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Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker's learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

This paper is concerned with the scheduling problem in a new-type seru production system by consideration of DeJong's learning effect to minimise the total weighted completion time, so as to achieve efficiency, flexibility, and fast responsiveness to cope with the current volatile market. A combinatorial optimisation model is constructed and then reformulated to a binary quadratic assignment program. Accordingly, after presenting the necessary and sufficient condition for the locally optimal solution, a tabu search with strategic oscillation based on 3-opt as a diversification strategy is designed as the solution approach. A set of test problems are generated, and computational experiments with large-scale cases are made finally. The results indicate that the proposed heuristic algorithm is promising in solving seru scheduling problems and has a good performance in term of solution quality, efficiency, and scalability. [ABSTRACT FROM AUTHOR]

In practical manufacturing systems, the job processing time usually varies with the performance change of manufacturing resources, among which the learning and deteriorating effects are typical characteristics. Due to the interests from both academic exploration and industrial innovation, the research on scheduling problems with these effects is abundant and diverse. However, some studied problems need to be strengthened in combination with realistic production scenarios. This paper provides a concise guide to scheduling problems with these effects, giving a comprehensive review and critical hints for future research. A novel classification scheme is designed based on four levels of different domains, i.e. effects, processing ways, processing time functions, and manufacturing environments. Based on this scheme, the scheduling problems are first distinguished into three categories: learning effects, deteriorating effects, and combined effects. In each category, models are then refined along three lines: general processing way, batch scheduling, and group scheduling. Combined with the attributes of actual processing time functions and manufacturing environments, the evolvement of related scheduling models and a critical analysis on the proposed algorithms are well analysed. Afterwards, the research gaps are revealed and the research directions are indicated from the perspectives of practical applications, time functions, and designed algorithms. [ABSTRACT FROM AUTHOR]

Severe environmental problems have made green scheduling an emerging research hotspot. In this paper, a permutation flow shop energy-efficient scheduling problem that considers multiple criteria is investigated. The aim is to find the optimal job processing sequence and conveyor speed that minimise both the makespan and total energy consumption. In addition to two types of common criteria, namely, machine-based criterion (i.e. sequence-dependent setup time) and energy-based criteria (including both the transportation time control strategy and machine shutdown strategy), a human-based criterion (i.e. a position-based learning effect) is introduced. A bi-objective programming model is developed, and a multi-objective iterated greedy (MOIG) is designed to reach the Pareto front of the model. Considering that there are two types of decisions in the model (i.e. job sequence and conveyor speed), two algorithm alternatives are designed based on the job sequence and conveyor speed, respectively. Meanwhile, an acceptance criterion with advantages in terms of the convergence speed and solution diversity is proposed. Existing algorithms, including NSGA-II and MOEA/D, are introduced to evaluate the performance of the MOIG. The results emphasise the efficiency of the MOIG. Overall, the model and MOIG effectively improve the green efficiency of enterprises and can reasonably control operating costs. [ABSTRACT FROM AUTHOR]

Time, price, and servicing are three important factors which affect the demand for any product. In a multi-period inventory model, different pricing in different periods may have a positive impact on demand. Offering discounts to customers during a shortage period is an efficient way to reduce lost sales. To address all these issues, a multi-period inventory model is developed and analyzed in this paper to obtain optimal pricing and replenishment periods where the market demand depends on price, time and service level provided by the retailer. The time horizon is fixed; the replenishment cycles are of equal length, but the shortage period may vary in different cycles. The retailer is allowed to charge different prices in different periods and provide discounts if someone places demand during a shortage period. Learning effects in holding and ordering costs are considered. Under a dynamic pricing framework, this study examines service improvement through learning and price discounts during shortages as an incentive for customers. It demonstrates that discounting indeed helps to produce more revenue when the back-order rate is low, and the service level is critical in generating demand when the stock-out period is lengthy, revealing a critical relationship between shortage periods and service level. A numerical experiment is performed to investigate the optimal policy in different situations and obtain some managerial insights. [ABSTRACT FROM AUTHOR]

