Arrival Of New Jobs Research Articles

Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization

Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some of the optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which is integrating the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which is detailing the various approaches taken into consideration while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered, and a rival algorithm for DOPs.

S-Graph-Based Reactive Scheduling with Unexpected Arrivals of New Orders

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method.

A discrete event simulator to implement deep reinforcement learning for the dynamic flexible job shop scheduling problem

The job shop scheduling problem, which involves the routing and sequencing of jobs in a job shop context, is a relevant subject in industrial engineering. Approaches based on Deep Reinforcement Learning (DRL) are very promising for dealing with the variability of real working conditions due to dynamic events such as the arrival of new jobs and machine failures. Discrete Event Simulation (DES) is essential for training and testing DRL approaches, which are based on the interaction of an intelligent agent and the production system. Nonetheless, there are numerous papers in the literature in which DRL techniques, developed to solve the Dynamic Flexible Job Shop Problem (DFJSP), have been implemented and evaluated in the absence of a simulation environment. In the paper, the limitations of these techniques are highlighted, and a numerical experiment that demonstrates their ineffectiveness is presented. Furthermore, in order to provide the scientific community with a simulation tool designed to be used in conjunction with DRL techniques, an agent-based discrete event simulator is also presented.

Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem

The distributed permutation flowshop problem (DPFSP) has been extensively studied in recent years. However, most of the research has overlooked the disturbance factors in the processing environment, such as the arrival of new jobs. To address this issue, a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning has been developed. First, an iterated greedy algorithm (IG) is proposed to generate an initial solution for the first stage. Then, the NSGA-II algorithm is designed to optimize dual-objective problems in the second stage, and the Q-learning algorithm is used to adjust the algorithm parameters. Next, two local search strategies based on key factories are adopted, including critical factory-based insert and swap operations. Finally, a comprehensive experiment of the proposed algorithm against other advanced multiobjective algorithms is conducted. The results confirm that the proposed algorithm can solve the distributed permutation flowshop rescheduling problem with high efficiency.

Real-time multi-factory scheduling in Industry 4.0 with virtual alliances

The ever-changing and dynamic market environment requires applying job shop systems based on real-time data. The establishment of physical-virtual systems in the production process has led to the emergence of intelligent factories. Compared with those employing traditional production methods, such factories manufacture products with higher quality, higher production speed, and other economic benefits. Regarding the virtual connections of factories, events such as the arrival of new jobs, and machine breakdowns, are identified by Radio Frequency Identification System between different production units, and related decisions are made quickly and carefully. In this intelligent job shop scheduling, it is assumed that independent factories create virtual production networks in which, each factory focuses on its own interests. Regarding the importance of this issue in today’s industry, this research explores the real-time scheduling problem in multi-agent production networks distributed in smart factories. Since this problem is a combination of static scheduling and real-time scheduling, a bi-objective model of mixed-integer linear programming is first developed. An approach is then proposed to solve the dynamic real-time scheduling problem. In addition, a learning-based memetic algorithm for solving large-size bi-objective instances is proposed due to the NP-hardness of the considered problem. Afterward, the results of the proposed algorithm are compared with the hybrid Pareto-based tabu search algorithm. The computational results show that in large-size instances, the proposed algorithm outperforms the competing algorithm.

Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem

In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to deal with the disturbance of the uncertainty of the arrival of new jobs in the job shop. The implementation process is as follows: combine data mining, discrete event simulation, and dispatching rules (DRs), take makespan and machine utilization as scheduling criteria, divide the manufacturing system production period into multiple scheduling subperiods, and build a dynamic scheduling model that assigns DRs to subscheduling periods in real-time; the scheduling strategies are generated at the beginning of each scheduling subperiod. The experiments showed that the method proposed enables a reduction in the makespan of 2–17% and an improvement in the machine utilization of 2–21%. The constructed scheduling model can assign the optimal DR to each scheduling subperiod in real-time, which realizes the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system.

Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems

Most production environments face random, unexpected events such as machine failure, uncertain processing times, the arrival of new jobs, and cancellation of jobs. For the reduction of the undesirable side effects of an unexpected disruption, the initial schedule needs to be reformed partially or entirely. In this paper, a mathematical model is presented to address the integrated cell formation and cellular rescheduling problems in a cellular manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system. Based on the principle of resistance to change, the reactive model seeks a new solution with the minimum difference from the initial solution. This is realized through a simultaneous minimization of the total completion time and the number of displaced machines. For the investigation of the performance of the proposed model, some numerical examples are solved using the GAMS software. The results demonstrate the ability of the reactive model to obtain solutions resistant to unexpected changes

Fast and efficient algorithms to handle the dynamism in a single machine scheduling problem with sequence-dependent setup times

