FROM AUTHOR]

This article addresses the open shop scheduling problem with the objective to minimise the maximum completion time, or makespan. Both asymptotical analysis and worst case analysis are conducted for the heuristic, rotation schedule (RS) algorithm. In the asymptotical analysis, we prove that the RS algorithm is asymptotically equal to the optimal solution when the problem size is large enough. In the worst case analysis, we show that the tight worst case performance ratio of RS algorithm is equal to the machine number m. To accelerate the convergence of the RS algorithm for medium size problems, the improved version of the heuristic, the modified RS (MRS) algorithm is presented. At the end of this article, the asymptotical optimality of the RS algorithm and the practical effectiveness of the MRS algorithm are shown by numerical simulations. [ABSTRACT FROM AUTHOR]

Abstract: We first introduce tent map base series. The tent map base series is special case of generalized Lüroth series which has the tent map as a base map. Then we study some elementary properties of its error-sum function, and show that the function is continuous. [Copyright &y& Elsevier]

Abstract: The m-machine open shop problem to minimize the sum of the completion times is investigated in our paper. First, a heuristic algorithm, Shortest Processing Time Block, is presented to deal with problem , where k is positive integer. And then, the heuristic is extended to solve the general problem . It is proved that the heuristic is asymptotically optimal when the job number goes to infinity. At the end of this paper, numerical experimentation results show the effectiveness of the heuristic algorithm. [Copyright &y& Elsevier]

Abstract: We study a coordinated scheduling problem of production and transportation in which each job is transported to a single batching machine for further processing. There are m vehicles that transport jobs from the holding area to the batching machine. Each vehicle can transport only one job at a time. The batching machine can process a batch of jobs simultaneously where there is an upper limit on the batch size. Each batch to be processed occurs a processing cost. The problem is to find a joint schedule of production and transportation such that the sum of the total completion time and the total processing cost is optimized. For a special case of the problem where the job assignment to the vehicles is predetermined, we provide a polynomial time algorithm. For the general problem, we prove that it is NP-hard (in the ordinary sense) and present a pseudo-polynomial time algorithm. A fully polynomial time approximation scheme for the general problem is obtained by converting an especially designed pseudo-polynomial dynamic programming algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a novel hybrid ant colony optimization approach called SS_ACO algorithm to solve the vehicle routing problem. The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with scatter search (SS). In our hybrid algorithm, we use ACO algorithm and greedy heuristic to generate the initial solutions which are then formed the reference set. Within the scatter search framework, after two-solution combination method for the reference set has been applied, we employ ACO method to generate new solutions through updating the common arc pheromone mechanism. Moreover, during implementing the hybrid algorithm, cyclic transfers, a new class of neighborhood search algorithm can also be embedded into the scatter search framework as neighborhood search to improve solutions. Despite the size of the cyclic transfer neighborhood is very large, a restricted subset of the cyclic transfer neighborhood is adopted to reduce the computational requirements to reasonable levels. Finally, the experimental results have shown that the proposed hybrid method is competitive to solve the vehicle routing problem compared with the best existing methods in terms of solution quality. [Copyright &y& Elsevier]

Abstract: This paper studies two-machine flowshop scheduling with batching and release time, whose objective is to minimize the makespan. We formulate the scheduling problem as a mixed integer programming model and show that it is a strongly NP-hard problem. We derive a lower bound and develop dynamic programming-based heuristic algorithms to solve the scheduling problem. Computational experiments are carried out to evaluate the performance of the heuristic algorithms. The numerical results show that some of the heuristic algorithms can indeed find effective solutions for the scheduling problem. [Copyright &y& Elsevier]

