Job Arrivals Research Articles

Dynamic Weighted Fairness with Minimal Disruptions

In this paper, we consider the following dynamic fair allocation problem: Given a sequence of job arrivals and departures, the goal is to maintain an approximately fair allocation of the resource against a target fair allocation policy, while minimizing the total number of disruptions, which is the number of times the allocation of any job is changed. We consider a rich class of fair allocation policies that significantly generalize those considered in previous work.

Symmetry Exploitation for Online Machine Covering with Bounded Migration

Online models that allow recourse can be highly effective in situations where classical online models are too pessimistic. One such problem is the online machine covering problem on identical machines. In this setting, jobs arrive one by one and must be assigned to machines with the objective of maximizing the minimum machine load. When a job arrives, we are allowed to reassign some jobs as long as their total size is (at most) proportional to the processing time of the arriving job. The proportionality constant is called the migration factor of the algorithm. Using a rounding procedure with useful structural properties for online packing and covering problems, we design first a simple (1.7 + ε)-competitive algorithm using a migration factor of O(1/ε), which maintains at every arrival a locally optimal solution with respect to the Jump neighborhood. After that, we present as our main contribution a more involved (4/3+ε)-competitive algorithm using a migration factor of Ō (1/ε 3 ). At every arrival, we run an adaptation of the Largest Processing Time first (LPT) algorithm. Since the new job can cause a complete change of the assignment of smaller jobs in both cases, a low migration factor is achieved by carefully exploiting the highly symmetric structure obtained by the rounding procedure.

On competitive analysis for polling systems

AbstractPolling systems have been widely studied, however most of these studies focus on polling systems with renewal processes for arrivals and random variables for service times. There is a need driven by practical applications to study polling systems with arbitrary arrivals (not restricted to time‐varying or in batches) and revealed service time upon a job's arrival. To address that need, our work considers a polling system with generic setting and for the first time provides the worst‐case analysis for online scheduling policies in this system. We provide conditions for the existence of constant competitive ratios, and competitive lower bounds for general scheduling policies in polling systems. Our work also bridges the queueing and scheduling communities by proving the competitive ratios for several well‐studied policies in the queueing literature, such as cyclic policies with exhaustive, gated or l‐limited service disciplines for polling systems.

Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns

The utilization of real-time information in production scheduling decisions becomes possible with the help of new developments in Information Technology and Industrial Informatics, such as Industry 4.0. Regardless of the beliefs that the availability of such information will enhance scheduling decisions, several questions and concerns have been reported. One such question is to what extent can the availability of real-time information enhance scheduling decisions? Another concern is how can such information be utilized to advance scheduling decisions and when should it be used? Moreover, there is a general assumption that continuous rescheduling using real-time system updates is beneficial to some extent. However, this general assumption has not been extensively investigated in complex manufacturing systems, such as flexible job shops. Therefore, in this paper, our objective is to study the above-mentioned research questions by developing real-time scheduling (RTS) models for the flexible job-shop scheduling problem (FJSP) with unexpected new job arrivals and machine random breakdowns. We investigate how real-time updates on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations can be utilized to generate new schedules (i.e., rescheduling). The performance of the developed RTS models is also investigated under different settings for shop-floor events, different rescheduling strategies, rescheduling policies, and scheduling methods. Lastly, results, conclusions, and several promising research avenues are provided.

Dynamic Weighted Fairness with Minimal Disruptions

In this paper, we consider the following dynamic fair allocation problem: Given a sequence of job arrivals and departures, the goal is to maintain an approximately fair allocation of the resource against a target fair allocation policy, while minimizing the total number of \em disruptions, which is the number of times the allocation of any job is changed. We consider a rich class of fair allocation policies that significantly generalize those considered in previous work. We first consider the models where jobs only arrive, or jobs only depart. We present tight upper and lower bounds for the number of disruptions required to maintain a constant approximate fair allocation every time step. In particular, for the canonical case where jobs have weights and the resource allocation is proportional to the job's weight, we show that maintaining a constant approximate fair allocation requires Θ(łog^* n) disruptions per job, almost matching the bounds in prior work for the unit weight case. For the more general setting where the allocation policy only decreases the allocation to a job when new jobs arrive, we show that maintaining a constant approximate fair allocation requires Θ(łog n) disruptions per job. We then consider the model where jobs can both arrive and depart. We first show strong lower bounds on the number of disruptions required to maintain constant approximate fairness for arbitrary instances. In contrast we then show that there there is an algorithm that can maintain constant approximate fairness with $O(1)$ expected disruptions per job if the weights of the jobs are independent of the jobs arrival and departure order. We finally show how our results can be extended to the setting with multiple resources.

