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We studied whether implementing binding ergonomic shift‐scheduling rules change ageing (≥45 years) social and healthcare employees' (mean age 52.5 years, 95% women) working‐hour characteristics (e.g. weekly working hours, number and length of night shifts, and short shift intervals) and sleep. We compared an intervention group (n = 253) to a control group (n = 1,234) by survey responses (baseline 2007/2008, follow‐up 2012) and objective working‐hour characteristics (intervention group n = 159, control group n = 379) from 91 days preceding the surveys. Changes in working‐hour characteristics were analysed with repeated measures general linear models. The fully adjusted model (sociodemographics and full‐/part‐time work) showed that proportion of short shift intervals (<11 hr, p =.033) and weekend work (p =.01) decreased more in the intervention than in the control group. Changes in sleep outcomes were analysed with generalised logit model to binomial and multinomial variables. The fully adjusted model (sociodemographics, full‐/part‐time work, job strain, health behaviours, and perceived health) revealed higher odds in the intervention group for long sleep (≥9 hr; odds ratio [OR] 5.53, 95% confidence interval [CI] 2.21–13.80), and lower odds of short sleep (<6 hr; OR 0.72, 95% CI 0.57–0.92), having at least two sleep difficulties often (OR 0.55, 95% CI 0.43–0.70), and more specifically difficulties in falling asleep (OR 0.56, 95% CI 0.41–0.77), waking up several times per night (OR 0.43, 95% CI 0.34–0.55), difficulties in staying asleep (OR 0.64, 95% CI 0.49–0.82), and non‐restorative sleep (OR 0.70, 95% CI 0.54–0.90) than the control group. In conclusion, implementation of ergonomic shift‐scheduling rules resulted in minor changes in ageing employees' objective working hours and a consistent buffering effect against worsening of sleep. [ABSTRACT FROM AUTHOR]

This paper applies stochastic dominance (SD) preference-ordering criteria to job shop scheduling rules. A simulation model of a hypothetical dual-constrained job shop is used to derive several measures of shop performance for a number of dispatching/due-date scheduling policies. The results presented suggest that previous research conclusions concerning the relative performance of dispatching scheduling rules may need to be reconsidered if production schedulers are risk-averse utility maximizers. [ABSTRACT FROM AUTHOR]

Much of the existing job shop scheduling literature has focussed on scheduling rules and heuristics that typically do not incorporate customer specific characteristics into their scheduling decisions. This paper specifically considers the situation in which depending upon the identity of the customer, different jobs have different tardiness penalties associated with them. In addition, the tardiness penalties associated with different customers are comparable in magnitude with one another. We propose here and test four different types of scheduling rules which utilize the jobs' tardiness penalty information, and show that the performance of the shop is only mildly affected by the shape and dispersion of the weighting scheme used in determining the tardiness penalties. The performance of scheduling rules is however contingent on due-date tightness. In general, apart from the traditionally well performing shortest processing time rule, the ATC rule works well on the mean flow time measure in loose due-date situations, while COVERT performs well on mean flow time under tight due-date conditions. Lesser differences are seen to exist between mean tardiness and root mean square tardiness (TRMS) performance of different scheduling rules. Weighted mean tardiness deteriorates with increasing due-date tightness and coefficient of variation of job tardiness penalties. Finally, customer service performance worsens considerably with weighted dispatching rules when the performance criteria are not weighted by each job's tardiness penalty. Implications of our results for providing more effective management of job shops, and exploring future research directions in this area are also presented and discussed. [ABSTRACT FROM AUTHOR]

The use of a dual-level structure in managing the use of resources in a multi-project environment involves two sets of decisions. Resource allocation decisions, usually controlled by a central authority, determine the allocation of resources among projects such that resources are allocated to the critical projects. Once assigned to a project, the resources are managed by an independent project manager who schedules the activities within his project using only the resources assigned to him. This paper evaluates the relative performance of four resource allocation priority rules and six resource transfer rules in conjunction with two activity scheduling rules. The results show that the First in System First Served resource allocation priority rule dominates over the other three proposed resource allocation priority rules with the lowest project tardiness, flowtime, and lateness standard deviation. Further evaluation of the resource transfer rules shows that additional resources should only be transferred into a project just-in-time of expected use, and idle resources which are expected to be utilized within a time period of not longer than two times the resource transfer time should be retained within their assigned project. The minimum late finish activity scheduling rule is also shown to perform better than the minimum slack activity scheduling rule. [ABSTRACT FROM AUTHOR]