Abstract: This paper considers two scheduling problems for a two-machine flowshop where a single machine is followed by a batching machine. The first problem is that there is a transporter to carry the jobs between machines. The second problem is that there are deteriorating jobs to be processed on the single machine. For the first problem with minimizing the makespan, we formulate it as a mixed integer programming model and then prove that it is strongly NP-hard. A heuristic algorithm is proposed for solving this problem and its worst case performance is analyzed. The computational experiments are carried out and the numerical results show that the heuristic algorithm is effective. For the second problem, we derive the optimal algorithms with polynomial time for minimizing the makespan, the total completion time and the maximum lateness, respectively. [Copyright &y& Elsevier]

Abstract: We study the coordinated scheduling problem of hybrid batch production on a single batching machine and two-stage transportation connecting the production, where there is a crane available in the first-stage transportation that transports jobs from the warehouse to the machine and there is a vehicle available in the second-stage transportation to deliver jobs from the machine to the customer. As the job to be carried out is big and heavy in the steel industry, it is reasonable assumed that both the crane and the vehicle have unit capacity. The batching machine processes a batch of jobs simultaneously. Each batch occur a setup cost. The objective is to minimize the sum of the makespan and the total setup cost. We prove that this problem is strongly NP-hard. A polynomial time algorithm is proposed for a case where the job transportation times are identical on the crane or the vehicle. An efficient heuristic algorithm for the general problem is constructed and its tight worst-case bound is analyzed. In order to further verify the performance of the proposed heuristics, we develop a lower bound on the optimal objective function. Computational experiments show that the heuristic algorithm performs well on randomly generated problem instances. [Copyright &y& Elsevier]

This paper investigates the hot rolling production scheduling problem in the steel industry and proposes a new mixed integer programming model for this problem based on the monolithic modeling strategy that integrates batching and scheduling. Using this strategy, the new model can simultaneously schedule multiple turns and simultaneously determine the production timetable of these turns in a global optimal view. A long-term tabu search heuristic using frequency-based memory is developed to obtain near-optimal solutions for this problem. Three kinds of speed-up strategies are developed to accelerate the search procedure of the proposed tabu search. A practical scheduling system combining the proposed mathematical model and the tabu search heuristic has been developed and tested on instances collected from practical production data. The experimental results show that the proposed mathematical model and the tabu search heuristic outperform both the current manual scheduling method and the traditional serial method. [ABSTRACT FROM AUTHOR]

Closed-loop control of an integrated dual-inverter to drive two permanent magnet motors independently is presented and evaluated experimentally. By utilizing the neutral point of the three-phase main motor, only two inverter legs are needed for the two-phase auxiliary motor. A modified field oriented control scheme for this integrated inverter was introduced and employed in real-time control. Experimental results show that the inverter is able to control two drives independently. An integrated, component-count-reduced drive is achieved. [ABSTRACT FROM AUTHOR]

• Webuild a black box operation optimization model for the BOF steelmaking process wherean optimization is integrated into a data analytics model. • We design a DFO algorithm based on trust region framework for solving the operation optimization problem. • In the proposed DFO algorithm, quadratic interpolation and sparse modeling are combinedto determine the surrogate model. Mastering the smelting status and making the material adding strategy are important and challenging issue in the basic oxygen furnace (BOF) steelmaking process. Unfortunately, complex physicochemical reaction with multi-input and multi-output makes the first-principles models unusable. To this end, a black box operation optimization (BBOO) problem integrated black box data analytics modeling and optimization is abstracted from the BOF steelmaking process. Moreover, a derivative free optimization (DFO) method based on the trust region framework is developed to solve the operation optimization problem. In the proposed algorithm, we integrate quadratic interpolation and sparse modeling to construct the surrogate model. Wherein, l 0 -norm optimization, which is solved through converting it into a convex optimization problem with introducing the slack variables, is used to determine the sparse surrogate model. Finally, we apply the proposed DFO algorithm and other DFO algorithms to solve the BBOO problem in BOF steelmaking process. The results demonstrate that the proposed method can be an alternative for providing solutions and insights of material addition in BOF process and the proposed algorithm is comparable. [ABSTRACT FROM AUTHOR]