This study reviews empirical studies on applying Multimodal Interactions in Virtual Reality (MIVR) environments in education. We selected 25 papers and reviewed them based on (1) educational research domains, (2) MIVR modalities, (3) MIVR devices/tools, and (4) the methodology and major findings. The results suggest that MIVR focused on language and mathematical science. The common modalities were Visual + Haptic and Visual + Auditory. The mature haptic feedback devices and Virtual Reality (VR) app have advantages in devices. In methodology and key findings, the participants were primarily from middle school and university; the duration of the studies commonly less than one day, and the number of participants were mostly less than 50; the research design more used quasi-experimental; the research methods were quantitative and mixed; the common data collection approaches were tests and questionnaires; scholars evaluated learners’ knowledge, skills development, learning experience, learning attitude, and opinion about the MIVR system. The advantages of MIVR in education include natural and real learning experiences, flexibility and efficiency in obtaining information etc., disadvantages were availability issues, cognitive load and personalized insufficiency. [ABSTRACT FROM AUTHOR]

The rapid advancement of Generative Artificial Intelligence Technology has increasingly drawn attention to its potential applications in the educational sector. This study aims to investigate the effects of Situational Interactive Teaching, facilitated by generative artificial intelligence, on students' learning outcomes and flow experiences. A series of experiments were designed to compare the performance of a Generative Artificial Intelligence-supported Situational Interactive Teaching Method with a Traditional Video Interactive Teaching Method. Data was collected using research tools such as questionnaires and test questions to assess students' cognitive levels, learning effectiveness, flow experiences, and subjective evaluations during the instructional process. The analysis revealed distinct differences between the two teaching methods. The findings suggest that compared to traditional teaching methods, Generative Artificial Intelligence-supported Situational Interactive Teaching significantly improves students' learning outcomes in cognitive, skill, and affective domains, while also enhancing flow experiences. These positive effects are not limited by individual student differences, indicating broad applicability. Furthermore, this teaching approach can foster a positive feedback loop between learning effectiveness and flow experience. In conclusion, this study confirms the effective application of generative artificial intelligence technology in legal education, providing empirical evidence for the promotion of this innovative teaching model in the educational field. [ABSTRACT FROM AUTHOR]

In a response to changes in customer requirements and environmental dynamics, product lifecycles have become shorter and shorter over the last decades. As a result, production ramp-ups have become more frequent in many industries, and they now often account for a significant share of the entire product lifecycle. Due to the prominent role production ramp-ups play in the lifecycle of a product, efficient production ramp-ups are now an important determinant of business success. This work proposes a mathematical model for managing production ramp-ups in a serial multi-stage production system. In the scenario considered here, both the productivity of workers and demand increase over time until a steady-state phase is reached at the end of the ramp-up. The model proposed in this paper supports the assignment of workers to the different stages of the production system and the balancing of production and demand to ensure a smooth transition from the production ramp-up to steady-state production. The model is analysed in numerical experiments to illustrate its potential for managing the production ramp-up. Our experiments show that high learning rates can have drawbacks in terms of large inventories if learning is not aligned with demand growth and across production stages. [ABSTRACT FROM AUTHOR]

Feed-in tariffs (FITs) are among the most favoured policies with which to drive the deployment of renewable energy. This paper offers insights into quantifying dynamic FITs to realise the expected installed capacity target with minimum policy cost under uncertainties of renewable intermittence and technology learning. We incorporate real options and use stochastic dynamic programming to model the strategic behaviour between policy-maker and investor and extend the one-time investment decision described by Farrell et al. [2017. 'Specifying an Efficient Renewable Energy Feed-in Tariff.' The Energy Journal 38: 53–75] to multiple-period decisions. An approach that combines binary tree scenario generation and a least squares Monte Carlo method is used to numerically identify the optimal FITs plan in practice. China's offshore wind power investment is used as a case study to investigate the relationships among the optimal dynamic FITs level, the total policy cost, the expected capacity target, and the learning effect. The simulation results demonstrate that our proposed dynamic FITs can track the changes in technology learning well and that they can avoid the inefficiency of fixed FITs in stimulating technology adoption in the initial periods, along with overpayment by the policy-maker. [ABSTRACT FROM AUTHOR]

In recent years, more and more technical tools have been introduced into various museums, bringing new opportunities and challenges for the research of museum learning. Whether the application of these technologies can effectively improve the effects of museum learning has become a major problem perplexing museum learning researchers. In this paper, a meta-analysis method is used to analyze and evaluate the 42 experimental or quasi-experimental studies on the technology application in the field of museum learning in the past decade. The results show that the technology application has a moderate or higher effect on museum learning, moreover, the age of learners, the type of technology, the type of museum and the visiting pattern of museum will significantly affect the effectiveness of technology on the museum learning. Therefore, the museum institutions can learn from excellent cases to accelerate the application of museum technology, give priority to the construction of digital resources, continue to promote the specialisation of museum technology application, and better play the role of technology in museum education. [ABSTRACT FROM AUTHOR]