This article reports the results of an experimental investigation of scheduling decision rules for a dedicated flexible manufacturing system (FMS) with variable performance times and machine breakdowns. An existing FMS was modeled using the actual performance times obtained from the system's transaction log. The performance of six heuristic scheduling rules were tested under differing uncertainty conditions. The simulation model included three types of uncertainty, namely: minor breakdowns, which averaged two hours and occurred once every two shifts; major breakdowns, which averaged ten hours and occurred once every ten shifts; and operation time deviations, which ranged from 0% to 20% of standard time. The runtime for the simulation approximated the work to be done over a ten-week period of time, or 7,590 parts. Six different part loading rules were used to determine whether they could produce different performance results. The smallest proportion of jobs launched and the next empty pallet rule worked appreciably better than the rest of the rules in obtaining the highest FMS utilization. [ABSTRACT FROM AUTHOR]

Summary: This article is concerned with the sliding mode control for Markovian jump systems under transmission constraints. To deal with node congestions, a switched channel scheduling scheme integrating round‐robin (RR) protocol and weight try‐once‐discard (WTOD) protocol is proposed to orchestrate the priority of multiple sensor nodes. At each transmission instant, a system performance‐based detector is adopted to select one scheduling protocol between RR and WTOD in a certain event‐triggering manner, by which only one sensor node has the priority to access the network. Compared with conventional protocols, the proposed scheduling scheme establishes a dynamic coupling mechanism between channel resource and scheduling rules, aiming to strike a balance between improving system performance and reducing communication resource requirements. Then, a token‐dependent sliding mode controller is constructed, and a genetic algorithm‐based optimization problem is given to achieve smaller convergent domain. Finally, numerical examples are shown to verify the present control strategy. [ABSTRACT FROM AUTHOR]

Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population's particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process. [ABSTRACT FROM AUTHOR]

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle‐based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini‐loads. With the recent increase in e‐commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh‐speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q‐learning) approach on a newly proposed tier‐to‐tier SBS/RS design, redesigned from a traditional tier‐captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML‐based algorithm for transaction selection in that system. The ML‐based solution is compared with traditional scheduling approaches: first‐in‐first‐out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q‐learning approach performs better than the two static scheduling approaches. [ABSTRACT FROM AUTHOR]

In this review article, we consider discrete-time birth-death processes and their applications to discrete-time queues. To make the analysis simpler to follow, we focus on transform-free methods and consider instances of non-birth-death Markovian discrete-time systems. We present a number of results within one discrete-time framework that parallels the treatment of continuous time models. This approach has two advantages; first, it unifies the treatment of several discrete-time models in one framework, and second, it parallels to the extent possible the treatment of continuous time models. This allows us to draw parallels and contrasts between the discrete and continuous time queues. Specifically, we focus on birth-death applications to the single server discrete-time model with Bernoulli arrivals and geometric service times and provide the reader with a simple rigorous detailed analysis that covers all five scheduling rules considered in the literature, with attention to stationary distributions at slot edges, slot centers, and prearrival epochs. We also cover the waiting time distributions. Moreover, we cover three Markovian models that fit the global balance equations. Our approach provides interesting insights into the behavior of discrete-time queues. The article is intended for those who are familiar with queueing theory basics and would like a simple, yet rigorous introductory treatment to discrete-time queues. [ABSTRACT FROM AUTHOR]

The flood control ability of cascade reservoirs is affected by many factors, such as the inflow flood form, and flood control scheduling rules. When large cascade reservoirs were built, it is necessary to evaluate the overall flood control ability of the reservoirs and explore the best joint scheduling strategy. In this study, cascade reservoirs in the upper reaches of the Yangtze River in China were selected as the research case. A stochastic model of inflow floods and a dynamic capacity flood regulation model for the Three Gorges Reservoir (TGR) were established, and the most dangerous inflow floods for downstream flood control were generated. Different scheduling strategies for the cascade reservoirs were set to block the TGR inflow flood to explore the flood control risk in the Jingjiang area under joint operation of the TGR and the upstream cascade reservoirs. The research results show that the joint flood control scheduling strategy has a huge impact on the flood control ability of cascade reservoirs, and different scheduling schemes must be adopted according to the inflow flood characteristics of the reservoirs. This study provides a new idea for systematically evaluating the flood control ability of large cascade reservoirs and selecting appropriate scheduling strategies. [ABSTRACT FROM AUTHOR]

