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Perfluorooctanoic acid (PFOA) is a widespread synthetic persistent organic pollutant that may enrich along the food chain and affect the growth, development, reproduction, and lipid metabolism of aquatic organisms, particularly the benthic organisms. However, the toxic effects of PFOA on the half-smooth tongue sole Cynoglossus semilaevis, a commercial benthic fish in China, have rarely been reported. Because juvenile fish are sensitive to environmental pollutants, in the present study, histological assessment and tran-scriptome sequencing were performed to determine the short-term impact of PFOA on juvenile half-smooth tongue soles. Histological analysis showed that PFOA exposure caused hepatocyte rupture, intestinal villi breakage, increased goblet cell count, and brain abnormal. Transcriptome results showed that some interesting signaling pathways, such as glycolysis/gluconeogenesis, PPAR signaling pathway and GABAergic synapse signaling pathway, were enriched after PFOA exposure. In addition, some metabolic, immune and neural genes were differentially expressed, which including ependymin, hbb1-like and gad 1, and they were up-regulated after 14 days of exposure. Transcriptome results also indicated that half-smooth tongue sole might improve energy metabolism in response to PFOA toxicity after 7 days of exposure. These findings provide a basis for studying the ecological effects of PFOA on marine benthic fishes. [ABSTRACT FROM AUTHOR]

This paper deals with a parallel machine scheduling problem with linearly increasing energy consumption cost. Maintenance activities are considered in the problem. After maintenance, the machine energy consumption cost returns to the normal level. Thus, an important decision is how to determine a reasonable number of maintenance activities to enable a significant tradeoff between the maintenance cost and the energy consumption cost. We define the jobs processed between two adjacent maintenance activities as a batch since the job processing cannot be interrupted. A further decision is how to batch the jobs. To solve the investigated problem, we first study a special case where there is only one single machine. A heuristic approach is proposed to solve the single machine scheduling problem. Then, we present a variable neighborhood search (VNS) algorithm for general cases, where the heuristic approach for the single machine case is intergrated. Extensive computational experiments are conducted and the results show that the proposed VNS algorithm is superior to artificial bee colony (ABC) algorithm, genetic algorithm (GA), ant colony optimization (ACO) algorithm, Tabu search (TS)algorithm, and greedy randomized adaptive search procedure (GRASP) algorithm. [ABSTRACT FROM AUTHOR]

Maintenance cost of aircraft engine is an important component of aircraft operation cost. The prediction of aircraft engine maintenance cost can provide decision support for airline to make reasonable maintenance plan and maintenance fund management. Considering that the prediction accuracy of engine maintenance cost is not high in the case of small samples, this paper proposes a bootstrapped support vector regression (SVR) prediction method based on improved recursive feature elimination, which realizes the point and interval prediction of engine maintenance cost in aircraft operation phase. First, the recursive feature elimination (RFE) is improved and then combined with SVR to select feature subsets. Second, particle swarm optimization (PSO) algorithm is applied to optimize the improved RFE-SVR model (IRFE-SVR) parameters. Finally, the point and interval estimates are obtained by bootstrapped IRFE-SVR. To demonstrate the performance of the bootstrapped IRFE-SVR, experiments on UCI and a real case study of engine maintenance cost prediction are conducted. The results on UCI and real datasets show that the bootstrapped IRFE-SVR method has high accuracy and reliability. [ABSTRACT FROM AUTHOR]

Multiphoton excited luminescence is of paramount importance in the field of optical detection and biological photonics. Self-trapped exciton (STE) emission with self-absorption-free advantages provide a choice for multiphoton excited luminescence. Herein, multiphoton excited singlet/triplet mixed STE emission with a large full width at half-maximum (617 meV) and Stokes shift (1.29 eV) has been demonstrated in single-crystalline ZnO nanocrystals. Temperature dependent steady state, transient state and time-resolved electron spin resonance spectra demonstrate a mixture of singlet (63%) and triplet (37%) mixed STE emission, which contributes to a high photoluminescence quantum yield (60.5%). First-principles calculations suggest 48.34 meV energy per exciton stored by phonons in the distorted lattice of excited states, and 58 meV singlet-triplet splitting energy for the nanocrystals being consistent with the experimental measurements. The model clarifies long and controversial debates on ZnO emission in visible region, and the multiphoton excited singlet/triplet mixed STE emission is also observed.Here the author study Self-trapped exciton emission for multiphoton excited luminescence in ZnO nanoparticle which until now have not been explored. They provide a reason for the emission enhancement and apply these materials in optical imaging for a proof of concept. [ABSTRACT FROM AUTHOR]