Surrogate-Assisted Symbiotic Organisms Search Algorithm for Parallel Batch Processor Scheduling

Parallel batch processor scheduling with dynamic job arrival is complex and challenging in semiconductor manufacturing. In order to get its reliable and high-performance schedule in a reasonable time, this work decomposes this scheduling problem into two-stage solution strategy: a batch forming subproblem and a batch scheduling subproblem. The batch formation is made by a heuristic rule. Then, a surrogate-assisted symbiotic organisms search algorithm with a new encoding mechanism is utilized to search for the optimal batch schedule, which integrates a surrogate model and a parameter control scheme. The surrogate model, which can predict the sequencing result instead of time-consuming true fitness evaluation, is used to reduce the computational burden greatly. In this article, a parameter control scheme based on reinforcement learning is proposed to balance the global and local search of symbiotic organisms search algorithm, as a guide for searching an assignment scheme. Finally, the experimental results demonstrate that the proposed algorithm can significantly improve the quality of a solution and save computational time via parameter control scheme and surrogate model.

A new weighted distance-based approximation methodology for flow shop scheduling group decisions under the interval-valued fuzzy processing time

Scheduling plays a significant role in production planning. This paper introduces a new extension of a weighted distance-based approximation (WBDA) methodology to determine the sequence of jobs in flow shop scheduling problems. Furthermore, a new version of WDBA is used to specify the decision-makers’ weights. In reality, there are many inherent uncertainties in the processing time owing to the batch loading, the capacity of processing unit, operator skills, transformation quality of raw materials in the production systems, imperfect information regarding systems, transportation lag, traffic jam, machine disablements, arrival of new jobs, and resources deficiencies. In this situation, interval-valued fuzzy sets (IVFSs) are employed for considering the uncertainty of practical conditions. Finally, an illustrative example of the literature under different weight schemes is adopted and solved to address the strengths of the introduced methodology better.

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

In modern manufacturing industry, dynamic scheduling methods are urgently needed with the sharp increase of uncertainty and complexity in production process. To this end, this paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under new job insertions aiming at minimizing the total tardiness. Without lose of generality, the DFJSP can be modeled as a Markov decision process (MDP) where an intelligent agent should successively determine which operation to process next and which machine to assign it on according to the production status of current decision point, making it particularly feasible to be solved by reinforcement learning (RL) methods. In order to cope with continuous production states and learn the most suitable action (i.e. dispatching rule) at each rescheduling point, a deep Q-network (DQN) is developed to address this problem. Six composite dispatching rules are proposed to simultaneously select an operation and assign it on a feasible machine every time an operation is completed or a new job arrives. Seven generic state features are extracted to represent the production status at a rescheduling point. By taking the continuous state features as input to the DQN, the state–action value (Q-value) of each dispatching rule can be obtained. The proposed DQN is trained using deep Q-learning (DQL) enhanced by two improvements namely double DQN and soft target weight update. Moreover, a “softmax” action selection policy is utilized in real implementation of the trained DQN so as to promote the rules with higher Q-values while maintaining the policy entropy. Numerical experiments are conducted on a large number of instances with different production configurations. The results have confirmed both the superiority and generality of DQN compared to each composite rule, other well-known dispatching rules as well as the stand Q-learning-based agent.

Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Integrating energy savings into production efficiency is considered as one essential factor in modern industrial practice. A lot of research dealing with energy efficiency problems in the manufacturing process focuses solely on building a mathematical model within a static scenario. However, in the physical world shop scheduling problems are dynamic where unexpected events may lead to changes in the original schedule after the start time. This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs. Because of the NP hardness of this problem, a dual heterogeneous island parallel genetic algorithm with the event driven strategy is developed. To reach a quick response in the dynamic scenario, the method we propose is made with a two-level parallelization where the lower level is appropriate for concurrent execution within GPUs or a multi-core CPU while codes from the two sides can be executed simultaneously at the upper level. In the end, numerical tests are implemented and display that the proposed approach can solve the problem efficiently. Meanwhile, the average results have been improved with a significant execution time decrease.

