FCFS Policy Research Articles

Planning of multi-production line maintenance

Efficiency of production lines is significantly impacted by maintenance activities, which are often overshadowed by the focus on production planning. The symbiotic relationship between maintenance and production activities is a critical yet overlooked factor. We examine the importance of maintenance planning, specifically within the dynamic context of production line operations. In this maintenance planning setting, scarce resources, particularly technicians, are commonly disregarded. Our research addresses the challenge of real-time maintenance planning across multiple production lines by developing a state-of-the-art heuristic, which takes into account machine production states and resource availability and is augmented by re-enactment procedures. We validate the heuristic by employing Markov Decision Processes and digital twin Discrete Event Simulations, which leverage real-world production data. This modeling framework is new for this specific problem. In our experiments, we compare the heuristic against optimal policies and practitioner-based policies. Our heuristic matches the performance of optimal policies in the Markov Decision Processes and significantly outperforms FCFS and practitioner-based policies in the Discrete Event Simulations. Notably, our heuristic is not only effective against standard policies, realizing improvement of 1% to 3%, but it is also the first to be generally applicable to production lines involving multiple machines and resources in real-time.

Nonsequential Appointment Scheduling With a Random Number of Requests

This article studies an appointment scheduling problem where a service provider dynamically receives appointment requests from a random number of customers. By leveraging the randomness of the number of potential customers, we develop a nonsequential appointment scheduling policy as an alternative to the conventional first-come-first-served (FCFS) policy. This allows for more flexibility in managing appointment scheduling. To calculate the optimal policy, we develop a branch-and-bound algorithm in which the lower bound is estimated using multiple approaches, such as optimality conditions, dynamic programming for calculating FCFS policy, and the shortest path reformulation. Through numerical studies, we observe that nonsequential appointment scheduling is particularly advantageous in systems characterized by highly fluctuating customer numbers or low congestion. In such cases, leaving gaps between appointments for potential future arrivals proves to be a more appropriate strategy. We also evaluate the performance of heuristics proposed in prior literature and provide insights into situations where these heuristics can be effectively applied.

Impact of Channel Memory on the Data Freshness

In this letter, we investigate the impact of channel memory on the average age of information (AoI) for networks with various packet arrival models under the first-come-first-served (FCFS) and the preemptive last-generated-first-served (pLGFS) policies over the Gilbert-Elliott (GE) erasure channel. For networks with Bernoulli and generate-at-will arrival models, the AoI performances under the FCFS and pLGFS policies are derived explicitly as functions of the channel state transition probabilities. For networks with periodic arrival model, we derive the closed-form expression for the average AoI under pLGFS and propose a numerical algorithm for efficient evaluation of the AoI under FCFS. It is revealed that for pLGFS policy, the average AoI increases monotonically with channel memory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {\eta }{1-\eta }$ </tex-math></inline-formula> over the symmetric GE channel. For FCFS, the average AoI increases even faster due to the queuing delay, with an additional term related to the packet arrival rate.

Disparities in emergency department prioritization and rooming of patients with similar triage acuity score.

We identify patient demographic and emergency department (ED) characteristics associated with rooming prioritization decisions among ED patients who are assigned the same triage acuity score. We performed a retrospective analysis of adult ED patients with similar triage acuity, as defined as an Emergency Severity Index (ESI) of 3, at a large academic medical center, during 2019. Violations of a first-come-first-served (FCFS) policy for rooming are identified and used to create weighted multiple logistic regression models and 1:M matched case-control conditional logistic regression models to determine how rooming prioritization is affected by individual patient age, sex, race, and ethnicity after adjusting for patient clinical and time-varying ED operational characteristics. A total of 15,781 ED encounters were analyzed, with 1612 (10.2%) ED encounters having a rooming prioritization in violation of a FCFS policy. Patient age and race were found to be significantly associated with being prioritized in violation of FCFS in both logistic regression models. The 1:M matched model showed a statistically significant relationship between violation of rooming prioritization with increasing age in years (adjusted odds ratio [aOR] 1.009, 95% confidence interval [CI] 1.005-1.013) and among African American patients compared to Caucasians (aOR 0.636, 95% CI 0.545-0.743). Among ED patients with a similar triage acuity (ESI 3), we identified patient age and patient race as characteristics that were associated with deviation from a FCFS prioritization in ED rooming decisions. These findings suggest that there may be patient demographic disparities in ED rooming decisions after adjusting for clinical and ED operational characteristics.

Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?

The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.

Quantifying Utilitarian Outcomes to Inform Triage Ethics: Simulated Performance of a Ventilator Triage Protocol Under SARS-CoV-2 Pandemic Surge Conditions

Background: Equitable protocols to triage scarce life-saving resources must be specified prior to disasters in order to promote transparency, trust and consistency. The degree to which proposed utilitarian protocols may maximize lives saved is unknown.Methods: We constructed a simulation model based on New York State 2015 guidelines for ventilator triage (NYS) compared to a first-come-first-served (FCFS) method for ventilator allocation under various hypothetical ventilator shortages. We included patients with laboratory confirmed SARs-CoV-2 infection admitted with respiratory failure requiring mechanical ventilation to three acute care hospitals in New York from 3/01/2020 and 5/27/2020. We estimated (1) survival rates, (2) number of excess deaths, (3) number of patients extubated early due to capacity constraints, (4) survival rates among patients not allocated a ventilator at triage or extubated early due to capacity constraints.Findings: 807 patients were included in the study. Survival rates were similar between the NYS and FCFS policies at all the survival probabilities estimated for those triaged to removal of the ventilator. At the lowest ventilator capacity, the NYS policy has an estimated survival of 28.5% (CI: 28.4-28.6), compared to 28.1% (CI: 27.7-28.5) for the FCFS policy. The two policies had similar estimated excess deaths above historical observed deaths.Interpretation: This simulation of a SOFA-based triage protocol for distributing ventilators during the COVID-19 pandemic revealed limitations in achieving the utilitarian goals these protocols are designed to fulfill. Quantifying these outcomes can inform a better balance competing moral aims.Funding: None to declare. Declaration of Interest: None to declare. Ethical Approval: This study was approved by the Albert Einstein College of Medicine/Montefiore Medical Center IRB (IRB # 2020-12128) and the Columbia University IRB (IRB # AAAT4062).

Space-time routing in dedicated automated vehicle zones

With the fast development of automated vehicle (AV) technologies, scholars have proposed various innovative local traffic control schemes for more effective management of AV traffic, especially at intersections. However, due to computational intractability, the investigation of network-level AV control is still at the initial stage. This study proposes a space-time routing framework applicable in dedicated AV zones. To relieve the computational load, we establish a node-based conflict point network to model realistic road networks, and at each conflict point, we record the space-time occupations of AVs in continuous timelines. Then, based on the conflict point network, we develop two space-time routing algorithms for each AV once it enters the dedicated AV zone to minimize its trip travel time while maintaining the non-collision insurances; these two algorithms can trade-off between solution quality and computational load. Furthermore, to enhance the network throughput for handling heavy traffic, we develop a ”platoon strategy” that forces AVs to pass through conflict points in platoons, and we adopt Deep Q-learning (DQN) to optimize the platoon sizes at different spots dynamically. Numerical tests show that both proposed algorithms perform well in that they can execute the routing tasks with very limited computational time, and the average vehicle delay approaches zero when the traffic is relatively mild. Meanwhile, compared with the FCFS policy and the optimization-based approach, the platoon strategy can greatly reduce the average vehicle delay under congested scenarios and give a better balance between the optimality and real-time performance.