The scheduling of jobs and resources is challenging in semiconductor production and large-scale integrated circuit design. This paper considers a semiconductor manufacturing alliance where there are several manufacturers with limited resources, and the goal is to minimize the makespan by making decisions on resources allocation, jobs assignment, jobs batching, and batches sequencing. The job processing time is investigated based on a convex resource formulation integrated with the deterioration effect. Jobs in a single batch have the same starting and finishing time. The batch setup time is defined by the time-dependent function. Meanwhile, limited resources can be allocated to jobs to improve the production efficiency in each batch. Focusing on settings where all jobs have been assigned to manufacturers, this paper derives some important structural properties. Then, for the case with a single manufacturer, an optimal schedule rule is established to arrange jobs and resources. Furthermore, a Variable Neighborhood Search algorithm based on the Biogeography-Based Optimization is designed to solve the problem, which is proved to be NP-hard. The computational results show that our algorithm can generate more robust and appropriate schedules compared to other algorithms from the literature. [ABSTRACT FROM AUTHOR]

As the only standard of its kind, GB5009.35–2016 provides the determination of water-soluble synthetic colorants in processed grain products with high starch content for the purpose of food safety risk monitoring. However, it's only applicable to candy products and liquid foods as beverages, but not solid grain products. Extraction is a critical and essential step in the overall analytical process for determination. This paper provides an improved method for extraction of synthetic colorants in food products presenting high starch content. The samples were successively extracted with methanol-water (4:6, v/v) containing 2.7% sodium bicarbonate, and the target analytes were purified by solid phase extraction column. The obtained eluent was concentrated in constant volume, separated by ODS-SP C18 column and determined by diode array detector. The limits of detection were in the range of 2.21 ~ 8.62 ng/mL for 6 synthetic colors. The average recoveries at the spiked levels of 10, 30, 50 μg/kg varied in the range of 79.3 ~ 101.4% with RSD (n = 6) around 0.2 ~ 6.7%. The developed sodium bicarbonate based extraction method was successfully applied to speciation analysis of water soluble azo synthetic colorant in starchy food, such as millet, grits, brown rice, rice flour, cornmeal and cornflakes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufacturing company (MC) receives a series of order requests from its customers. Due to the limited production capacity, the MC is not able to fulfill all the orders requested by the customers on time. To satisfy the order demand as much as possible, one of the MC's choices is to establish a collaborative manufacturing platform (i.e., DVMN) that leverages the advantages of a social manufacturing network to expand their production capabilities. However, the increase in production capacity does not mean that all orders from the customers are able to be accepted and produced. In fact, standing at the profit-maximizing point of view, the order requests that do not bring profits to the company need to be rejected at the beginning of planning horizon. For the accepted orders, the MC will produce a part of them by its plants in the DVMN, and the remaining orders will be outsourced to other co-manufacturers in the DVMN. Based on some structural properties, we develop a novel Reduced Variable Neighborhood Search (RVNS) algorithm incorporated multiple random mutations neighborhood structures (NSs-MRM) to determine the composition of the DVMN, and further determine which orders should be rejected, which accepted orders should be processed by the company-owned plants in the DVMN, and which accepted orders should be outsourced to the co-manufacturers in the DVMN. In particular, the processing sequence of the orders produced in the company-owned plants are also determined. Finally, a reasonable composition of the DVMN and corresponding order acceptance as well as order scheduling on each member of the DVMN are given. To evaluate the proposed algorithm, a series of computational experiments are conducted, and the experimental results show that the proposed algorithm outperforms other existing baseline algorithms in solving both small-scale and large-scale problem instances. [ABSTRACT FROM AUTHOR]