In the iron and steel industry, the hardness of steel strips is one of the key performance indicators to evaluate strip quality and guide production for the continuous annealing production line (CAPL). However, the hardness cannot be measured online in the actual production process. Consequently, the precise prediction of the strip hardness based on practical data becomes one of the key tasks during production. In this article, a multiobjective sparse nonlinear ensemble learning with evolutionary feature selection (MOSNE-EFS) method is proposed, which is data-driven modeling of the soft sensor. The method mainly consists of two stages: 1) the construction of individual learners based on multiobjective feature selection learning (MOFSL) and 2) the selection and ensemble of individual learners based on sparse nonlinear ensemble learning via differential evolution (SNEL-DE). The final ensemble model obtained by SNEL-DE is used as the prediction model for strip hardness in CAPL. The proposed method is evaluated with industrial production data. Experimental results indicate that the two strategies, i.e., evolutionary feature selection and sparse nonlinear ensemble, are effective in improving the accuracy and robustness of the prediction model, and further comparison results demonstrate the superiority of the MOSNE-EFS model over the other existing methods. Note to Practitioners—Many quality metrics in the iron and steel industry cannot be online checked, which causes great difficulties in process monitoring, control, and operation optimization. The proposed multiobjective sparse nonlinear ensemble learning with evolutionary feature selection method can help practitioners to construct quality prediction models of many other similar production lines, such as hot rolling and cold rolling, and thus, better process monitoring, control, and optimization of product quality can be achieved. [ABSTRACT FROM AUTHOR]

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. Note to Practitioners—Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems. [ABSTRACT FROM AUTHOR]

We study the effects of higher labor costs on the adoption of industrial robots by Chinese firms. We use the number of robots imported by firms from the customs data to measure the adoption of robots. To address endogeneity concerns, we focus on the effects of higher minimum wages from contiguous city pairs that straddle provincial borders. We find negligible effects of minimum wages on firms' robot adoption over 2004–2007, but much larger and statistically significant effects over 2008–2012. For a 10% increase in the minimum wage over 2008–2012, the probability of a firm adopting robots increases by 0.11 percentage points, compared to an average adoption probability of 0.19% over the same period. The effects of higher minimum wages on robot adoption are stronger for firms with higher productivity, those in the coastal region, those with private ownership, and those from skilled-labor-intensive industries. [ABSTRACT FROM AUTHOR]

Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature. [ABSTRACT FROM AUTHOR]

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use. Note to Practitioners—Bridge cranes are commonly used to move heavy items in manufacturing and logistics systems. Generally, more than one crane runs on a common track. The latest versions of such cranes, such as those used in slab yards in the steel industry, can hold two items simultaneously. Operations scheduling of multiple double-load cranes involves the assignment of tasks to the cranes, the combination of tasks to double-load operations, and sequencing of the tasks, considering the noncrossing constraint between cranes. Effective solution of this complex problem can help to fully utilize the crane capability, increase productivity, and reduce energy consumption. This article models this problem and develops a heuristic solution that combines differential evolution (DE) and DP. Experiment results show that the algorithm is effective and efficient for practical use in slab yards. It may also be applicable to other systems using similar cranes. [ABSTRACT FROM AUTHOR]

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is difficult to establish a precise process model for the OOPFP. In this paper, firstly, a novel hybrid modeling method combining temperature mechanism with data analytics is proposed to map the relationship of the temperature variables in the furnace process more accurately. The main idea of the hybrid modeling is to compensate for the deviation of original temperature mechanism model through the least squares support vector machine. Then, an improved differential evolution (DE) with feasible region dynamic adjustment strategy and population size gradual shrinking strategy is proposed to solve the OOPFP under hybrid model. Finally, extensive numerical experiments are carried out to demonstrate the effectiveness and accuracy of the hybrid model, and evaluate the performance of the improved DE on solving OOPFP. Experimental results show that the hybrid model is more precise than the original mechanism one, and the proposed DE outperforms other representative algorithms, respectively. [ABSTRACT FROM AUTHOR]