Random Arrivals Research Articles

Cooperation-based bi-level rescheduling method for multi-objective distributed hybrid flow shop with unrelated parallel machines under multi-type disturbances

ABSTRACT Distributed hybrid flow shop production styles are gaining popularity, and many of them feature unrelated parallel machines (UPMs). However, most existing studies fail to consider or solely focus on a single disturbance type. Moreover, they seldom consider processing stability metrics. To closely resemble real production scenarios, this paper first studies the distributed hybrid flow shop scheduling problem with UPMs under two common disturbances (random arrival of new jobs and machine breakdowns), considering processing efficiency and stability metrics. To solve this problem, a cooperation-based bi-level rescheduling method is designed based on the nature of the problem. Among them, a factory load balancing mechanism is proposed to improve factory assignment schemes rapidly. A greedy job insertion partial rescheduling (first level) and improved genetic algorithm (IGA) (second level) are designed to obtain high-quality inner-factory scheduling schemes. In IGA, a dynamic decoding and guided elite individual optimization strategy are presented to balance conflicting objectives. An adaptive tabu search is designed to improve local search efficiency. The experiments involve 120 randomly generated instances. The results illustrate that the proposed methods are very effective. Compared to other efficient full rescheduling algorithms, IGA performs best in spread, generational distance, and inverted generational distance.

A millimeter-wave photometric camera for long-range imaging through optical obscurants using kinetic inductance detectors.

Passive imaging through optical obscurants is a promising application for mm-wave sensing. We have thus developed the Superconducting Kinetic Inductance Passive Radiometer (SKIPR), a 150GHz polarization-sensitive photometric camera optimized for terrestrial imaging using a focal plane array with 3840 kinetic inductance detectors (KIDs). We present a full description of the instrument design, with a particular emphasis on the cryogenic system based on a Gifford-McMahon cryocooler with a two-stage adiabatic demagnetization refrigerator and a dedicated 1.59m crossed Dragone telescope with an altitude/azimuth mount. We include a detailed lab-based characterization of the KIDs, which results in a determination of their superconducting resonator parameters and optical properties. We also present in situ measurements from the telescope, including point-spread functions and noise characterization. In sum, we find that SKIPR performs as expected, providing diffraction-limited imaging with detector noise performance set by the random arrivals of photons from the ambient background. There is minimal variation in detector characteristics over the full SKIPR focal plane array, and the overall detector yield is 92%.

Double Deep Q-Network-Based Solution to a Dynamic, Energy-Efficient Hybrid Flow Shop Scheduling System with the Transport Process

In this paper, a dynamic energy-efficient hybrid flow shop (TDEHFSP) scheduling model is proposed, considering random arrivals of new jobs and transport by transfer vehicles. To simultaneously optimise the maximum completion time and the total energy consumption, a co-evolutionary approach (DDQCE) using a double deep Q-network (DDQN) is introduced, where global and local search tasks are assigned to different populations to optimise the use of computational resources. In addition, a multi-objective NEW heuristic strategy is implemented to generate an initial population with enhanced convergence and diversity. The DDQCE incorporates an energy-efficient strategy based on time interval ‘left shift’ and turn-on/off mechanisms, alongside a rescheduling model to manage dynamic disturbances. In addition, 36 test instances of varying sizes, simplified from the excavator boom manufacturing process, are designed for comparative experiments with traditional algorithms. The experimental results demonstrate that DDQCE achieves 40% more Pareto-optimal solutions compared to NSGA-II and MOEA/D while requiring 10% less computational time, confirming that this algorithm efficiently solves the TDEHFSP problem.

Applications of simulated annealing algorithm in mathematical modelling for scheduling problems

Scheduling problems are common in many fields, with project planning, industrial, operations management, and service establishment. These problems involve the advanced distribution of resources across tasks to exploit exact objectives within predetermined bounds. This study examines an enhanced simulated annealing (ESA) algorithm for addressing dynamic scheduling challenges in job shops, particularly in the context of random job arrivals frequently encountered in manufacturing environments. This programming methodology seeks to minimize three primary targets: the machine sequence variation, the make-span divergence from the original schedule, and the discontinuity rate of newly delivered tasks during processing. In the rescheduling horizon, the ongoing processes are handled as dynamic resource allocation (DRA). Tactics involved in the ESA algorithm include a modified cooling schedule, adaptive temperature regulation, and a solution approval criterion that considers DRA. The experimental results indicate that the ESA algorithm effectively solves job shop scheduling problems. The proposed ESA has a high job completion rate of 95%, task acceptance rate of 92%, job arrival predictability of 85%, discontinuity rate of 8%, and return on investment of 25%. The results highlight the effectiveness of the ESA algorithm in achieving optimal scheduling solutions, underscoring its potential for practical applications in dynamic manufacturing settings.