As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation. [ABSTRACT FROM AUTHOR]

We consider a two-machine no-wait permutation flow shop common due date assignment scheduling problem where the processing time of a job is given as a function of its position in the sequence and its amount of resource allocated to this job. The common due date (CON) assignment method means that all the jobs are given a common due date. We need to make a decision on the common due date, resource allocation and the sequence of jobs to minimise total earliness, tardiness, common due date cost and total resource cost. We show that the problem remains polynomially solvable under the proposed model. [ABSTRACT FROM AUTHOR]

Due to the low volume high variety strategies of manufacturing companies, manual assembly operators have a much larger cognitive load than before. The expertise of the operators must be kept up to date at any time. Since the high investment and low flexibility of a real setting to perform a manual assembly training, a virtual replica is introduced in many cases. The aim of this paper is to study the effect of an elementary virtual training for manual assembly tasks. In literature, different studies on the topic can be found; nevertheless, a comparison between the different studies is not possible due to diverse evaluation methods and descriptions. A benchmark for a uniform evaluation of virtual training systems is presented and applied to this experiment. Two groups were submitted to a number of manual assembly tasks. The test group got a virtual training period in advance.Asignificant learning transfer during that training period was observed. When the first assembly of the reference group is counted as a real training, no significant difference can be found between the virtual and real training. The outcomes of this experiment will be used in future work to compare different virtual training systems and influential factors such as the assembly complexity. Furthermore, the application of virtual training to manual assembly in a mixed-model environment and its industrial usability are topics that still need to be studied. [ABSTRACT FROM AUTHOR]

This paper deals with a single-machine scheduling problem with a time-dependent learning effect. The goal is to determine the job sequence that minimise the number of tardy jobs. Two dominance properties, two heuristic algorithms and a lower bound to speed up the search process of the branch-and-bound algorithm are proposed. Computational experiments show that the branch-and-bound algorithm can solve instances up to 18 jobs in a reasonable amount of time, and the proposed heuristic algorithm MFLA performs effectively and efficiently [ABSTRACT FROM PUBLISHER]

In a variety of discrete manufacturing environments, it is common to experience a nonlinear production rate. In particular, our interest is in the case of an increasing production rate, where learning creates efficiencies. This leads to greater output per unit time as the process continues. However, the advantages of an increasing production rate may be offset by other factors. For examples, JIT policies typically lead to smaller lot sizes, where the value of an increasing production rate is largely lost. We develop a general model that balances the impact of various competing effects. Our research focuses on determining lot sizes that satisfy demand requirements while minimising production and holding costs. We extend our prior work by developing a multi-product, multi-machine method for modelling and solving this class of production problems. The solution method is demonstrated using the production function from the PR#2 grinding process for a production plant in Carlisle, PA. The solution heuristic provides solution times that are on average only 0.22 to 0.55% above optimum as the solution parameters are varied and the ratio of heuristic solution times to optimal solution times varies from 18.16 to 14.15%. [ABSTRACT FROM AUTHOR]

This is a study of a earliness and tardiness sequencing and scheduling problem on a single machine. The processing time for jobdepends on its position in a sequence, its starting time, and its allocation of non-renewable resource. For three due date assignment methods i.e. the common due date, the slack due date and the unrestricted due date our goal is to find the optimal schedule, due dates, and resource allocations of jobs. We prove that three versions of the problem can be solved in polynomial time, respectively. [ABSTRACT FROM AUTHOR]

After the Fukushima disaster, the authors analyzed all past core-melt accidents and estimated a failure rate of 1 per 3704 reactor years. This rate indicates that more than one such accident could occur somewhere in the world within the next decade. The authors also analyzed the role that learning from past accidents can play over time. This analysis showed few or no learning effects occurring, depending on the database used. Because the International Atomic Energy Agency (IAEA) has no publicly available list of nuclear accidents, the authors used data compiled by the Guardian newspaper and the energy researcher Benjamin Sovacool. The results suggest that there are likely to be more severe nuclear accidents than have been expected and support Charles Perrow's "normal accidents" theory that nuclear power reactors cannot be operated without major accidents. However, a more detailed analysis of nuclear accident probabilities needs more transparency from the IAEA. Public support for nuclear power cannot currently be based on full knowledge simply because important information is not available. [ABSTRACT FROM AUTHOR]