Eliciting patient views on the allocation of limited healthcare resources: a deliberation on hepatitis C treatment in the Veterans Health Administration

BackgroundIn response to the development of highly effective but expensive new medications, policymakers, payors, and health systems are considering novel and pragmatic ways to provide these medications to patients. One approach is to target these treatments to those most likely to benefit. However, to maximize the fairness of these policies, and the acceptance of their implementation, the values and beliefs of patients should be considered. The provision of treatments for chronic hepatitis C (CHC) in the resource-constrained context of the Veterans Health Administration (VHA) offered a real-world example of this situation, providing the opportunity to test the value of using Democratic Deliberation (DD) methods to solicit the informed opinions of laypeople on this complex issue.MethodsWe recruited Veterans (n = 30) from the VHA to attend a DD session. Following educational presentations from content experts, participants engaged in facilitated small group discussions to: 1) identify strategies to overcome CHC treatment barriers and 2) evaluate, vote on, and modify/improve two CHC treatment policies – “first come, first served” (FCFS) and “sickest first” (SF). We used transcripts and facilitators’ notes to identify key themes from the small group discussions. Additionally, participants completed pre- and post-DD surveys.ResultsMost participants endorsed the SF policy over the FCFS policy, emphasizing the ethical and medical appropriateness of treating the sickest first. Concerns about SF centered on the difficulty of implementation (e.g., how is “sickest” determined?) and unfairness to other Veterans. Proposed modifications focused on: 1) the need to consider additional health factors, 2) taking behavior and lifestyle into account, 3) offering education and support, 4) improving access, and 5) facilitating better decision-making.ConclusionsDD offered a robust and useful method for addressing the allocation of the scarce resource of CHC treatment. Participants were able to develop a modified version of the SF policy and offered diverse recommendations to promote fairness and improve quality of care for Veterans. DD is an effective approach for incorporating patient preferences and gaining valuable insights for critical healthcare policy decisions in resource-limited environments.

Temporal-spatial dimension extension-based intersection control formulation for connected and autonomous vehicle systems

Traffic congestion has become a serious issue worldwide due to the rapid increase in population and traffic demands. Advances in connected automated vehicle (CAV) technology demonstrate the potential to improve traffic mobility and safety performance at intersections. An advanced intersection control system is proposed in this study to coordinate vehicle trajectories and ensure safety and operation efficiency at intersections. A temporal-spatial dimension extension-based trajectory coordination model is developed by formulating all possible trajectories of vehicles at the intersection. Correspondingly, according to the trajectory coordination model, two signal-free control algorithms, including the priority-based algorithm and the Discrete Forward-Rolling Optimal Control (DFROC) algorithm are proposed in this study to manage vehicles at the intersection. These two algorithms, together with the FCFS policy, are compared with the conventional signal control method in a SUMO-based simulation platform. Experimental results indicate that the proposed algorithms outperform the signal control method in terms of reducing total traffic delays at intersections and increasing intersection capacity and operation efficiency.

Performance analysis of cloud computing using series of queues with Erlang service

In this paper we have proposed a priority based Erlang service distribution with k-phases for cloud computing architecture. The multiple users from the public cloud entering into two serially connected M/M/s and M/Ek/1 (Erlang service queue) queues are served based on the non-pre-emptive priority discipline. We have assumed each user of different priority class i, (i ≥ 2) desired to wait until the current user is being served if priority of the service is similar, as per FCFS policy. If the servers are free, users can enter into the M/M/s queue, then enter into the M/Ek/1 queue with probability ϕ and leave the system after service completion or leave the system with probability (1 - ϕ) without entering into the ESQ. We have obtained waiting time for both the queues in the cloud system and shown numerically the total waiting time is lower than the existing system.

Multilevel rationing policy for spare parts when demand is state dependent

The multilevel rationing (MR) policy is the optimal inventory control policy for single-item M / M / 1 make-to-stock queues serving different priority classes when demand rate is constant and backlogging is allowed. Make-to-repair queues serving different fleets differ from make-to-stock queues because in the setting of the former, each fleet comprises finitely many machines. This renders the characterization of the optimal control policy of the spare part inventory system difficult. In this paper, we implement the MR policy for such a repair shop/spare part inventory system. The state-dependent arrival rates of broken components at the repair shop necessitate a different queueing-based solution for applying the MR policy from that used for make-to-stock queues. We find the optimal control parameters and the cost of the MR policy; we, then compare its performance to that of the hybrid FCFS and hybrid priority policies described in the literature. We find that the MR policy performs close to the optimal policy and outperforms the hybrid policies.