An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling

Heterogeneity among factories in distributed manufacturing significantly expands the solution space, complicating optimisation. Traditional centralised scheduling methods lack the scalability to adapt to varying factory scales. This paper proposes an end-to-end decentralised scheduling framework based on deep reinforcement learning (DRL) for dynamic distributed heterogeneous permutation flowshop scheduling problem (DDHPFSP) with random job arrivals. The framework utilises a multi-agent architecture, where each factory operates as an independent agent, enabling efficient, robust, and scalable scheduling. Specifically, the DDHPFSP is formulated as a partially observable Markov decision process (POMDP), with a state space reflecting heterogeneity and permutation characteristics and a new tailored reward function addressing sparse rewards and high reward variance. An end-to-end policy network with dual-layer architecture is developed, incorporating a feature extraction network to capture intrinsic relationships between jobs and heterogeneous factories, enhancing the agent's self-learning and policy evolution. Moreover, a backward swap search (BSS) method based on greedy heuristics optimises the pre-scheduling plan during the online phase with minimal computation time. Experimental results demonstrate the framework outperforms the best comparison methods by 39.76% on 540 baseline instances and 59.95% on 2430 generalisation instances. Furthermore, the framework's effectiveness improves by 68.9% with the introduction of the BSS method.

Assembly Graph as the Rosetta Stone of Ecological Assembly.

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories. The assembly graph visually represents assembly dynamics, where nodes symbolise species combinations and edges represent transitions driven by species introductions. Through the lens of assembly graphs, I review how ecological processes reduce uncertainty in random species arrivals (informational approach), identify graphical properties that guarantee species coexistence and examine how the class of dynamical models constrain the topology of assembly graphs (dynamical approach), and quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, I also review methods to decompose complex assembly graphs into smaller, measurable components, as well as computational tools for deriving empirical assembly graphs. In sum, this math-light review of theoretical progress aims to catalyse empirical research towards a predictive understanding of ecological assembly.

Multi-Agent Reinforcement Learning for Distributed Flexible Job Shop Scheduling with Random Job Arrival

Multi-Agent Reinforcement Learning for Distributed Flexible Job Shop Scheduling with Random Job Arrival

An analytical delay model for multi-class and lane-free traffic condition.

This study emphasises the criticality of delay as a performance metric for signalized intersections and the challenges associated with its estimation, particularly in the context of Multi-class and Lane-free (MCLF) traffic conditions. Traditional delay models are often inadequate for such conditions, necessitating the development of a tailored approach. A novel delay equation is proposed, integrating insights from queuing theory principles with consideration of multi-class of vehicles and lane-free movement. Key features include assumption of random arrival and departure pattern as well as distribution, incorporation of Passenger Car Equivalent (PCE) and virtual lane concepts to account for the diverse vehicle classes and lane-free movement prevalent in Indian traffic. The model's efficacy is demonstrated through comparison with conventional in practice delay models, showing its superior performance. This tailored approach enhances the accuracy of delay estimation and also highlights the importance of accounting for specific traffic characteristics in optimising signal design for intersections under MCLF traffic conditions.

Elective Surgery Planning in Mobile Operating Theaters Under Uncertain Demand of Emergency Patients

ABSTRACTMobile operating theaters (MOTs) emerged as a solution to cope with the serious shortage of medical resources in various European countries. In this paper, we study the multi‐MOT elective surgery planning problem. Each MOT consists of an operating room (OR) and a recovery room, that is, a post‐anesthesia care unit (PACU), with two beds, which can be used to perform surgeries and recovery care for both elective and emergency patients. The daily assignment of elective surgeries to MOTs is made in advance (e.g., a week ahead) in the face of uncertain demand of emergency patients. The problem is to determine the assignment of elective patients under uncertain surgery and recovery durations within a finite planning horizon, aiming to minimize total daily costs, which include the cost of assigning elective patients, the OR and PACU utilization cost, and the postponement cost of deferring any elective surgery to the next planning horizon. Due to the possible idle time between surgeries (recoveries, resp.) in the OR (PACU, resp.) and the possible OR blocking due to a limited PACU capacity, the daily OR and PACU utilization cost cannot be expressed in an analytical functional form directly in contrast to the existing literature on OR planning. In this paper, we simulate the surgery and recovery process in ORs and PACUs based on associated distributions, which are empirically verified by the literature, and fit the OR and PACU utilization costs as piece‐wise linear convex functions of the total surgery and recovery durations of the elective patients, respectively. We model the MOT planning problem as a set‐partitioning problem and solve it by column generation. We show that the pricing problem that arises from column generation is NP‐hard. By effectively characterizing the structural properties of the optimal solution to the continuous relaxation of the pricing problem, an efficient implementation of the branch‐and‐bound procedure can be applied to obtain the optimal integral solution to the pricing problem. This solution approach also extends to the case with general OR and PACU utilization cost functions. Computational experiments demonstrate that the proposed approach can solve moderate‐sized instances efficiently. Numerical results also show that, compared with the traditional OR planning model that (i) does not consider the daily PACU utilization cost and thereby ignores the possible blocking times between surgeries in ORs and (ii) neglects the possible idle times between surgeries in ORs caused by the random arrivals of emergency patients, our model results in a cost reduction of around 15.80% on average.