This paper concerns the total weighted tardiness on single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, 4 (four) heuristic methods are developed to solve real size applications including the size of 1000 jobs. The applied heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Solutions denote that developed heuristics are efficient for the proposed model. Research of this topic shows that, no study exists on the total weighted tardiness problem with learning effect and unequal release dates together asserted in this paper. [ABSTRACT FROM AUTHOR]

This article examines the learning effects of citizen perceptions on satisfaction with public services. Using an experimental method with internet survey data in Michigan, the study observed the relationship between performance minus expectations (PME) and satisfaction became linear after providing concrete information, indicating that citizens' antigovernment recognition can be modified by information. Moreover, the learning effect in counties was larger than in municipalities since citizens tend to clearly recognize services for regional governments. Governments should provide preferential treatment to citizens to improve the quality of cognitive process depending on individuals in different PME conditions across different governance levels. [ABSTRACT FROM AUTHOR]

This article considers the flow shop problem of minimising the total weighted completion time in which the processing times of jobs are variable according to general position weighted learning effects. Two simple heuristics are proposed, and their worst-case error bounds are analysed. In addition, some complex heuristics (including simulated annealing algorithms) and a branch-and-bound algorithm are proposed as solutions to this problem. Finally, computational experiments are performed to examine the effectiveness and efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This research adopts an approach that uses computer simulation and statistical analysis of uncertain activity time, activity cost, due date and project budget to address quality and the learning process with regard to project scheduling. Since the learning process affects the scheduling problem, a Cobb–Douglas multiplicative power model is used to represent the relationship between the dependent variable, which is the standard deviation of activity time, and the independent variables, which are the cumulative trials and the mean of activity time. The mean value and standard deviation are used to randomly generate activity times for project scheduling analysis. Response surface methodology (RSM) is used in order to develop a rationale of the time-cost trade-off problem. The solutions found with RSM are optimised only for a single objective, such as project completion time, total project cost, completion time probability and total cost probability. Thus, multiple objectives for further optimisation become necessary and a limited project budget, restricted completion time, allowable total cost probability and acceptable completion time probability have to be considered at the same time as the learning effect. With response functions from RSM, compromise programming is adopted in order to formulate the proposed project scheduling problem for multi-objective optimisation. [ABSTRACT FROM PUBLISHER]

This note revisits single machine due-date assignment scheduling with job-dependent learning effects and resource allocation. Under the convex decreasing resource consumption function, the problem is to minimize the scheduling cost subject to a constraint on the total weighted resource consumption cost. Firstly, it is shown that the published results are incorrect by a counter-example from the literature. Then the corrected results are provided, and it is shown that this problem can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

This article considers a single-machine due-window assignment scheduling problem based on a common flow allowance (i.e. all jobs have slack due window (SLKW)). We assume that the actual processing time of a job is a function of its position in a sequence (learning effect) and its continuously divisible and non-renewable resource allocation. The problem is to determine the optimal due windows, the optimal resource allocation and the processing sequence simultaneously to minimise costs for earliness, tardiness, the window location, window size, makespan and resource consumption. For a linear or a convex function of the amount of a resource allocated to the job, we provide a polynomial time algorithm, respectively. Some extensions of the problem are also shown. [ABSTRACT FROM PUBLISHER]

This paper concerns with the total weighted tardiness on a single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, four heuristic methods are developed to solve real size applications including the size of 1000 jobs. Proposed heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Results denote that developed heuristics are efficient for the considered problem. Research on this topic shows that no study exists on the total weighted tardiness problem with learning effect and unequal release dates simultaneously tackled in this paper. [ABSTRACT FROM AUTHOR]

This study investigates the effect of learning in fuzziness by considering fuzzy demand in the EOQ model for deteriorating items under a finite time horizon. The crisp equivalent form of the fuzzy objective function is obtained by employing the centroid method. Using calculus, the number of replenishments which optimizes the fuzzy objective function is derived. The model is extended by applying learning in fuzziness and an algorithm is developed to determine the number of replenishments. Numerical illustrations are provided for the model under a crisp, fuzzy and fuzzy-learning environment. Numerical results reveal that the cost is lower with learning in fuzziness than that of without learning in fuzziness. Besides, results indicate that the learning in fuzziness is more effective whenever the parameter has higher impreciseness in the estimation of its value. [ABSTRACT FROM AUTHOR]