This research investigates an integrated inventory and production scheduling problem (IIPSP) in a manufacturer that deals with the perishable goods. The objective is to find an optimal schedule to minimize the sum of inventory cost and production cost. Both single-plant problem and multi-plant problem are investigated in this paper. For the single-plant problem, we prove that it is optimal to arrange the processing of raw materials in descending order of the value of the product of consumption rate and unit inventory cost. For the more complex multi-plant problem, we first prove that it is NP-hard, and then, we propose a hybrid intelligent algorithm to solve it. The experiments show that the proposed algorithm is superior to several other algorithms in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses a serial-batching scheduling problem where the jobs with arbitrary release times are scheduled on parallel machines with the objective to minimize the makespan. The effects of learning and deterioration are considered simultaneously, and each job's actual processing time depends on the sum of previous jobs' processing times and the position of the current job. Each machine can process up to c jobs in the manner of serial batch, i.e., one after another with a setup time for each batch. Structural properties are identified for the special cases of the studied problem. Based on these derived structural properties, we propose a novel hybrid SC-VNS algorithm to solve the studied problem, which combines Society and Civilization (SC) algorithm with Variable Neighborhood Search (VNS). Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The results demonstrate that the proposed hybrid SC-VNS algorithm performs quite better than the compared algorithms in terms of the solution quality and the required running time. [ABSTRACT FROM AUTHOR]

This paper investigates permutation flowshop manufacturing cell scheduling problems with deteriorating jobs and sequence dependent setup times under dominant machines. In the proposed models, we need to make joint decisions on part families sequencing and jobs sequencing within each family. To solve the makespan minimization problem, the structural properties of the optimal solutions are derived, based on which an optimization algorithm is developed. Then, we consider the total completion time minimization problem and propose a useful lemma for the optimal solutions. Finally, we discuss two special cases of the problem and propose optimization algorithms to solve them respectively. [ABSTRACT FROM AUTHOR]

This paper investigates an integrated production and assembly scheduling problem with the practical manufacturing features of serial batching and the effects of deteriorating and learning. The problem is divided into two stages. During the production stage, there are several semi-product manufacturers who first produce ordered product components in batches, and then these processed components are sent to an assembly manufacturer. During the assembly stage, the assembly manufacturer will further process them on multiple assembly machines, where the product components are assembled into final products. Through mathematical induction, we characterize the structures of the optimal decision rules for the scheduling problem during the production stage, and a scheme is developed to solve this scheduling problem optimally based on the structural properties. Some useful lemmas are proposed for the scheduling problem during the assembly stage, and a heuristic algorithm is developed to eliminate the inappropriate schedules and enhance the solution quality. We then prove that the investigated problem is NP-hard. Motivated by this complexity result, we present a less-is-more-approach-based variable neighborhood search heuristic to obtain the approximately optimal solution for the problem. The computational experiments indicate that our designed LIMA-VNS (less is more approach–variable neighborhood search) has an advantage over other metaheuristics in terms of converge speed, solution quality, and robustness, especially for large-scale problems. [ABSTRACT FROM AUTHOR]

Parallel-batching processing and job deterioration are universal in the real industry. Scholars have deeply investigated the problem of parallel-batching scheduling and the problem of scheduling with deteriorating jobs separately. However, the situations where both parallel-batching processing and job deterioration exist simultaneously were seldom considered. This paper studies the parallel-batching scheduling problem with nonlinear processing times on a single machine, and proposes several structural properties and an optimal algorithm to solve it. Based on the above properties and optimal algorithm for the single machine setting, we further study the problem of parallel-batching scheduling with nonlinear processing times under the unrelated parallel machine setting. Since the unrelated parallel machines scheduling problem is NP-hard, a hybrid SFLA-VNS algorithm combining Shuffle Frog Leap Algorithm (SFLA) with Variable Neighborhood Search Algorithm (VNS) is proposed. Computational experiments and comparison are finally conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates a competitive two-agent parallel-batching scheduling problem with aging effect on parallel machines. The objective is to minimize the makespan of agent A with the constraint that the makespan of agent B is no more than a given threshold. Some key structural properties are first identified in two different cases, and based on these structural properties a novel decision tree of scheduling rules is constructed and a heuristic algorithm is designed. Then, an effective hybrid BF-VNS algorithm combining Bacterial Foraging (BF) with variable neighborhood search (VNS) is developed to tackle the studied problem. Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The experimental results indicate that the hybrid BF-VNS algorithm performs quite better than the compared algorithms. [ABSTRACT FROM AUTHOR]

Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments. [ABSTRACT FROM AUTHOR]