An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns

With rising energy prices and environmental concerns, reduction of energy consumption has become a critical manufacturing focus. One appropriate way to reduce energy consumption in manufacturing systems is to develop energy-conscious optimization strategies for production planning. In a flexible machining job shop, this planning must accommodate common dynamic events, such as new job arrivals and machine breakdowns. Dynamic events could change production energy consumption, thus require plan changes in pursuit of energy consumption reduction. To this end, this paper proposes an energy-conscious optimization method in flexible machining job shops considering dynamic events. In this paper, a optimization method which updates the jobs and machine plan status when dynamic events occur is proposed. The method considers two states for machine tool energy consumption: actual machining and machine idling/stand-by. The optimization model considers the total energy consumption and makespan, and employs Non-dominated Sorting Gene Algorithm II (NSGA-II) approach to obtain a solution. The proposed method is evaluated with a test case in which a flexible machining job shop experiences new dynamic job arrivals and machine breakdowns. The results show that the proposed method is effective at adjusting the schedule in response to dynamic events.

Improved Genetic Algorithm Approach Based on New Virtual Crossover Operators for Dynamic Job Shop Scheduling

The realtime manufacturing system is subject to different kinds of disruptions such as new job arrivals, machine breakdowns, and jobs cancellation. These different disruptions affect the original schedule that should be updated to maintain the system’s performance. An effective re-scheduling is required in this situation to make better utilization of the system resources. This paper studies the dynamic job shop scheduling problem. The problem is known as strongly NP Hard optimization problem where new jobs are unconditionally arrived at the system. Hence, to deal with system changes and performing hard tasks scheduling, we propose an evolutionary genetic algorithm based on virtual crossover operators. Experimental results are compared with state-of-the-art heuristics and metaheuristics dedicated for evaluating large scale instances. Simulation results show the efficiency of the proposed virtual crossover operators integrated into the genetic algorithm approach.

Integrisano planiranje i terminiranje tehnoloških procesa u dinamičkim uslovima - pregled stanja u oblasti istraživanja

This paper gives a detailed state-of-the art in the research area o f the important function o f Intelligent Manufacturing Systems (IMS) - integrated process planning and scheduling o f manufacturing systems in dynamic environment (DIPPS). Referring to this, description o f the DIPPS problem is given, the criteria on the basis o f which the optimal rescheduling plan are formulated and considered, the adopted assumptions are defined and the mathematical model o f this problem is presented. Furthermore, the disturbances that occur in manufacturing systems are considered in detail: (i) machine breakdown, (ii) arrival of a new job and (iii) job cancellation. Approaches for solving DIPPS problems based on multiagent systems as well as approaches based on algorithms are analyzed. When it comes to approaches based on algorithms, the focus of this paper is on biologically inspired optimization algorithms: evolutionary algorithms, swarm intelligence based algorithms as well as hybrid approaches. The critical analysis within this research area is shown in order to conclude that biologically inspired artificial intelligence techniques have great potential in optimizing the considered IMS function.

Meta-heuristics for dynamic real time scheduling of diffusion furnace in semiconductor manufacturing industry

Most of the earlier research in dynamic scheduling (DS) of diffusion furnace (DF), considers only future arrival of jobs. However, in reality along with the future arrival of jobs, various unexpected real time events (RTE) related to jobs, and/or resources will occur. Hence, this study addresses the important real life characteristics of both future arrival jobs and the occurrence of a RTE while scheduling DF, called as dynamic real time scheduling (DRTS), with the scheduling objective of minimising total weighted tardiness (TWT). This study first explains the mathematical model for DS of single DF to minimise TWT. Then, this study proposes 12 variants of meta-heuristics (six variants of simulated annealing and six variants of tabu search) by considering six different initial solutions obtained from six variants of greedy heuristic algorithm for DRTS of DF. From empirical and statistical analyses on 270 problem instances, this study observed that one of the variants of simulated annealing consistently performing better.

Meta-heuristics for dynamic real time scheduling of diffusion furnace in semiconductor manufacturing industry

Most of the earlier research in dynamic scheduling (DS) of diffusion furnace (DF), considers only future arrival of jobs. However, in reality along with the future arrival of jobs, various unexpected real time events (RTE) related to jobs, and/or resources will occur. Hence, this study addresses the important real life characteristics of both future arrival jobs and the occurrence of a RTE while scheduling DF, called as dynamic real time scheduling (DRTS), with the scheduling objective of minimising total weighted tardiness (TWT). This study first explains the mathematical model for DS of single DF to minimise TWT. Then, this study proposes 12 variants of meta-heuristics (six variants of simulated annealing and six variants of tabu search) by considering six different initial solutions obtained from six variants of greedy heuristic algorithm for DRTS of DF. From empirical and statistical analyses on 270 problem instances, this study observed that one of the variants of simulated annealing consistently performing better.