Deciding how to jointly schedule jobs and perform preventive maintenance is a fundamental problem in flexible manufacturing systems, particularly those arising in semiconductor manufacturing. At the same time, past work in this area shows that, even when there is only one station and one type of job, identifying policies that minimize the amount of work‐in‐process (WIP) is a difficult problem. In this paper, we study a single‐station version of this problem with an arbitrary number of job classes, with the objective of minimizing average maintenance costs plus the weighted average amount of WIP. We identify conditions under which it suffices to schedule jobs according to both a server‐state‐dependent version of the cμ‐rule, and a static cμ‐rule where the average service rates are used. One of these conditions states that the ratio between the service rates should remain constant as the server deteriorates. When this assumption does not hold, scheduling with the cμ‐rule can in fact lead to an unstable system; we illustrate this using a simple example. On the other hand, we also present numerical evidence that cμ‐based scheduling performs well compared to other scheduling rules, and relative to a policy based on solving a Markov decision process. [ABSTRACT FROM AUTHOR]

This study examines the effects of sequencing flexibility on the performance of rules used to schedule operations in manufacturing systems. The findings show that taking advantage of even low levels of sequencing flexibility in the set of operations required to do a job results in substantial improvement in the performance of scheduling rules with respect to mean flowtime. Differences in the mean flowtime measure for various rules also diminish significantly with increasing sequencing flexibility. Performance improvements additionally result for such due-date related performance measures as mean tardiness and the proportion of jobs tardy. At high levels of sequencing flexibility, some nonparametric scheduling rules outperform the shortest processing time rule in terms of the mean flowtime criterion. Rules based on job due dates also outperform rules based on operation milestones in terms of tardiness related criteria at high levels of sequencing flexibility. The implications of these findings for the design of manufacturing systems and product design are noted. [ABSTRACT FROM AUTHOR]

It is known that it is critical for train rescheduling problem to address some uncertain disturbances to keep the normal condition of railway traffic. This paper is keen on a mathematical model to reschedule high-speed trains controlled by the quasi-moving blocking signalling system impacted by multidisturbances (i.e., primary delay, speed limitation, and siding line blockage). To be specific, a mixed-integer linear programming is formulated based on an improved alternative graph theory, by the means of rerouting, reordering, retiming, and train control. In order to adjust the train speed and find the best routes for trains, the set of alternative arcs and alternative arrival/departure paths are considered in the constraints, respectively. Due to this complex NP-hard problem, a two-step algorithm with three scheduling rules based on a commercial optimizer is applied to solve the problem efficiently in a real-word case, and the efficiency, validity, and feasibility of this method are demonstrated by a series of experimental tests. Finally, the graphical timetables rescheduled are analysed in terms of free conflicts of the solution. Consequently, the proposed mathematical model enriches the existing theory about train rescheduling, and it can also assist train dispatchers to figure out disturbances efficiently. [ABSTRACT FROM AUTHOR]

Under the constraints of limited spaces and imbalanced traffic volumes (for both in and out directions) of container gates, reversible lane layouts become an economical and practical way to improve the service level of container terminal systems and make the maximum use of the current terminal resources. Together with a consideration of minimized total costs (both construction and operating) of terminal gate system, this paper first developed an optimization model to decide the number and scheduling rules of the reversible lanes at a terminal gate. A metaheuristic algorithm was built to solve the optimal model. Meanwhile, to reflect the randomness and dynamics property of the terminal gate system in practice, parameters that cannot be calculated from conventional analytic methods are obtained through a simulation model. Finally, a hub container terminal in the northeast of China was employed to verify the effectiveness of the proposed method and provide a theoretical foundation for the construction and management of terminal gate systems. [ABSTRACT FROM AUTHOR]

This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power‐flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi‐objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small‐sized instances. We also proposed a multi‐objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade‐off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4–6% increase), while at the same time allowing for a small reduction in energy consumption. [ABSTRACT FROM AUTHOR]

At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of multiyards railway intermodal terminal (MYRIT) construction in China, performance evaluation has become an important issue for terminal design and management departments. Due to the complexity of the multiyards terminal and the associated rail network, the train moving process and related terminal operations have become more complicated compared with the traditional intermodal container terminal. However, in general simulation platforms, the train moving process is simplified and train route scheduling rules are not considered in existing simulation models. In order to provide an accurate and comprehensive quantitative evaluation tool for MYRIT, a simulation platform based on the Timed Petri Net model has been developed, which can offer decision support for terminal design and management departments. In this platform, a yards and facilities layout module has been created to give simulation users access to designing the railway network on this platform. And a train route dispatching simulation method has been integrated to provide an accurate simulation of the train moving process. Based on a real case of Qianchang railway intermodal terminal that is located in Fujian Province, China, the platform is thoroughly validated against historical data. And the test scenarios show that train routes arrangement and handling equipment configuration both have a significant influence on overall terminal performance, which need to be carefully considered during terminal design and management. [ABSTRACT FROM AUTHOR]