This paper studies the bounded parallel-batching scheduling problem considering job rejection, deteriorating jobs, setup time, and non-identical job sizes. Each job will be either rejected with a certain penalty cost, or accepted and further processed in batches on a single machine. There is a setup time before processing each batch, and the objective is to minimize the sum of the makespan and the total penalty. Several useful preliminaries for arranging accepted job with identical size are proposed. Based on these preliminaries, we first investigate a special case where all the jobs are considered to have the identical size, and develop a dynamic programming algorithm to solve it. The preliminaries help to reduce the complexity of the dynamic programming algorithm from O n ! n 2 ∑ i = 1 n w j to O n 2 ∑ i = 1 n w j . For the general problem with non-identical job sizes, we propose a hybrid algorithm combining heuristic with dynamic programming algorithm (H-DP) to obtain satisfactory solutions within reasonable time. Finally, the effectiveness and efficiency of the H-DP algorithm are illustrated by a series of computational experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a parallel-batching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processing time of the batches is equal to the largest processing time of their belonged jobs. Then, the generated batches are assigned to parallel machines to be processed. Motivated by the characteristics of machine maintenance activities in a semiconductor manufacturing process, we take the machine preventive maintenance into account, i.e., the machine should be maintained after a fixed number of batches have been completed. In order to solve the problem, we analyze several structural properties with respect to the batch formation and sequencing. Based on these properties, a hybrid BRKGA-DE algorithm combining biased random-key genetic algorithm (BRKGA) and Differential Evolution (DE) is proposed to solve the parallel-batching scheduling problem. A series of computational experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates an information security game between two competitive firms in a market consisting of loyal customers and switchers. The switchers are classified into unaggressive switchers and aggressive switchers based on whether they always transact with the more secure firm. We find that the switcher type plays a significant role in affecting firms' information security decisions. Firms can achieve pure strategy Nash equilibrium in the unaggressive case while no pure strategy Nash equilibrium exists in the aggressive case. Instead, a mixed strategy Nash equilibrium in the aggressive case is obtained. Our analyses show that firms will acquire more profits in the unaggressive case compared to that in the aggressive case when they determine their information security levels individually. Whereas, when they make their information security decisions jointly, the profits in the unaggressive case will be smaller than that in the aggressive case. Furthermore, we find that the loyal customer rate has different impacts on firms' profits in Nash equilibrium and optimal solution for both the unaggressive case and the aggressive case. At last, two contracts are proposed to help firms coordinate their information security strategies when they make individual decisions. [ABSTRACT FROM AUTHOR]

In this work, microstructural changes during creep of 9Cr-1Mo steel were characterized using ultrasonic and hardness measurements. The creep testing of this steel was firstly performed at 873 K under the initial stresses of 160 and 175 MPa, respectively. Then, eight specimens under each stress level were prepared by the interrupted tests to simulate various creep states. Subsequently, two parameters of ultrasonic attenuation and velocity were introduced by the ultrasonic testing. Experiment results showed that regardless of the stresses, the ultrasonic attenuation experiences a peak at about 20% of the creep rupture time and a minimum value at about 50%, showing a strong correlation between ultrasonic attenuation and the creep life fraction. In addition to a slight increase during the primary creep, the ultrasonic velocity exhibits a significant change during the tertiary creep. Based on the analysis of microstructure changes, it indicated that the ultrasonic attenuation is significantly sensitive to the dynamic changes of the dislocation. In contrast, the velocity is mainly related to the evolution of precipitates. The Vickers hardness decreases monotonously during the creep process and presents a linear relation with the inverse of average diameter of M23C6 carbides during the secondary and tertiary creep. Accordingly, it suggested that the present ultrasonic and hardness measurements provided a convenient tool to evaluate the microstructure evolution during creep degradation of heat-resistant steels. [ABSTRACT FROM AUTHOR]