An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals

Abstract Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability of a schedule in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm.

An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals

We present an optimally-competitive algorithm for the problem of maximum online perfect bipartite matching with i.i.d. arrivals. In this problem, we are given a known set of workers, a distribution over job types, and non-negative utility weights for each pair of worker and job types. At each time step, a job is drawn i.i.d. from the distribution over job types. Upon arrival, the job must be irrevocably assigned to a worker and cannot be dropped. The goal is to maximize the expected sum of utilities after all jobs are assigned. We introduce Dispatch, a 0.5-competitive, randomized algorithm. We also prove that 0.5-competitive is the best possible. When a job arrives, Dispatch first selects a “preferred worker” and assigns the job to this worker if it is available. The preferred worker is determined based on an optimal solution to a fractional transportation problem. If the preferred worker is not available, Dispatch randomly selects a worker from the available workers.

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

PurposeIn real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.Design/methodology/approachOwing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.FindingsFinally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.Originality/valueRobustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

Heuristics for Online Scheduling on Identical Parallel Machines with Two GoS Levels

This paper considers the online scheduling problem on m (m ≥ 3) parallel machines (the first k machines with grade 1 and the remaining m − k machines with grade 2) with two GoS levels and makespan as the objective function. The jobs arrive over time with grade 1 or 2 and an arrival job can be assigned to a machine only when the grade of the job is no less than the grade of the machine. Three cases are considered: (i) For k = 1, the authors present an online algorithm with competitive ratio of 9/5. (ii) For 1 < k < m − 1, an online algorithm with competitive ratio of 2.280 is proposed. (iii) For k = m − 1, an online algorithm is presented with competitive ratio of 2. All the three algorithms are based on greedy algorithm with a similar structure. At last, numerical instances are given and the average competitive ratios of the instances show good performance of the proposed algorithms.

To optimize load of hybrid P2P cloud data-center using efficient load optimization and resource minimization algorithm

Cloud is the most trending technology used at almost in every part of the business and in every field of business. Cloud provides number of services in wider spectrum to cloud users from anywhere at any time. But, it achieved through several parameters like deployment model, resource optimization, load optimization etc. Nowadays, Load optimization is playing crucial role in cloud computing behalf system performance. The best optimization technique goal is to fulfill the user requirement efficiently with minimal resources and processing time. Parallel task processing is highly demanded in cloud application. The CPU resources are needed to move for parallelism growth due to communication and synchronization of parallel job arrival in cloud. It is difficult but highly demanded for a data center to response arrived task in parallel way. The objective of work is to design Efficient Load Optimization and Resource Minimization (ELORM) algorithm for optimizing the tasks at different Hybrid P2P Cloud data center zones and different users in cloud environment. The works provides an effective way to distribute the resources based on load prediction in the data centers for resource optimization. It enhances the load optimization, by maintaining the reliability and stability between the user base and data center during data transmission process. It also reduces the resource utilization and response time of the proposed algorithm compared than conventional methods. Proposed ELORM reduces 83.13 s Task completion time, 20.82 $ Virtual machine cost, 6.68% load balancing compare than conventional methods.

Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

Nowadays, scheduling problems under different disruptions are a key to become competitive in the global market of this century. Permutation flow shop scheduling problems are very important as they consider one of the important types of scheduling problems. In this paper, we consider a challenging scheduling problem of a permutation flow shop in the presence of different types of real-time events such as new job arrival and machine breakdown. A multi-objective optimisation model that takes into account multiple performance measures in order to minimise the effect of different real-time events is used in this paper. To solve this problem, we apply the proposed multi-objective model and adapt a predictive-reactive based Biased Randomised Iterated Greedy approach for the problem, which is hybridised a Biased Randomisation process and the Iterated Greedy algorithm. Furthermore, the proposed approach is compared against the predictive-reactive based Particle Swarm Optimisation method for the same problem. Additionally, to show the efficiency of the proposed model, we compare this model by testing the predictive-reactive based BRIG approach to two other models: the bi-objective model that consider only two objectives and the classical single-objective model of minimising the makespan. Further statistical analysis is performed in this study by using an Analysis of Variance measure. The extensive experiments and statistical analysis demonstrate that the proposed multi-objective model is better than the other models in reducing the relative percentage deviation. Additionally, despite their simplicity, the BRIG algorithm is shown to be state-of-the-art method that outperforms the Particle Swarm Optimisation algorithm.