On variants of state-dependent queueing systems for egress modelling

An M/G/c/c state dependent network is a quantitative model for replicating and analyzing the behavior of occupants travelling through a network. The model, however, assumes that its inter-arrival times are independent and exponentially distributed, which inadequately reflects the dynamic and high-stakes nature in emergency situations. This paper simulates the M/G/c/c model for emergency evacuations using Erlang-k distributions—representing the real-world arrival patterns of evacuees in a more controlled and less random arrivals—and correlated inter-arrival times—common during evacuations as occupants moving in clusters due to panic or structural flow patterns. To achieve this, an M/G/c/c simulation model was first developed using Arena simulation software. The model was used to analyze the impact of various arrival rates on system performance metrics. Consequently, the performance metrics were compared with those obtained by replacing exponential inter-arrival times with Erlang-k distributions and independent arrivals with correlated cluster arrivals. The results show that Erlang-k distributions lead to better performance and smoother flow since the arrival is more controlled, while correlated arrivals increase congestion.

Optimal Control of Service Systems with Heterogeneous Servers and Priority Customers

We study service systems with parallel servers and random customer arrivals and focus on the waiting cost of customers. Using a Markov decision process (MDP) modeling approach, we analytically characterize the structures of the optimal dynamic server assignment policies for two important systems, one consisting of multiple homogeneous servers and two classes of customers and the other consisting of two heterogeneous servers and multiple classes of customers. Based on the obtained results, we propose a threshold-type heuristic policy for the generalized system consisting of multiple heterogeneous servers and multiple classes of customers. To design such a heuristic policy, we first develop techniques for the performance evaluation of general threshold-type policies with any given threshold values. We then construct a path to search for the optimal threshold values. We compare the performance of the best threshold-type heuristic policy with that of the optimal policy and show that our proposed heuristic policy is computationally efficient yet generates great performance. To derive additional managerial insights, we compare the system under our threshold-type dynamic server assignment policy with other commonly seen and simple systems, such as the dedicated system and the work-conserving flexible priority system. The clear performance advantage observed from extensive numerical experiments demonstrates the importance and usefulness of dynamic server assignment control for systems serving multiple classes of customer arrivals. Finally, we extend our analysis to incorporate customer-dependent service rates and sojourn-time minimization performance metrics. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Hong Kong Research Grants Council [16500921, 16505819] and the National Natural Science Foundation of China [72201233, 72301222]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01228 .

Efficiency of dynamic traffic signal control in work zones with shuttle operation: theory and practice

Portable traffic signals with fixed-time signal plans are a common type of traffic control at work zones with shuttle traffic. The most-used alternatives are flagging and intelligent transport systems with traffic-actuated signals. These can provide more efficient traffic control, but existing policies often do little to encourage their practical application. This paper provides a clear and accessible overview of shuttle operations and a comparison of the main signal control types while addressing some knowledge gaps, such as whole-day operation efficiency. The relations between different variables of the signal plan and traffic flow are derived to build a theoretical framework and models for both deterministic and random arrivals to estimate delays and to find the optimal signal plan setting for a wide range of circumstances. The hypothetical scenarios are supported by a case study. Traffic and signal control data from several construction phases of a work zone with shuttle operation were gathered, processed, analysed, and used to compare different control scenarios. The results provide solid evidence for the efficiency of the dynamic systems. The magnitude of the difference is heavily affected by circumstances. The efficiency of the green signal almost doubled with the dynamic control. The case study also revealed a severe impact of road conditions (milling) on the work zone capacity. Several standards and policies are proposed based on the findings to encourage wider and more efficient adoption of traffic-actuated signals in work zones.