Scheduling of traditional job shops in make-to-order systems has seen extensive research over the past three decades. In such systems, performance is often related to various job completion metrics such as average flow time, average lateness, etc. This paper examines a scheduling problem in a make-to-stock environment where individual job completion measures are irrelevant. In this case, customer orders are satisfied through on-hand inventory where customer service is more closely related to the manufacturer's ability to quickly satisfy demand. We consider the role of scheduling in reducing inventories and improving customer service in the context of a manufacturer who assembles several different products on a single assembly line. We develop scheduling rules for such a system and experimentally compare their performance to those typically used in such environments. Our results indicate that rules which consider the inventory position and demand forecast outperform traditional fixed cycle rules.. [ABSTRACT FROM AUTHOR]

A simulation study was conducted to investigate the behavior of family scheduling procedures in a dynamic dispatching environment. Two scheduling rules that incorporate setup avoidance mechanisms (FCFS-F and SPT-F) and two that do not (FCFS and SPT) were applied to a single machine. The scheduling environment was varied by controlling several important factors: the machine utilization, the number of setup configurations (families), the size of the family setup times relative to the job run times, the frequency by which members of the part families were released for processing, and the distribution of job interarrival and job run times. The major results from the study are the following: (1) The degree of stability in the system is the most influential factor with respect to mean flow time and flow time variance. Under low variance service and interarrival time distributions, the impact of scheduling nde selection is minor. (2) Conversely, under unstable scheduling situations, family scheduling procedures can have a substantial impact. (3) Clear interaction effects are noticed between all factors. The environment most conducive to family scheduling is characterized by high resource utilization, low setup-to-run time ratio, few part families, and erratic job arrivals. (4) Under conditions favorable to family scheduling, setup avoiding procedures can be used to increase output while at the same time reduce the mean and variance of flow time. (5) The shortest processing time rule (SPT) performs well with respect to mean flow time when relative setup times are small. Overall, however, SPT-F generates the lowest mean flow time while FCFS-F produces the lowest flow time variance. This study shows that scheduling procedures that consider setups in their structure can outperform rules that do not under many different operating conditions. However, the magnitude of this advantage very much depends on the scheduling environment. The results also highlight the fact that it may be better to try to reshape the manufacturing environment than worry about selecting the correct scheduling rule. If the environment cannot be stabilized, then the choice of a setup avoiding procedure, allocation of families to machines, and setup reduction become important issues. [ABSTRACT FROM AUTHOR]

In a cloud computing environment, the primary goal of resource scheduling is to reduce the economic expenditures for cloud users and grow fiscal achievement for cloud providers. In this article, the study of numerous forms of resource scheduling algorithms is presented that has been applied in IaaS clouds. The selected research articles are classified into six categories, according to the nature of the algorithm used. Further, the authors pointed out several issues and challenges with the help of the resource scheduling algorithms, the comparative performance metrics, and the simulation tools used to validate the several algorithms. The enhancement illustrates better performance concerns decreasing the cost and time while improving the competence and utilization of resources for IaaS clouds. These algorithms are executed in numerous simulation tools and real environments similar to CloudSim, SimGrid, MATLAB, and test‐bed environments (practical implementation). This critical review and classification will serve as a foundation for further research in IaaS clouds in the Internet of Things (IoT) environments. [ABSTRACT FROM AUTHOR]

Multi‐criteria methods and systems have been developed over many years to support decision‐making with a process of eliciting preferences. The emphasis has been on those that incorporate compensatory rationality, the characteristics of which make the process more complex. Neuroscience has offered the opportunity to capture aspects of cognition, generating insights. This paper investigates the preference elicitation process with Flexible and Interactive Tradeoff (FITradeoff), using electroencephalography and eye‐tracking applied to a sample of undergraduate and graduate students. While FITradeoff offers greater flexibility when evaluating recent advances in the area, the experiment carried out incorporates paradigms present in other methods developed for compensatory rationality. The results indicate the effect the type of predominant criterion has on cognitive effort. Moreover, a non‐linear relationship between the number of criteria and questions was found, which suggests that decision‐makers use a strategy of minimizing cognitive effort. Such findings help to direct efforts to enhance systems and methods with compensatory rationality. [ABSTRACT FROM AUTHOR]