This paper introduces the serial batching scheduling problems with position-based learning effect, where the actual job processing time is a function of its position. Two scheduling problems respectively for single-machine and parallel-machine are studied, and in each problem the objectives of minimizing maximum earliness and total number of tardy jobs are both considered respectively. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on the serial-batching machine. We take some practical production features into consideration, i.e., setup time before processing each batch increases with the time, regarded as time-dependent setup time, and we formalize it as a linear function of its starting time. Under the single-machine scheduling setting, structural properties are derived for the problems with the objectives of minimizing maximum earliness and number of tardy jobs respectively, based on which optimization algorithms are developed to solve them. Under the parallel-machine scheduling setting, a hybrid VNS-GSA algorithm combining variable neighborhood search (VNS) and gravitational search algorithm (GSA) is proposed to solve the problems with these two objectives respectively, and the effectiveness and efficiency of the proposed VNS-GSA are demonstrated and compared with the algorithms of GSA, VNS, and simulated annealing (SA). This paper demonstrates that the consideration of different objectives leads to various optimal decisions on jobs assignment, jobs batching, and batches sequencing, which generates a new insight to investigate batching scheduling problems with learning effect under single-machine and parallel-machine settings. [ABSTRACT FROM AUTHOR]

This paper investigates a single machine serial-batching scheduling problem considering release times, setup time, and group scheduling, with the combined effects of deterioration and truncated job-dependent learning. The objective of the studied problem is to minimize the makespan. Firstly, we analyze the special case where all groups have the same arrival time, and propose the optimal structural properties on jobs sequencing, jobs batching, batches sequencing, and groups sequencing. Next, the corresponding batching rule and algorithm are developed. Based on these properties and the scheduling algorithm, we develop a hybrid VNS-ASHLO algorithm incorporating variable neighborhood search (VNS) and adaptive simplified human learning optimization (ASHLO) algorithms to solve the general case of the studied problem. Computational experiments on randomly generated instances are conducted to compare the proposed VNS-ASHLO with the algorithms of VNS, ASHLO, Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The results based on instances of different scales show the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We study a differential game of information security investment and information sharing in a market consisting of n complementary firms. Two game approaches, the non-cooperative game and the totally cooperative game, are employed to investigate the steady state strategy of each firm. Under certain conditions, a unique steady state can be obtained for both games. We find that the steady state security investment and information sharing level are not always less in the non-cooperative game than that in the totally cooperative game. In addition, some theoretical analyses are made on the impacts of the complementarity degree and industry size on firms’ steady state strategies for both games. Finally, some numerical experiments are conducted to give some insights related to the instantaneous profit in the steady state. It can be found that a firm will obtain more instantaneous profit in the steady state of the totally cooperative game than that of the non-cooperative game, which emphasizes the importance of coordinating strategies. The effects of the complementarity degree and industry size on the instantaneous profits in the steady state are also obtained through the numerical experiment results. [ABSTRACT FROM AUTHOR]

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability. [ABSTRACT FROM AUTHOR]

This article considers the single-machine serial-batching scheduling problem with a machine availability constraint, position-dependent processing time, and time-dependent set-up time. The objective of this problem is to make the decision of batching jobs and sequencing batches to minimize the makespan. To solve the problem, three cases of machine non-availability periods are considered, and the structural properties of the optimal solution are derived for each case. Based on these structural properties, an optimization algorithm is developed and an example is proposed to illustrate this algorithm. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing and remanufacturing systems have become a topic of considerable interest in the advanced manufacturing industry due in part to the profit and cost saving by reusing remaufacturable parts in the end-of-use products. In this paper, we investigate a production planning problem in such a hybrid system with the integration of resource capacity planning that is shared by both manufacturing and remanufacturing processes. Due to the uncertain nature in practice, both new and remanufactured product demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the historical data, we formulate the stochastic aggregate production planning problem as a mixed integer linear programming (MILP) model. Based on the Lagrangian relaxation (LR) technique, the MILP model is decomposed into four sets of sub-problems. For these sub-problems, four heuristic procedures are developed, respectively. Then, a LR based heuristic for the main problem is proposed and further tested on a large set of problem instances. The results show that the algorithm generates solutions very close to optimums in an acceptable time. At last, the impact of demands uncertainty on the solution is analyzed by the sensitivity analysis on a number of scenarios. [ABSTRACT FROM AUTHOR]

The emerging information technology of internet of things (IoT) has caught much attention from both theoretical research and production practice. The application of IoT in manufacturing industry can dramatically improve business process efficiency and promote organizational performance. However, firms tend to hesitate in the investment decisions, which poses great challenges to the IoT application progress. Besides the investment nature of long cycle and high risk, the 'organizational factors' are also non-ignorable. A general organizational change model is first refined according to the organizational change of Jianghuai Auto Company when applying IoT. Based on the binomial lattice tree framework, we propose a two-stage real options model to solve the optimal investment problems in both stages of internal pilot and product life-cycle management (PLM) expansion. Investment costs and future benefits are considered to investigate the promoting effect of organizational factors in project implementation. This paper demonstrates the internal causes and significances of organizational change for IoT development project, which is also theoretically meaningful for explanation of real options approach in organizational area. [ABSTRACT FROM AUTHOR]