We model a nonlinear production process at CTS Reeves, a manufacturing firm in Carlisle, PA, using a linear approximation model and heuristic methods to find a near optimal solution to determining lot sizes when learning effects are present. In a nonlinear manufacturing process, the average time to produce a part varies based upon the lot size, and typically, the average time to produce a part decreases as the lot size increases owing to a learning effect. Our linear approximation model uses discrete time periods to represent production lots. The amount of production is known for any discrete period, and as the length of the period increases, the production amounts increase at the nonlinear rate. The discrete time periods enable a production schedule to be determined that minimises production and holding costs. We build upon our prior method, which successfully addressed the single-product, single-machine environment. In this work, we expand the method to the multiple product and single machine environment. Our method constructs a feasible production schedule with total production and holding costs close to those in the optimal linear approximation model. The viability of the heuristic method is verified with testing on 50, 100, 200, 500, 1500, and 3000 period models. [ABSTRACT FROM PUBLISHER]

Job scheduling and maintenance activities scheduling are two important issues in production management, which are often addressed separately. In this paper, the non-permutation flow shop scheduling problem with learning effects and flexible maintenance activities is studied. Each machine has a number of preventive maintenance activities that should be finished within specific time intervals. The aim is to simultaneously determine the sequence of jobs and the finish time of maintenance activities for minimising the sum of tardiness costs and maintenance costs. A mixed integer linear programming model is proposed to formulate the problem. Owing to the high complexity of the problem, an improvement heuristic method and a hybrid meta-heuristic algorithm based on the simulated annealing algorithm and firefly algorithm is presented to find nearly optimal solutions for medium and large problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the hybrid algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution approaches. [ABSTRACT FROM PUBLISHER]

In the context of return to play assessment following a sports concussion, athletes may have to complete a cognitive task several times within days. The current longitudinal study aimed to examine the practice effect associated with the serial administration of the switch task. We hypothesized that the practice effect would be the greatest between the first and second assessments and that a plateau would be obtained by the third assessment. Forty healthy university students completed the switch task four times at an interval of 48-hour, with half of them doing version A on all visits (AA group), while the others alternated between A and B every other assessment (AB group). For response accuracy, performance generally improved from V1 to V2, and from V2 to V3 (ps ≤.001), irrespective of group. For reaction time, a significant interaction of Visit × Group was observed. Specifically, AA group performance improved from V1 to V2, and from V2 to V3, while the AB group only improved from V2 to V3 (ps ≤.01). Performance improved over the first three assessments and stabilized with no statistically significant change between the third and fourth testing sessions. Although the use of an alternative version has helped reduce the practice effect, it did not help in eliminating it. The current results highlight the need for further examination of the practice effect and its implication in clinical decision-making. Clinicians working in the sports concussion field must take the practice effect into account when they use the switch task for serial testing. [ABSTRACT FROM AUTHOR]

Objectives: To assess are there learning-related improvements in the speech reception thresholds (SRTs) for the Finnish matrix sentence test (FMST) and the Finnish digit triplet test (FDTT) in repeated use over 12 months. Design: Test sessions were scheduled at 0, 1, 3, 6 and 12 months, and each session included five FMST measurements and four FDTT measurements. The within-session and inter-session improvements in SRTs were analysed with a linear mixed model. Study sample: Fifteen young normal-hearing participants. Results: Statistically significant mean improvements of 2.0 dB SNR and 1.2 dB SNR were detected for the FMST and the FDTT, respectively, over the 12-month follow-up period. For the FMST, majority of the improvement occurred during the first two test sessions. For the FDTT, statistically significant differences were detected only in comparison to the first test session and to the first test measurement of every session over the 12-month follow-up. Conclusions: Repeated use of the FMST led to significant learning-related improvements, but the improvements appeared to plateau by the third test session. For the FDTT, the overall improvements were smaller, but a significant within-session difference between the first and consecutive FDTT measurements persisted throughout the test sessions. [ABSTRACT FROM AUTHOR]

This article addresses how to minimize total completion time on a single machine with a position-weighted learning effect. Under non-zero job release dates, a lower bound on the objective function, a number of dominance properties, and an initial solution (i.e. an NEH-based heuristic) are derived, which are used to expedite the elimination process of a branch-and-bound solution procedure. The performance of the branch-and-bound algorithm and the heuristic algorithm are evaluated on a set of randomly generated problem instances. [ABSTRACT FROM AUTHOR]