The aircraft scheduling problem (ASP) is the real-time problem of scheduling takeoff and landing operations at a congested airport in a given time horizon, taking into account the runways and the air segments in the terminal maneuvering area . The ASP can be viewed as a job shop scheduling problem with additional real-world constraints. Compared with the current literature based on job shop scheduling applied to solve the ASP, we enrich the existing models by including new formulations of relevant practical constraints. We introduce and analyze three alternative ASP formulations, in which the objective function is the minimization of delay propagation with respect to the off-line timetable. Scheduling rules, heuristic and exact methods are implemented and tested on instances from the Roma Fiumicino airport, in Italy. Computational experiments show that practical-size instances are solved to near-optimality by our branch and bound algorithm in a few seconds of computation. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 65(3), 212-227 2015 [ABSTRACT FROM AUTHOR]

This paper describes a solution approach for stochastic multi‐resource multi‐patient staff scheduling problems in inpatient units. Our solution approach has four steps. First, we classify patients into a number of groups with similar care‐provider requirements. Second, a predictive Markov model captures patients' flow in the inpatient unit and provides a prediction of the number of patients of each group in the future. This predictive model allows us to generate a potentially large set of possible system utilization scenarios over the planning horizon. Third, a mixed‐integer programming model with an expected value objective function seeks to minimize the expected over‐staffing and under‐staffing costs across all possible scenarios. Lastly, we use simulation to sample system utilization scenarios and the sample average approximation method to find a reliable and generalizable solution to the model across all possible scenarios. We evaluate the performance of the proposed solution using real data from the Children's Hospital of Eastern Ontario's inpatient mental health unit. The results show that the proposed approach significantly decreases the expected cost of the schedules in comparison to the traditional approaches. [ABSTRACT FROM AUTHOR]

ABSTRACT Appointment policy design is complicated by patients who arrive earlier or later than their scheduled appointment time. This article considers the design of scheduling rules in the presence of patient unpunctuality and how they are impacted by various environmental factors. A simulation optimization framework is used to determine how to improve performance by adjusting the schedule of appointments. Prior studies (that did not include patient unpunctuality) have found that a scheduling policy with relatively consistent appointment interval lengths in the form of a dome or plateau dome rule to perform well in a variety of clinic environments. These rules still perform reasonably well here, but it is shown that a combination of variable-length intervals and block scheduling are better at mitigating the effects of patient unpunctuality. In addition, performance improves if the use of this policy increases toward the end of the scheduling session. Survey and observational data collected at multiple outpatient clinics are used to add realism to the input parameters and develop practical guidelines for appointment policy decision making. [ABSTRACT FROM AUTHOR]

We present an integrated approach to pricing and scheduling for services that are differentiated in terms of throughput, delay and loss specifications. The key building block to the model are quality value curves that specify a user's value of higher quality levels. From the analysis emerges a pricing rule that charges based on rate and quality grade, and a dynamic scheduling rule, called the Gcµ rule. The analysis also derives the economically optimal probabilistic quality of service (QoS) guarantee parameters. We compare our model to the deterministic approach of QoS guarantees using burstiness constraints and fair scheduling rules. The scheduling that arises from such a deterministic approach is the well-known Generalized Processor Sharing (GPS). A comparative analysis inspires the fair Gcµ-PS rule as the scheduling rule that combines the unique strengths of GPS and Gcµ. This Gcµ-PS rule is proposed as a tailored scheduling solution for both the expedited forwarding class and the four assured forwarding classes in the IETF's differentiated services. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Surface hazards can form hazard cascades which expand their reach. However, our understanding of their complex dynamics and ability to mitigate their impacts remain limited. In 2018, two landslides dammed the Jinsha River in the Tibetan plateau and formed landslide‐dammed lakes. Subsequent dam breaches prompted the evacuation of >120,000 people. An early warning system for floods using a regional seismic network has been proposed on the basis of catastrophic floods having been detected ∼100 km away, with seismic energy proportional to discharge. Surprisingly, we find that this catastrophic outburst flood was undetectable beyond a few kilometers, with peak seismic energy preceding peak discharge. We propose that river channel stability also controls seismic energy generation and should be considered for accurate monitoring of catastrophic floods. In contrast, we find that the various processes during dam breach can be well‐characterized seismically further away and provide warning ∼60 min before discharge exceeds monsoon flood levels. We also show that numerical modeling of dam breaches which typically lacks in situ measurements can benefit from incorporating seismic data as constraints. Finally, we show that this event drastically increased sediment fluxes ∼670 km downstream for years and may significantly reduce the capacity of hydropower plants. Our results reveal ways to improve early warning of catastrophic outburst floods and the need to consider surface hazards' long‐term impact when managing infrastructure in mountainous regions. Plain Language Summary: Mountainous regions are prone to surface hazards such as landslides and floods. However, our understanding of how these events evolve and interact is limited. In 2018, two landslides blocked a river in Tibet and created landslide‐dammed lakes. Subsequently, the dam broke and the resulting outburst floods severely damaged downstream infrastructure and led to the evacuation of >120,000 people. In contrast to what previous studies suggest, we find that this catastrophic outburst flood was undetectable beyond a few kilometers, with peak seismic energy preceding peak discharge. We propose that river channel stability also controls seismic energy generation and should be considered for accurate monitoring of catastrophic floods. Conversely, the dam breakage could be detected seismically further away and provide warning ∼60 min before discharge exceeded monsoon flood levels. Finally, we show that this event drastically increased sediment amounts downstream for years and may reduce the capacity of hydropower plants. Our results reveal ways to improve seismic monitoring and early warning of catastrophic floods and the need to consider the long‐term impact of surface hazards when managing downstream infrastructure. Key Points: Catastrophic outburst floods were surprisingly quiet with peak seismic energy preceding peak discharge due to changes in channel stabilityVarious dam breach processes can be well‐characterized seismically, which can constrain numerical modeling and provide earlier warningThese hazard cascades drastically increased sediment fluxes ≥670 km downstream for years and reduced hydropower plant capacity [ABSTRACT FROM AUTHOR]