Optimization of Conflicting Tram Signal Priority Requests Based on Spatiotemporal Interlocking Logic Using Microscopic Simulation

This paper proposes a method to optimize the conflicting tram signal priority requests at a multi-lines (M-lines) interchange intersection. The method is formulated on the basis of the spatiotemporal logic of complex switch area operations and the characteristics of multiple conflicting tram priority requests. Our study first depicts the interlocking logic of tram system operation according to the phases design and the spatial rules. Furthermore, it classifies the tram priority requests by the intersection entrances, priority phases and tram routes. Finally, it optimizes the serving sequences under the conditions of tram travel delay and switch operation delay. A microscopic traffic simulator, VISSIM, was used to simulate tram system and collect vehicle data. An external application program interface, VISSIM's COM-interface, was used to implement the proposed optimization method. The results show that the proposed method significantly outperforms the no-priority and the conventional first-come-first-served (FCFS) policy in terms of reducing tram delays. More specifically, the average tram delay is reduced by more than 26.6% over the no-priority policy and 13.0% on the FCFS policy in the initial operation. In addition, we find that the average delay which is caused by the switch operation delay increases with the tramline frequency. But the benefits of the proposed method are greater than those of the no-priority strategies.

Differentiated service policy in smart warehouse automation

Smart warehouse automation has emerged as an effective, competitive solution for suppliers and distributors. With the increasing demand for physical storage and distribution services, suppliers and service providers are challenged to respond not only effectively, but with minimal latency. Differentiated service levels for different classes of customer orders have not yet, however, been developed for physical storage and retrieval. In this paper, in the context of smart warehouse automation services, a novel policy, called Differentiated Probabilistic Queuing (DPQ) is developed for servicing customers’ orders by Automated Guided Vehicles (AGV). Applying the DPQ policy, the average overall latency of each customer order, the mean overall processing time of this customer’s orders in the smart warehouse automation system, is characterised under Poisson customer order arrival patterns. The weighted average latency of all customer orders is optimised over the choice of (1) storage assignment and (2) DPQ policy. Due to the existence of two types of variables, Alternating Minimisation method is applied to solve this joint optimisation problem. Compared with a combination of the classical turn-over rate storage assignment method and FCFS policy, the new approach yields 19.64% lower (better) objective function value with statistical significance. Numerical analysis results also indicate, as expected, that when the smart warehouse system resources become more limited, and the price difference among different classes of customer orders increases, the improvement becomes even more significant.

A policy of picking up parcels for express courier service in dynamic environments

As a particular logistics service, the express courier service has seen considerable growth recently, which resulted in an unprecedented fierce competition. Besides, the development of information and communication technologies has enabled express company to manage their service. With the purpose of improving service quality and operation efficiency for express company, we focus on the problem of intercity express courier routing in courier-triggered pickup service. A novel pickup policy for courier routing is proposed based on the idea of centrality measures and the nearest-neighbour (NN) policy by considering the un-serviced customer requests as a globally coupled network. This policy enables to dispatch the idle courier to the more central request location, which allows the courier to easily serve the neighbouring requests around the central request location, thus securing both global and local performance. We also propose a simple prototype of real-time fleet management system where the proposed pickup policy is embedded into it. To evaluate the efficiency and practicability of the pickup policy, we conduct comprehensive computational experiments to generate various testing scenarios; moreover, two widely used dispatching policies – NN and first-come-first-served (FCFS) – are considered as the benchmark policy. Results show that the proposed pickup policy significantly outperforms the NN and FCFS policies in terms of waiting time and total service time.