This article studies a single-machine scheduling problem with DeJong's effect and a machine availability constraint taking the total completion time as the objective function. Firstly, a fully polynomial-time approximation scheme (FPTAS) was given for the problem, and afterwards that FPTAS was extended for the single-machine scheduling problem with deteriorating jobs, DeJong's learning effect and a machine availability constraint. [ABSTRACT FROM AUTHOR]

The influence of learning effects on job scheduling has been studied for years. By considering learning effects, an operator can schedule jobs in a more precise way and improve on the original schedules. In fact, jobs with duplicate contents (i.e., overlapping jobs) do not require as much processing time as do disjoint jobs. However, this phenomenon is seldom discussed in traditional scheduling models. In this study, a parallel-machine scheduling problem with a learning effect and an overlap effect is introduced. The objective is to minimise the total weighted tardiness of jobs whose processing times are influenced by both effects. A branch-and-bound algorithm comprising a lower bound algorithm is developed to generate the optimal schedules. Compared with past research, two main contributions are made. First, a model considering simultaneously both the learning effect and the overlap effect is proposed. Second, an efficient lower bound algorithm accelerating the execution speed is developed. At the end, computational experiments are conducted to show the execution efficiency and cost effectiveness. [ABSTRACT FROM AUTHOR]

In this article, the two-machine no-wait permutation flowshop scheduling problem with common due window assignment, learning effect and resource allocation is considered, wherein learning effect and resource allocation mean that the processing time of a job is a function of its position in a sequence and its amount of resource allocation. According to the common due window assignment method, each job is associated with a common due window, which is a decision variable. The goal is to determine the sequence of jobs, common due window starting time, due window size and resource allocation such that the linear weighted sum of job earliness, tardiness, due window size and resource cost is minimized. This article shows that the scheduling problem can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

This paper uses data on Chinese manufacturing enterprises from 2000 to 2005 to explore the possible causal relationship between importing and firm productivity. By examining firm heterogeneity, we find that importers outperform non-importers on all the major scale and efficiency indicators. With respect to total factor productivity (TFP), importers perform better than firms with no international trade, and bilateral traders perform better than exporters. However, the positive relationship between importing and firm productivity is induced by the learning by importing effect, rather than the self-selection effect. We find no self-selection effect among non-exporters, processing trade exporters, and other exporters, whereas non-exporters and processing trade exporters benefit from importing; processing trade exporters with low TFP in particular benefit from faster TFP growth. No learning by importing effect is found among other exporters. [ABSTRACT FROM AUTHOR]

In studies on automatic scheduling problems, processing times do not differ according to repetition of job or process sequences so it may also be necessary to consider processing times independent from setup times. While considering setup times, the human factor has an important effect on setup, so by the processing of similar tasks frequently worker skills improve and they are able to perform setup at a greater pace. This fact is known as the ‘learning effect’ in the literature. This paper deals with sequence-dependent setup times (SDSTs) hybrid flow shop scheduling with learning effect of setup times for minimising weighted sum of makespan and total tardiness. A mathematical programming model that incorporates these aspects of the problem is developed which belongs to the NP-hard class. Thus, because of the intensive computation, we propose a novel meta-heuristic approach called water flow-like algorithm (WFA) which has the feature of multiple and dynamic numbers of solution agents. Various parameters of the problem and the WFA are reviewed by means of Taguchi experimental design. For the evaluation of the proposed WFA, problem data was generated to compare it against a random key genetic algorithm (RKGA). The results demonstrate the high performance of the WFA with respect to the RKGA. [ABSTRACT FROM PUBLISHER]

The collaboration of vendor and buyer is one of the key factors for successful supply chain management. The most common strategy for a collaborative system is to propose an integrated replenishment plan aimed at maintaining a win-win partnership for both vendor and buyer. The objective of this study is to develop a production and shipment model for a system that incorporates learning effect and deteriorating items and to derive an optimal joint total cost from the integrated perspective of both vendor and buyer. A simple solution procedure is presented to determine the optimal production time and number of deliveries. A numerical example is provided to illustrate the proposed model. A sensitivity analysis is conducted to study the effect of changes in the related parameters on the optimal solution. This paper shows that the proposed integrated model can result in a significant cost reduction as compared with the independent decisions made by either vendor or buyer. [ABSTRACT FROM AUTHOR]