This paper considers the cutting stock problem with frustum of cone bars. A multiple objective optimization model is established by taking into account trim loss, the number of cutting patterns and usable leftovers. A decision-making method for solving this cutting stock problem is designed. First, an improved non-dominated sorting heuristic evolutionary algorithm is developed for generating the Pareto non-dominated solutions. Then the weights of the objectives are calculated by combining the subjective methods (subjectively determined by the decision maker) and objective methods (objectively determined by numerical computing). Finally, a multi-attribute decision making method is used for choosing a cutting plan from the Pareto non-dominated solutions. Computational results indicate that the method proposed is feasible. [ABSTRACT FROM AUTHOR]

This paper investigates coordinated scheduling on uniform parallel batch machines with batch transportation. Jobs are characterized by different processing time and sizes, and they are first delivered to manufacturers in batches and then processed on the uniform parallel batch machines. The manufacturers are distributed in different geographic zones and there exists one parallel batch machine in each manufacturer. A mixed integer programming model is developed for the studied problem, and its objective is to minimize the makespan. In addition, the structural properties of the problem are analyzed. A hybrid algorithm combining the merits of discrete particle swarm optimization (DPSO) and genetic algorithm (GA) is proposed to solve this problem. In the hybrid algorithm, a heuristic and a local search strategy are introduced. Finally, computational experiments are conducted and the results show that the proposed hybrid algorithm can effectively and efficiently solve the problem within a reasonable time, particularly in large-scale instances. [ABSTRACT FROM AUTHOR]

In this paper, we study a scheduling model in which the features of deteriorating jobs, serial batches, multiple job types, and setup times are considered simultaneously. In this proposed model, the jobs of each type are first partitioned into serial batches, and then all batches of different job types are processed on a single serial-batching machine. The actual job processing time is an increasing function of its starting time, and the setup time of the batches is sequence-dependent, i.e., setup time is required only when a new batch is processed first on the machine or immediately after a batch belonging to another job type. We develop optimization algorithms to solve the makespan minimization problem, the maximum tardiness minimization problem, the maximum lateness minimization problem, and the maximum earliness minimization problem, respectively. We also propose optimization algorithms to solve the problem of minimizing the number of tardy jobs under a certain agreeable condition. Finally, we discuss two special cases of the total completion time minimization problem and develop optimization algorithms to solve them. [ABSTRACT FROM AUTHOR]

This paper deals with serial-batching scheduling problems with the effects of deterioration and learning, where time-dependent setup time is also considered. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on a single serial-batching machine. The actual job processing time is a function of its starting time and position. In addition, a setup time is required when a new batch is processed, and the setup time of the batches is time-dependent, i.e., it is a linear function of its starting time. Structural properties are derived for the problems of minimizing the makespan, the number of tardy jobs, and the maximum earliness. Then, three optimization algorithms are developed to solve them, respectively. [ABSTRACT FROM AUTHOR]

Many dynamic events exist in real manufacturing systems, such as arbitrary machine breakdowns and dynamic job arrivals, which makes the scheduling problem even more complicated. In this paper, we address a serial-batching scheduling problem with the above dynamic events. Jobs need to be processed on the serial-batching machines of two manufacturers and then transported by vehicles to a customer for further processing. The objective of the scheduling problem is to minimize the makespan, and the problem is proved to be strongly NP-hard. Some structural properties and a lower bound of the problem are also proved or derived. On the basis of job arrival times, we divide the problem into two phases and propose different rules regarding these two phases. Based on these properties and rules, a heuristic algorithm is developed to solve the problem and its worst case performance is analyzed. The heuristic algorithm is tested on a large set of randomly generated problem instances, and the relative gaps between the found lower bound and the solutions of the proposed heuristic algorithm are reported. The experimental results illustrate the high efficiency and effectiveness of the proposed heuristic algorithm compared with other four classic approaches. [ABSTRACT FROM AUTHOR]