A Comprehensive examination of utilizing neutrosophic parameters in manufacturing industry through queueing approach

Queueing theory plays a pivotal role in analyzing the dynamics of manufacturing systems subject to random arrivals and service times. The M/G/1 queueing model, which represents a single-server queue with general distributions for inter-arrival and service times, is fundamental in this regard. However, traditional queueing models often fail to account for the inherent uncertainties encountered in real-world manufacturing scenarios. Neutrosophic theory provides a robust framework for modeling indeterminacy, ambiguity, and inconsistency in queueing parameters, thereby offering a more adaptable and precise depiction of system behavior. This research investigates the utilization of Neutrosophic sets in defining arrival rates, service times, and queue length distributions within the M/G/1 framework tailored to manufacturing settings. By employing numerical examples and comparative analyses, the study explores the effects of Neutrosophic parameters on key performance indicators such as mean waiting time, queue length distribution, and server utilization, considering distributions like Exponential, Erlang, and Deterministic. Additionally, it delves into the implications of integrating Neutrosophic parameters into queueing theory, providing valuable insights into improved decision-making and system optimization across various manufacturing operational contexts.

Dynamic flexible job shop scheduling based on deep reinforcement learning

The unpredictable dynamic events in smart factory seriously influence the scheduling schemes and production efficiency. To minimize the total tardiness of orders, this paper proposes a Deep Reinforcement Learning (DRL) method to solve the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) with random job arrival. In the scheduling process, the intelligent agent can select the operations to be processed on the available machines according to the job shop state at each scheduling point by transforming DFJSP into a Markov Decision Process (MDP). For reflecting the job shop environment, eight state features are extracted, and six composite dispatching rules are designed as the action space. Moreover, a reward function is developed to enhance the learning efficiency and solution quality of the intelligent agent, the Soft-max selection strategy is applied to balance exploration and exploitation. The Dueling Double Deep Q-Network (Dueling DDQN) algorithm is introduced to train the intelligent agent, which effectively alleviates the overestimation problem. Numerical simulation results show that the proposed Dueling DDQN method effectively solves the DFJSP. Compared with existing scheduling strategies, the proposed approach not only exhibits superior performance but also maintains its advantages among different scale instances.

Beam Management in Low Earth Orbit Satellite Networks With Random Traffic Arrival and Time-Varying Topology

Low earth orbit (LEO) satellite communication networks have been considered as promising solutions to providing high data rate and seamless coverage, where satellite beam management plays a key role. However, due to the limitation of beam resource, dynamic network topology, beam spectrum reuse, time-varying traffic arrival and service continuity requirement, it is challenging to effectively allocate time-frequency resource of satellite beams to multiple cells. In this paper, aiming at reducing time-averaged beam revisit time and mitigate intersatellite handover, a beam management problem is formulated for dynamic LEO satellite communication networks, under inter-cell interference and network stability constraints. Particularly, intercell interference constraints are further simplified into off-axis angle based constraints, which provide tractable rules for spectrum sharing between two beam cells. To deal with the long-term performance optimization, the primal problem is transformed into a series of single epoch problems by adopting Lyapunov optimization framework. Since the transformed problem is NPhard, it is further divided into three subproblems, including serving beam allocation, beam service time allocation and serving satellite allocation. With the help of conflict graphs built with off-axis angle based constraints, serving beam allocation and beam service time allocation algorithms are developed to reduce beam revisit time and cell packet queue length. Then, we further develop a satellite-cell service relationship optimization algorithm to better adapt to dynamic network topology. Compared with baselines, numerical results show that our proposal can reduce average beam revisit time by 20.8% and keep strong network stability with similar inter-satellite handover frequency.

Distributed optimal scheduling for EVs charging and discharging: A penalty-based consensus approach

Distributed optimal scheduling for EVs charging and discharging: A penalty-based consensus approach

Modeling residual-vehicle effects on uncertainty estimation of the connected vehicle penetration rate

Modeling residual-vehicle effects on uncertainty estimation of the connected vehicle penetration rate

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Optimization of Single-user Task Migration based on Improved DDPG

Aiming at the problems of slow convergence and unstable convergence of traditional reinforcement learning algorithms in minimizing computational cost on edge servers with random task arrivals and time-varying wireless channels, an improved DDPG algorithm (IDDPG) was proposed. The Critic network structure of DDPG was replaced by the Dueling structure, which converged faster by splitting the state value function into an advantage function and a value function. The update frequency of the Critic network was adjusted to be higher than that of the Actor-network to make the overall training more stable. The Ornstein- Uhlenbeck noise was added to the actions selected through the Actor-network to improve the algorithm exploration ability, and the action noise size was set in segments to ensure the stability of convergence. Experimental results show that, compared with other algorithms, the IDDPG algorithm can better minimize the computational cost and has a certain improvement in the convergence speed and convergence stability.

Data‐driven inventory control involving fixed setup costs and discrete censored demand

AbstractWe investigate a data‐driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long‐run average performances. When demand has a finite support, we can accomplish a regret bound in the order of which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between and depending on the nature of moment‐related bounds that help characterize the degree of ambiguity. These are improvable to when distributions are light‐tailed. Our simulation demonstrates the merits of various policy ideas.