In this paper we study the makespan minimization in a dynamic single-machine scheduling problem with sequence-dependent setup times, where the dynamism is triggered by the arrival of new jobs through the production process, with unknown, in advance, release times. To solve it we use the periodic rescheduling approach, where the production horizon is divided into time intervals and a re-optimization process is launched at the beginning of each interval to obtain an initial schedule which is generally modified due to the arrival of new jobs. We implement two rescheduling strategies. The first one uses all the available jobs at the beginning of each interval to obtain a sequence with minimum makespan. In the second strategy, instead of scheduling all available jobs, we design an iterative insert-improve algorithm that tries to schedule only those jobs that can be completed until the next re-optimization point. Its aim is not to spend time scheduling jobs that could later be rescheduled. To implement these two strategies we design a constructive procedure and three improvement procedures: a composite local search and other two based on the Iterated Greedy metaheuristic. Using these strategies we integrate three algorithms for each strategy. These algorithms are easy to code in a computer language due to the simplicity of the designed components. Moreover, the computational experimentation on instances with different levels of dynamism showed that the proposed algorithms are very fast and provide high quality solutions.

Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments

The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment.

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.

Outsourcing and rescheduling for a two-machine flow shop with the disruption of new arriving jobs: A hybrid variable neighborhood search algorithm

This paper addresses the two-machine flow shop outsourcing and rescheduling problem (TFSORP) in response to an unexpected arrival of new jobs, in which each job can be processed in the in-house shop or outsourced to a single subcontractor. An efficient schedule is constructed for the in-house jobs, and its performance is measured by the makespan. Each of the outsourced jobs requires paying an outsourcing cost. The objective is to minimize the sum of the in-house makespan and total outsourcing cost, subject to a limit on the number of original jobs processed by the subcontractor after disruption. To solve this NP-hard problem, a mixed integer programming formulation and helpful optimization properties are first established, and then a hybrid variable neighborhood search algorithm (HVNS) that incorporates one-dimensional array representation and three problem-specific neighborhood structures is developed. At the initialization stage, either of two well-designed constructive heuristics, i.e., job addition heuristic (JAH) and job removal heuristic (JRH) is employed. At the iterative search stage, an adaptive best-improvement procedure based on three neighborhood structures is devised to play the role of local search. An extensive experimental calibration for main factors in HVNS is carried out by a full-factorial design of experiments. To validate the calibrated HVNS, computational experiments are conducted upon the test suite of various instances. As demonstrated in the results, the calibrated HVNS gains the advantage over both the commercial optimizer and exhaustive approach in terms of computation time for small-sized instances, while it also outperforms an existing meta-heuristic algorithm and two canonical ones (based on the principles of genetic algorithm and simulated annealing) in the optimizing capability.

Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions

In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.

Energy efficient temporal load aware resource allocation in cloud computing datacenters

Cloud computing datacenters consume huge amounts of energy, which has high cost and large environmental impact. There has been significant amount of research on dynamic power management, which shuts down unutilized equipment in a datacenter to reduce energy consumption. The main consumers of power in a datacenter are servers, communications network and the cooling system. Optimization of power in a datacenter is a difficult problem because of server resource constraints, network topology and bandwidth constraints, cost of VM migration, the heterogeneity of workloads and the servers. The arrival of new jobs and departure of completed jobs also create workload heterogeneity in time. As a result, most of the previous research has concentrated on partial optimization of power consumption, which optimizes either server and/or network power consumption through placement of VMs. Temporal load aware optimization, minimization of power consumption as a function of time has vastly been studied. When optimization also included migration, then solution had been divided into two steps, in the first step optimization of server and/or network power consumption is performed and in the second step migration of VMs has been taken care of, which is not an optimal solution. In this work, we develop joint optimization of power consumption of servers, network communications and cost of migration with workload and server heterogeneity subject to resource and bandwidth constraints through VM placement. Optimization results in an integer quadratic program (IQP) with linear/quadratic constraints in number of VMs assigned to a job on a server. IQP can only be solved for very small size systems, however, we have been able to decompose IQP to master and pricing sub-problems which may be solved through column generation technique for systems with larger sizes. Then, we have extended the optimization to manage temporal heterogeneity of the workload. It is assumed that time-axis is slotted and at the end of each slot jobs makes probabilistic complete/partial release of the VMs that they are holding and there will also be new job arrivals according to a Poisson process. The system will perform re-optimization of power consumption at the end of each slot that also includes the cost of VM migration. In the re-optimization, VMs of unfinished jobs may experience migration while new jobs are assigned VMs. We have obtained numerical results for optimal power consumption for the system as well as its power consumption due to two heuristic VM assignment algorithms. The results show optimization achieves significant power savings compared to the heuristic algorithms. We believe that our work advances state-of-the art in dynamic power management of datacenters and the results will be helpful to cloud service providers in achieving energy saving.

On Rescheduling for Two-Machine Flow Shop System with the Arrival of New Jobs

SYNOPTIC ABSTRACTIn this paper, we consider the rescheduling problem for a two-machine flow shop system with the arrival of new jobs, in which a set of original jobs has been given a planned schedule (but has not been executed) on a two-machine flow shop system to minimize the makespan, when a set of new jobs arrives. The production planner needs to insert the new jobs into the planned schedule without excessively disrupting it to minimize the makesapn on the original and the new jobs. The disruption is measured by the maximum time deviation for any given original job, between the original and new schedules. We examine two special models and a general model. In the first special model, the maximum time deviation appears as a constraint. In the second special model, the maximum time deviation appears as the part of the cost objective. In the general model, the maximum time deviation appears both as a constraint and as the part of the cost objective. For the two special models, we prove that they are NP-hard and accordingly the general model is also NP-hard. Furthermore we propose three heuristic algorithms to solve the general model followed by a numerical example.