This article addresses single machine resource allocation scheduling problems with learning effects, where learning effects mean job-dependent position-based learning effects. For the common due-date assignment (CON) and slack due-date assignment (SLK) methods, a bi-cost analysis of the scheduling cost and the total weighted resource consumption cost is provided. The objective is to determine the optimal job sequence and the resource allocation simultaneously, such that the scheduling cost (the total weighted resource consumption cost) is minimized subject to the total weighted resource consumption cost (the scheduling cost) being limited. Solution procedures are provided for the problems under consideration. [ABSTRACT FROM AUTHOR]

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem. [ABSTRACT FROM AUTHOR]

This paper investigates the operator allocation problem with learning effects and server breakdown in cellular manufacturing systems (CMSs) using fuzzy computer simulation and response surface methodology (RSM). The primary contribution of this study is incorporating combined server breakdowns and learning effects in CMS under uncertainty. Machine breakdowns of all the machines as well as the probability related to each entity should be delivered in good order are considered. Also, previous studies did not consider fuzzy simulation and RSM to deal with environmental and data uncertainty in operator allocation problems. The superiority of the presented model, in comparison with the traditional one, is shown according to the number of required iterations. The proposed simulation model is run in uncertain state to obtain the total processing time. RSM algorithm identifies a fitted function in terms of the value of allocated capital to each server and total processing time. This is a practical approach for decision-makers of all Cellular Manufacturing Systems. [ABSTRACT FROM AUTHOR]

Purpose: To establish the within-day and between-day intra-rater reliability, inter-rater reliability, validity and systematic errors of the tandem gait test (TGT). Materials and methods: Thirty participants performed the TGT and the timed up and go test (TUG) twice on the first day. Three independent raters measured these tests. After two weeks, the protocol was repeated by one tester to assess between-day reliability. The within-day and between-day intra-rater reliability was estimated by intraclass correlation coefficient (ICC) and Bland-Altman plots to assess systematic errors associated with the learning effect. Inter-rater reliability was estimated by ICC and minimum detectable change at 95% confidence. Concurrent validity was assessed by examination of the correlation between TGT and TUG using Pearson’s correlation coefficient. Results: Within-day and between-day intra-rater reliability were good (ICC > 0.9 and ICC > 0.7). The Bland-Altman plots showed systematic error in the between-day measurements. Inter-rater reliability was supported by strong values for ICC (ICC > 0.9). The Pearson’s correlation coefficient between the TGT and TUG showed a significant positive correlation (r > 0.67, p < .001). Conclusions: These results show that the learning effect of the TGT might be a considerable factor when interpreting follow-up measurements after a few days. [ABSTRACT FROM AUTHOR]

In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions. [ABSTRACT FROM AUTHOR]

Learning effect is a phenomenon in industrial processes that a machine (plant, worker, etc) can improve its productivity continuously with time, that is the actual processing time of a job decreases after the machine (plant, worker, etc) processes other jobs and gains some experiences. We study single machine scheduling problems with sum-of-processing-time based and job-dependent learning effect. The objectives are to minimize the maximum lateness, the number of tardy jobs, and total weighted completion time. By performing reductions from equal cardinality partition problem, we prove that these problems under investigation are all NP-hard. Two special cases that can be solved in polynomial time are also presented. [ABSTRACT FROM AUTHOR]

Chinese as a foreign language (CFL) learners generally encounter difficulty in using some special rules of Chinese grammar because such grammar points do not exist in their native languages. CFL learners require an effective learning strategy to assist them in acquiring a greater understanding of Chinese grammar. Thus, we integrated a self-explanation strategy into a Chinese-learning system. Specifically, the system includes self-explanation prompts, instructional feedback, and remedial learning materials. The aim of this study was to determine the effects of using a self-explanation strategy on Chinese sentence learning in a computer-assisted language learning environment. The participants were assigned to an experiment group with self-explanation instruction and a control group without self-explanation to practice Chinese grammar points. Learning performance, including grammar tests and cognitive loads, was measured. The results showed higher learning outcomes for Chinese grammar and sentence structure in the self-explanation group. Furthermore, no significant difference in cognitive load was observed between the two groups. Implications for using self-explanation in computer-assisted language learning are discussed. [ABSTRACT FROM PUBLISHER]