While the majority of the literature on shop scheduling has emphasized time-based performance criteria such as mean flow time, lateness, and tardiness, the primary goal of management should be the maximization of shop profitability. In this research the net present value (NPV) criterion is introduced to measure shop profitability. This measure combines aspects of job flow time and inventory holding costs into a single measure. A simulation model of a job shop is used to examine the performance of a variety of time- and value-based scheduling rules. These rules are evaluated with respect to the NPV criterion in both random and flow shop environments. The results suggest that priority rules that utilize monetary information about jobs yield a higher NPV than many time-based rules in most situations, with little sacrifice in job tardiness. A well-researched time-based rule, critical ratio, also provides excellent performance when the shop is heavily loaded. [ABSTRACT FROM AUTHOR]

The primary issue addressed in this research is how to schedule clients as they call for appointments, without knowing which "types" of clients will call at a later time. The main goal is to compare various scheduling rules in order to minimize the waiting time of the clients as well as the idle time of the service provider. Interviews with receptionists verified that they have knowledge regarding differences between clients' service time characteristics. This information is used both to differentiate between clients and to develop various scheduling rules for those clients. A simulation model of a dynamic medical outpatient environment is developed based on insight gained from the interviews and from prior research. Two decision variables are analyzed ("scheduling rule" and "position of appointment slots left unscheduled for potential urgent calls") while two environmental factors are varied ("expected mean of the clients' service time", and "expected percentage of clients with low service time standard deviation compared to those with high service time standard deviation"). This resulted in 30 combinations of decision variables, each tested within 15 combinations of environmental factors. By using multiple performance measures, it is possible to improve considerably on some of the "best" rules found in the current literature. The "best" decisions depend on the goals of the particular clinic as well as the environment it encounters. However, good or best results can be obtained in all cases if clients with large service time standard deviations are scheduled toward the end of the appointment session. The best positioning of slots left open for urgent clients is less clear cut, but options are identified for each of a number of possible clinic goals. [ABSTRACT FROM AUTHOR]

Surveys of industrial scheduling practice show that meeting customer due dates is a critical concern for many manufacturing systems. While there is considerable research on the effectiveness of scheduling rules in job shops, very little work is reported on flow shops. Although scheduling rules developed for job shops can be applied in other systems as well, we show that the inherent structure of a flow shop can be utilized to construct effective solution procedures. In particular, for the total tardiness problem, we develop conditions for local optimality in a 2-machine flow shop, and use these results to generate an efficient improvement heuristic procedure. We also construct solution methods that utilize the notion of shifting bottlenecks. Our computational experience reveals the superiority and robustness of the proposed approaches under a variety of problem scenarios. The results of this study also indicate the need to look beyond permutation schedules for solving the flow shop tardiness problem. Furthermore, they suggest that the criterion for determining machine criticality needs to take the scheduling objective into consideration. While it appears intuitive to impute criticality on the basis of machine workloads and, therefore, emphasize the bottleneck machine for generating the shop schedule, this approach may frequently result in inferior system performance. [ABSTRACT FROM AUTHOR]