On fairness in polling systems

Which service discipline is more fair, exhaustive or gated? gated or globally-gated? How can we compare their fairness level? These questions are usually answered by handwaving as no fundamental research in this topic was yet conducted. Polling systems are widely used in many computer networks where several users compete for access to a common resource. Fairness is a fundamental aspect of polling systems; perhaps it serves as the motivation behind various polling schemes. Despite this fundamental role, fairness to customers in polling systems has not been extensively studied to date. This work is an attempt to model fairness in polling systems and study the relative fairness of various polling schemes. We focus, in the context of this work, on evaluating the system’s obedience to the FCFS policy as a measure of the fairness experienced by the individual customers. Using this metric we study the cyclic polling system under five different service disciplines: exhaustive, gated, binomial-gated, two-stage gated and globally-gated. For these systems we derive their “fairness” level both in a discrete system model and in a continuous model. We use the analysis as well as numerical examples to provide basic observations on fairness of polling systems.

On mean waiting time completeness and equivalence of EDD and HOL-PJ dynamic priority in 2-class M/G/1 queue

This paper identifies two different parametrized dynamic priority queue disciplines, earliest due date (EDD) based and head of line priority jump (HOL-PJ), which are found to be mean waiting time complete in two class M/G/1 queue. An explicit one-to-one non linear transformation is obtained between earliest due date and delay dependent priority policy. Mean waiting time equivalence between these queue disciplines is established. Motivation behind the mean completeness and equivalence results is discussed from optimal control perspective. Notion of minmax fairness is introduced and it is argued that a simple global FCFS policy is the only solution for minmax fairness problem in two class by exploiting completeness in the structure of EDD based dynamic priority. Further, these completeness results are used to propose a simpler way for developing optimal control policy in celebrated c/ρ rule for two class M/G/1 queues.

Effect of global FCFS and relative load distribution in two-class queues with dedicated servers

In this paper, we investigate multi-class multi-server queueing systems with global FCFS policy, i.e., where customers requiring different types of service—provided by distinct servers—are accommodated in one common FCFS queue. In such scenarios, customers of one class (i.e., requiring a given type of service) may be hindered by customers of other classes. The purpose of this paper is twofold: to gain (qualitative and quantitative) insight into the impact of (i) the global FCFS policy and (ii) the relative distribution of the load amongst the customer classes, on the system performance. We therefore develop and analyze an appropriate discrete-time queueing model with general independent arrivals, two (independent) customer classes and two class-specific servers. We study the stability of the system and derive the system-content distribution at random slot boundaries; we also obtain mean values of the system content and the customer delay, both globally and for each class individually. We then extensively compare these results with those obtained for an analogous system without global FCFS policy (i.e., with individual queues for the two servers). We demonstrate that global FCFS, as well as the relative distribution of the load over the two customer classes, may have a major impact on the system performance.

Design and evaluation of overloaded service systems with skill based routing, under FCFS policies

We study an overloaded service system with servers of types S={s1,…,sJ}, serving customers of types C={c1,…,cI} under FCFS. Customers arrive in Poisson streams, join the queue and then abandon or get served. Service is skill based, which is described by a compatibility graph G, where (i,j)∈G if server type sj is trained to serve customer type ci. The service duration depends on both server and customer type. This system is motivated by applications in areas as diverse as manufacturing, call centers, housing assignment, health care, data servers and online retailers. At this level of generality, the design in terms of staffing and cross-training decisions is a challenging problem. Based on recent results in Adan and Weiss (2012a, 2012b) [14,15] and on some asymptotic assumptions, we propose an algorithm to determine, for given data, the required levels of staffing to meet target levels of service quality and labor division. The algorithm is validated through a systematic simulation study, showing that it is remarkably robust and accurate. As such, we believe that the algorithm will prove to be useful in aiding the design and effective operation of complex systems with skill based routing.

Analysis of an $$M/M/1+G$$ M / M / 1 + G queue operated under the FCFS policy with exact admission control

In this article, we present an exact theoretical analysis of an $$M/M/1$$ M / M / 1 system, with arbitrary distribution of relative deadline for the end of service, operated under the first come first served scheduling policy with exact admission control. We provide an explicit solution to the functional equation that must be satisfied by the workload distribution, when the system reaches steady state. We use this solution to derive explicit expressions for the loss ratio and the sojourn time distribution. Finally, we compare this loss ratio with that of a similar system operating without admission control, in the cases of some common distributions of the relative deadline.

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