A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study

In industrial environments, scheduling systems often operate under dynamic and stochastic circumstances. In these conditions, it is inevitable to encounter some disruptions which are inherently stochastic or totally unexpected events. These disruptions may cause the initial schedule to become infeasible and non-optimal. So, appropriate revisions and rescheduling methods are needed to overcome the unfavorable subsequent of these disruptions. In this paper, we address a dynamic flexible flow shop (FFS) environment considering unexpected arrival of new jobs into the process as disruptions. A novel reactive model is proposed based on a classical objective function (total weighted tardiness) and two new surrogate measures, stability and resistance to change. In fact the proposed model is presented to generate a stable reschedule against of any possible occurrences of mentioned disruption. Due to the computational complexity, a variable neighborhood search (VNS) algorithm is implemented to solve the problem. To show the performance of the reactive approach, a case study in petrochemical industry is studied. Computational experiments and comparisons of the proposed algorithm with three dispatching rule and an efficient rescheduling approach show the efficiency of the presented reactive approach to reschedule the jobs.

A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling

This paper considers dynamic multi-objective machine scheduling problems in response to continuous arrival of new jobs, under the assumption that jobs can be rejected and job processing time is controllable. The operational cost and the cost of deviation from the baseline schedule need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with a differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.

Integrated rescheduling and preventive maintenance for arrival of new jobs through evolutionary multi-objective optimization

In this paper, we study a rescheduling problem in response to arrival of new jobs in single machine layout, where preventive maintenance should be determined. Preventive maintenance together with controllable processing time could alleviate the inherent deteriorating effect in manufacturing system. Processing sequence of original and new jobs, compression of each job, and position of maintenance should be optimized simultaneously with regards to total operational cost (job's total completion times, maintenance cost and compression cost) and total completion time deviation. An improved elitist non-dominated sorting genetic algorithm (NSGA-II) has been proposed to solve the rescheduling problem. To address the key problem of balancing between exploration and exploitation, we hybridize differential evolution mutation operation with NSGA-II to enhance diversity, constitute high-quality initial solution based on assignment model for exploitation, and incorporate analytic property of non-dominated solutions for exploration. Finally computational study is designed by randomly generating various instances with regards to the problem size from given distributions. By use of existing performance indicators for convergence and diversity of Pareto fronts, we illustrate the effectiveness of the hybrid algorithm and the incorporation of domain knowledge into evolutionary optimization in rescheduling.

Minimising the total weighted tardiness and instability for the parallel machine re-scheduling problem with deadlines and ready times

The parallel machine rescheduling problem (PMRP) assumes that the jobs have different ready times and due date-to-deadline windows. PMRP can be defined as updating the existing schedule after being disrupted by job-related events including the arrival of new jobs, departure of an existing job, and changes to job priority. In PMRP, bi-criteria objective function is considered to simultaneously minimise both the total weighted tardiness (schedule quality) and schedule instability. We first formulate the PMRP by revising and extending the mixed integer linear programming (MILP) model of the scheduling problem in Kaplan and Rabadi (2012). Five heuristic algorithms, namely MetaRE, BestINSERT, SEPRE, LSHIFT and SHUFFLE, are introduced to handle the bi-objective PMRP using the developed algorithms by Kaplan and Rabadi (2012, 2013). Experiments have been conducted for small size (12 jobs) and large size (60 size) problems for different types of disruptions.

Single-machine rescheduling with deterioration and learning effects against the maximum sequence disruption

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

Process planning and scheduling integration with optimal rescheduling strategies

It is of more practical significance to carry out research on the integrated process planning and scheduling/rescheduling (IPPSR) to achieve a global improvement for the performance of a manufacturing system with sustained pursuit and various uncertainties. In this paper, a framework and a unified dynamic rescheduling model for IPPSR with three typical types of situations normally encountered in a production system, i.e. arrival of new jobs, machine breakdown and order cancellation have been constructed. Meanwhile, an improved evolutionary algorithm (IEA) for the integrated process planning and scheduling (IPPS) to generate an optimal initial scheduling plan has been developed. In order to improve the performance of the algorithm for IPPS and facilitate the extension of it to rescheduling situation, new genetic representation for the scheduling plan combined with process plans and corresponding genetic operators are developed. Based on the dynamic rescheduling model and initial optimised result, the integrated rescheduling strategies for the above three types of situations have been developed. In order to verify the feasibility and performance of the proposed strategies, experimental studies were conducted and comparisons were made for different performance measures; the results show that the proposed approaches not only provide more effective result for IPPS but also for integrated rescheduling with the above three types of uncertainties.