This paper reports on a simulation study which was undertaken to determine if float could be significantly reduced by altering a bank's check-processing priority assignments. Float, the dollar value of check payments not yet collected from parent banks, represents a significant opportunity cost in terms of lost interest income to receiving banks. The study consisted of a comparison of a major city bank's present check sorting decision rules based on minimization of check handling, versus a proposed sorting scheme based on dollar magnitudes of checks. The results of the research indicated that float could be substantially reduced and interest income increased by employing the proposed priority of scheduling rules. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush–Kuhn–Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min. [ABSTRACT FROM AUTHOR]

Aim. To bring insights into how healthcare managers and staffing assistants work to achieve sustainable working hours within a participatory scheduling system. Background. Hospital nurses and assistant nurses often work on rotating shifts, which affects their opportunities for sleep, recovery, and work-life balance. In Sweden, a participatory scheduling approach is commonly used, where working hours are planned in collaboration between employees, managers, and staffing assistants. Influence over working hours is related to positive outcomes among shift workers. However, it also places responsibility on the employee to schedule working hours that promote health and patient safety, i.e., sustainable working hours. Accordingly, the organisation has responsibilities to support the employee in this regard. Methods. Semistructured individual interviews were conducted with 11 managers and 9 staffing assistants from four Swedish regions and analysed using thematic analysis. Results. Several key factors for achieving sustainable working hours within the context of participatory scheduling were described: distribution and clarity of responsibilities, allocating time for scheduling, establishing shared responsibility, considering fairness, fostering an individual relationship with the employee, managing dissatisfaction, providing support, clarifying guidelines for sustainable scheduling, managing inconsistencies between employee requests and sustainable working hours, and considering recovery opportunities and the competence mix on shifts. Additionally, contextual factors, such as staffing levels, working procedures, working time arrangements for night work, and technological support, were highlighted as important. Conclusion. Achieving sustainable working hours within participatory scheduling involves considering the interactions between factors at the levels of the organisation, the individual, and the technological systems. Implication for Nursing Management. Nurse managers and staffing assistants must work closely with their employees during participatory scheduling to ensure sustainable working hours. Key goals in this regard include establishing a shared responsibility, clarifying responsibilities and guidelines for sustainable scheduling, and allocating time for the scheduling process. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has led to changes in the morphology and structure of the river system, exacerbating flooding in the basin and seriously affecting the regulation and storage capacity of the river system. We propose the evaluation indexes of the regulation and storage capacity, establish a hydrological–hydrodynamic model to investigate the effects of the polder‐type flood control pattern on the regulation and storage capacity during urbanization. The results show that the river system's static regulation and storage capacity exhibits a decreasing trend during the urbanization process. The change in static regulation and storage capacity of low‐grade rivers has a more significant impact on the total regulation and static storage capacity. The construction of polders increases the flood pressure of the river system and reduces the dynamic regulation and storage capacity. The smaller the size of the flood, the more pronounced the effect. With the increase in flood‐protected areas, the dynamic regulation and storage capacity gradually decreases, and minor flooding is more sensitive to the number of polders. [ABSTRACT FROM AUTHOR]

Real‐time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real‐time irrigation scheduling requires reliable present soil‐crop‐atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer‐based tools are rarely connected in a closed‐loop and farmers' feedback are usually not incorporated into a real‐time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real‐time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human‐machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human‐computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers' own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers' direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers' feedbacks show interest in using the tool in real‐world irrigation scheduling and providing meaningful suggestions to improve the tool for real‐world application. Key Points: Simulation‐optimization, data assimilation, and human‐computer interaction are integrated into a real‐time irrigation scheduling toolHuman‐computer interaction facilitates practical application of the tool through farmer's engagementUsing the tool leads to more accurate state estimations and higher profits in comparison to traditional techniques and practices [ABSTRACT FROM AUTHOR]