One of the most common objectives in single-machine scheduling problems is the minimisation of total tardiness. Due to the non-deterministic polynomial-time hard nature of this problem, different heuristic and metaheuristic algorithms have been employed to solve it. In this article, an integer programming model with non-monotonic time-dependent job processing times is developed in which deterioration and learning considerations are incorporated simultaneously. A hybrid genetic algorithm-tabu search (GA-TS) approach is employed to solve the problem. Also, to improve the performance of the GA, Taguchi method is used for parameter tuning. Lastly, in an attempt to validate the proposed model, different test problems are generated randomly and solved by both the hybrid GA-TS and an optimisation software, and thereafter a comparison is performed between the results obtained by them. According to the results, the developed approach has potential to yield near-optimum solutions in large-sized problems. To the best of our knowledge, this is the first study that considers non-monotonic time-dependent processing times, deterioration and learning effect simultaneously in a single-machine scheduling problem with the objective of total tardiness minimisation. [ABSTRACT FROM AUTHOR]

The 6-minute walk test (6MWT) is recommended to be performed twice to accurately assess exercise performance in stable chronic obstructive pulmonary disease (COPD) due to the presence of a learning effect. It is unknown whether a learning effect exists when the 6MWT is performed during hospitalisation for acute exacerbation of COPD (AECOPD). This study investigated whether repeat 6MWTs are necessary when conducted in inpatients with AECOPD. Pooled analysis was undertaken of data from two studies (Australia and Brazil) involving 46 participants (25 males, mean age 67.2 years, FEV1 43% predicted) admitted with AECOPD. Two 6MWTs, separated by ≥20 minutes, were performed on the day of discharge. Six-minute walk distance (6MWD; primary outcome), perceived dyspnoea (Borg scale), heart rate and oxyhaemoglobin saturation were recorded. 6MWD data from tests one (T1) and two (T2) were analysed via visual inspection of Bland-Altman plots. Factors associated with test improvement or decline were explored. Mean 6MWD difference between T1 and T2 was 6.2 m, however limits of agreement were wide (−92.2 m to 104.5 m). 32 (70%) participants improved (by any distance) from T1 to T2 by a mean (± standard deviation) of 32 m ± 28 m. Of these, 14 (30%) improved by a distance > 30 m. Fourteen (30%) participants recorded poorer 6MWD at T2 by a mean of 52 m ± 36 m. No factors were able to identify participants who improved or declined upon repeat testing. When performed in patients admitted to hospital with AECOPD, the 6MWT needs to be conducted twice in order to better estimate 6MWD. [ABSTRACT FROM AUTHOR]

In this paper, we study the issue of single-machine rescheduling with linear deteriorating jobs and position-based learning effects simultaneously in response to an unexpected arrival of new jobs. The scheduling efficiency is measured in terms of the makespan, while the cost of disruption is measured in terms of the maximum difference in processing orders of the original jobs before and after disruption. By introducing the effects of deterioration and learning, the job actual processing time is defined by an increasing function of its starting time, meanwhile a decreasing function of its position. Two types of problems are considered. For the first one, the makespan is minimised subject to a limit on the maximum sequence disruption; while in the second one, a linear combination of the makespan and the maximum sequence disruption is minimised. For each problem, the polynomial solvability is demonstrated, and an efficient algorithm is then developed. Finally, extensive computational experiments are conducted to show the efficiency and running behaviours of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

The article scrutinises the learning effect of the unit production time on optimal lot size for the uncertain and imprecise imperfect production process, wherein shortages are permissible and partially backlogged. Contextually, we contemplate the fuzzy chance of production process shifting from an ‘in-control’ state to an ‘out-of-control’ state and re-work facility of imperfect quality of produced items. The elapsed time until the process shifts is considered as a fuzzy random variable, and consequently, fuzzy random total cost per unit time is derived. Fuzzy expectation and signed distance method are used to transform the fuzzy random cost function into an equivalent crisp function. The results are illustrated with the help of numerical example. Finally, sensitivity analysis of the optimal solution with respect to major parameters is carried out. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a group scheduling model with general deteriorating jobs and learning effects in which deteriorating jobs and learning effects are both considered simultaneously. This means that the actual processing time of a job depends not only on the processing time of the jobs already processed, but also on its scheduled position. In our model, the group setup times are general linear functions of their starting times and the jobs in the same group have general position-dependent learning effects and time-dependent deterioration. The objective of scheduling problems is to minimise the makespan and the sum of completion times, respectively. We show that the problems remain solvable in polynomial time under the proposed model. [ABSTRACT FROM AUTHOR]