During the last decades many companies have to retrieve and treat their end-of-use products when products leave their end users in order to contribute to environmental protection and avoid defiance of relevant legislations. The utilization of returned products in a proper way is the best choice to conform to the above requirement, and save the cost in the production and maintenance process as well. With the development of information technologies, especially the internet of things used in product life cycle data management, the product life cycle information can be tracked, detected, stored and used in the returned product process. In this paper, an integer linear programming model is presented based on the detail product information for the optimization of procurement, manufacturing, recovering and disposal decisions. The model considers three recovery options, several value levels of returns and the value deterioration during the processing time period in order to satisfy the products and components demand in the production planning. A numerical example and sensitivity analysis are used to illustrate the performance and applicability of the model. [ABSTRACT FROM AUTHOR]

Recovery of the end-of-use products has become a topic of considerable interest in the advanced manufacturing industry due in part to uncertainties in the quality and volume of product returns. The Internet of Things (IoT) that enables the tracing, detecting, storing, and analyzing the product life cycle data for each individual item can mitigate or eliminate these uncertainties. In this paper, an integrated three-stage model is presented based on IoT technology for the optimization of procurement, production and product recovery, pricing and strategy of return acquisition. The remaining value is used to measure the return condition. The model considers three recovery options related to refurbishing, component reuse and disposal, and the value deterioration for satisfying the product demand in each stage of product life cycle (PLC). A novel particle swarm optimization (PSO) algorithm based on two heuristic methods is proposed to solve the problem. A numerical example and sensitivity analysis are used to illustrate the performance of both algorithm and applicability of the model. [ABSTRACT FROM AUTHOR]

This paper investigates a scheduling model with certain co-existing features of serial-batching, dynamic job arrival, multi-types of job, and setup time. In this proposed model, the jobs of all types are first partitioned into serial batches, which are then processed on a single serial-batching machine with an independent constant setup time for each new batch. In order to solve this scheduling problem, we divide it into two phases based on job arrival times, and we also derive and prove certain constructive properties for these two phases. Relying on these properties, we develop a two-phase hybrid algorithm (TPHA). In addition, a valid lower bound of the problem is also derived. This is used to validate the quality of the proposed algorithm. Computational experiments, both with small- and large-scale problems, are performed in order to evaluate the performance of TPHA. The computational results indicate that TPHA outperforms seven other heuristic algorithms. For all test problems of different job sizes, the average gap percentage between the makespan, obtained using TPHA, and the lower bound does not exceed 5.41 %. [ABSTRACT FROM AUTHOR]

This paper investigates the scheduling problems of a single serial-batching machine with independent setup time and deteriorating job processing times. With the assumption of deteriorating jobs, the job processing times are described by an increasing function of their starting times. All the jobs are first partitioned into serial batches and then processed on a single serial-batching machine. Before each batch is processed, an independent constant setup time is required. Two optimization algorithms are proposed to solve the problems of minimizing the makespan and the total number of tardy jobs, respectively. Specifically, for the problem of minimizing the total completion time, two special cases with the smallest and the largest number of batches are studied, and an optimization algorithm is also presented for the special case without setup time. [ABSTRACT FROM AUTHOR]

With the concept of Internet of Things (IoT) being proposed, more and more fields are involved in order to improve their development. This paper studies basic architecture of IoT for optimizing manufacture process of tokamak machine. A brief architecture has been established based on existing services architecture for other field. On this basis, general strategy has been designed and it will improve machining and inspection processes. In addition, intelligent manufacturing system for tokamak machine has been proposed. In the case of application of IoT for manufacturing tokamak machine, it could effectively save cost and improve the quality of products. [ABSTRACT FROM AUTHOR]

Nuclear fusion is one of the most important ways to resolve human energy shortages issues. Due to risk of ionizing radiation, the nuclear fusion reactor will relay on remote handling maintenance to achieve its scientific mission. Remote handling maintenance system is required to provide more reliable and powerful remote handling tools for increasing the efficiency of maintenance. With the development of technology, Internet of Things (IoT) has been became an important way to increase the maintenance efficiency. The basic structure of fusion machine and its functions has been introduced in this article. Besides, the maintenance system for fusion reactor has been described. Finally, IoT-based maintenance process for fusion reactor remote handling system has been established and it will greatly improve the maintenance efficiency and save the maintenance cost. [ABSTRACT FROM AUTHOR]