Over the last two decades, numerous approaches have been developed for automating construction safety assessment, that is, prevention through design and planning (PtD/P), that overcome the limitations of a purely manual safety assessment process. Despite this, such automated approaches are yet to be widely adopted in practice. Barriers include a lack of standardization of construction safety concepts within regulatory frameworks and a lack of confidence in automated approaches on the part of practitioners. This work presents a freely available 4D BIM‐based benchmark model (3D Building Information Model and 68 schedules) and construction safety ontology covering the two most frequent and severe construction site accidents: falls from height and struck‐by falling object hazards. The benchmark model and ontology enable the construction safety community to test and compare the performance of alternative automated approaches. Moreover, they are utilized in our PtD/P tool SafeConAI, and empirical evaluation results show how the safety situation changes among different construction schedules of the same project. [ABSTRACT FROM AUTHOR]

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Polder is usually used for flood control in the river delta area. With the rapid development of urbanization, the dikes or pumps cut the original stream network system, and the stream network connectivity (SNC) and the river system pattern have changed. The dikes or pumps generally force up the river's water level, and regional flood formation mechanisms and processes have changed. In order to quantitatively describe the characteristics of polder‐type flood control measure (PFCM) and the change law of SNC, firstly, the streams inside polders were generalized as virtual streams, a hydrological‐hydrodynamic model was constructed by connecting Hydrologic Engineering Center‐Hydrologic Modeling System and MIKE11 model. Secondly, an SNC evaluation model was constructed based on flow resistance and hydrological process. Finally, the SNC under different scenarios was simulated and evaluated to reveal the influence of the PFCM on SNC. And the dominance analysis method obtained the main control factors of SNC changes. The results showed that the pumps as the main drainage facility under the PFCM, SNC after the opening of the pumps were increased by 0.060, 0.103, and 0.311 for 50%, 30%, and 3% frequency flood scales compared with the pumps closed, respectively. However, compared with the natural stream (without the PFCM), the SNC decreased by 0.391, 0.456, and 0.487, respectively, at the same time of the same flood scale. The PFCM negatively impacted the SNC, and the number of pumps was the main control factor of the SNC. [ABSTRACT FROM AUTHOR]

The flexible and interactive tradeoff (FITradeoff) method is a multi‐criteria decision‐making/aiding (MCDM/A) method in the context of the multi‐attribute value theory. The implementation of the FITradeoff method is supported by a decision support system (DSS), which includes an elicitation procedure for scaling constants with the flexibility of including a holistic evaluation. The holistic evaluation has graphics that can be used to assist decision‐makers (DMs) during the FITradeoff decision process. In this context, three neuroscience experiments have been conducted to investigate how a DM uses the graphics to evaluate an MCDM/A problem. Thus, the main contribution of this paper is to propose the success‐based decision rule. Based on this rule, recommendations are made concerning the holistic evaluation. The first is that this rule can assist the analyst to advise DMs with information on the visualization's probability of success. Second, this rule suggests improvements that can be made to the design of the FITradeoff DSS. [ABSTRACT FROM AUTHOR]

Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM. [ABSTRACT FROM AUTHOR]

The multi-skill project scheduling problem (MSPSP) has been first addressed in the scheduling community for more than 15 years. This article deals with a new variant of this problem, the MSPSP with partial preemption, where only a subset of resources can be released during the preemption periods. Like the standard problem, this variant is NP-hard, because of which we propose in this article a series of heuristic algorithms to solve instances arising from an industrial application. First, we present a serial greedy algorithm, based on priority rules and a flow problem for resource allocation. To improve the solutions of the greedy algorithm, we then introduce a binary tree based search algorithm and a greedy randomized adaptive search procedure (GRASP). Finally, we propose a large neighborhood search (LNS) algorithm integrating exact and heuristic methods. The best results in terms of solution quality and execution time are obtained by combining the GRASP algorithm and LNS approach. Furthermore, the proposed GRASP algorithm is able to find new best results on 56 of 216 instances on a standard MSPSP instance set, which shows the quality of the approach even on special cases of the considered problem. [ABSTRACT FROM AUTHOR]

Appointment schedules aim at achieving a proper balance between the conflicting interests of the service provider and her clients: a primary objective of the service provider is to fully utilize her available time, whereas clients want to avoid excessive waiting times. Setting up schedules that strike a good balance is severely complicated by the fact that the clients' service times are random. Because of the lack of explicit expressions, one has often set up schedules relying on simulation techniques. In this paper, we take a radically different approach: we use newly developed analytical techniques to numerically determine optimal schedules (i.e., schedules that optimize a given objective function that incorporates the interests of the service provider as well as the clients) and compare them with a number of easily evaluated heuristics; in our setup, it is throughout assumed that a given fraction of the clients does not show up. Our results are particularly useful in situations in which there is a significant variation in the service times, which is typically the case in various healthcare-related settings. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]