Scenario-based multi-objective robust scheduling for a semiconductor production line

Scheduling problems of semiconductor manufacturing systems (SMS) with the goal of optimising some classical performance indices (NP-hard), tend to be increasingly complicated due to stochastic uncertainties. This paper targets the robust scheduling problem of an SMS with uncertain processing times. A three-stage multi-objective robust optimisation (MORO) approach is proposed, that can collaboratively optimise the performance indices and their robustness measures. In the first stage, this paper studies the scheduling problem in the deterministic environment and obtains feasible scheduling strategies that perform well in four performance indices (the average cycle time (CT), the on-time delivery rate (ODR), the throughput (TP), and the total movement amount of wafers (MOV)). Then, in the second stage, the uncertainties are introduced into the production system. In the third stage, this paper proposes a hybrid method consisting of scenario planning, discrete simulation, and multi-objective optimisation to obtain an approximately and more robust optimal solution from the feasible scheduling strategy set. The proposed MORO approach is tested in a semiconductor experiment production line and makes a full analysis to illustrate the effectiveness of our method. The results show that our MORO is superior concerning the total robustness with multi-objective.     

Robust multi-objective optimization for energy production scheduling in microgrids

In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.     

A pinwheel packet scheduling scheme for broadband wireless networks

Abstract

A frame‐based packet scheduling scheme, the pinwheel scheduling (PWS) scheme, is proposed and investigated at the medium access control (MAC) layer in broadband wireless networks. The objective of the proposed scheduling scheme is to provide low delay and low jitter for real‐time traffic. We have demonstrated in this paper that the proposed PWS scheme not only satisfies the packet‐level QoS requirements but also has low implementation cost. The PWS scheme gives the highest priority to CBR connections in a service cycle to minimize their delay and jitter. For ABR traffic, a number of slots are allocated to fit their MCR. The remaining time slots are then allocated to VBR traffic according to their PCR. Thus, the VBR traffic may suffer large delay and jitter when the number of CBR connections increases. The PWS with modular strategy (PWS/MS) is proposed to improve the performance of the VBR traffic. We also introduce three different slot‐reuse strategies named real‐time traffic first (RTF), non‐real‐time traffic first (NRTF) and longest queue first (LQF), respectively, to improve resource utilization. In order to demonstrate the performance of the PWS and PWS/MS schemes, comparisons are made with existing schemes such as the round‐robin (RR) and weighted‐round‐robin (WRR) by using OPNET software. Simulation results show that the proposed schemes are capable of maintaining the lowest delay and jitter for VBR and CBR traffic while not sacrificing the available bit rate (ABR) traffic.     

An activity-list-based nested partitions algorithm for resource-constrained project scheduling

The resource-constrained project scheduling problem (RCPSP) has received wide attention. In this paper, an activity-list-based nested partitions algorithm (ALNP) is developed for solving the RCPSP and a P-ALNP is proposed to improve ALNP with local adjustment. In the algorithms, to improve the search efficiency, a partial double justification is employed as local search mechanism. The computational experiments on the PSPLIB and analysis on robustness of the algorithms show that ALNP outperforms the traditional serial scheduling scheme for solving the large-scale, complex RCPSPs, and P-ALNP can improve ALNP and obtain better results. P-ALNP is a competitive algorithm for solving the RCPSP.     

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.     

Integrated process planning and scheduling using an imperialist competitive algorithm

Effective performance of modern manufacturing systems requires integrating process planning and scheduling more tightly, which is consistently challenged by the intrinsic interrelation and intractability of these two problems. Traditionally, these two problems are treated sequentially or separately. Integration of process planning and scheduling (IPPS) provides a valuable approach to improve system performance. However, IPPS is more complex than job shop scheduling or process planning. IPPS is strongly NP-hard in that, compared to an NP-hard job shop scheduling problem with a determined process plan, the process plan for each job in IPPS is also to be optimised. So, an imperialist competitive algorithm (ICA) is proposed to address the IPPS problem with an objective of makespan minimisation. An extended operation-based representation scheme is presented to include information on various flexibilities of process planning with respect to determined job shop scheduling. The main steps of the proposed ICA, including empires construction, assimilation, imperialistic competition, revolution and elimination, are elaborated using an illustrative example. Performance of the proposed ICA was evaluated on four sets of experiments taken from the literature. Computational results of the ICA were compared with that of some existing algorithms developed for IPPS, which validates the efficiency and effectiveness of the ICA in solving the IPPS problem.     

Robustness and stability measures for scheduling: single-machine environment

This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.     

A fuzzy logic modelling of dynamic scheduling in FMS

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.     

A tabu search algorithm for simultaneous machine/AGV scheduling problem

Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous scheduling problem of machines and AGVs in a flexible manufacturing system (FMS) since the global optimum cannot be reached by considering each of them individually. In this paper, a mixed integer linear programming (MILP) model is developed with the objective of makespan minimisation. The MILP model consists of the following two constraint sets: machines and AGVs scheduling sub-problems. As both sub-problems are known to be NP-hard, a heuristic algorithm based on tabu search (TS) is proposed to get optimal or near to optimal solution for large-size problems within reasonable computation time. The proposed algorithm includes a novel two-dimensional solution representation and the generation of two neighbour solutions, which are alternately and iteratively applied to improve solutions. Moreover, an improved lower bound calculation method is introduced for the large-size problems. Computational results show the superior performance of the TS algorithm for the simultaneous scheduling problem.     

考虑疲劳效应的伤员应急手术调度模型及算法

为了解决城市突发事件应急救援中批量应急手术的调度问题,并考虑医生长时间连续手术对手术持续时间和挽救病人生命的成功率带来的恶化效应,提出了三阶段批量手术调度模型,将应急手术调度看作是存在并行机的流水车间调度问题。利用改进的飞蛾扑火算法对应急手术模型进行求解,并通过实证来测试模型和算法的有效性。为了验证算法的性能,将经典飞蛾扑火算法、粒子群算法和布谷鸟算法与其对比,取20次运行结果,得知最大手术完成时间均值中改进的飞蛾扑火算法为最小,调度模型给出的调度方案中,3个救治阶段在时间维度上保持连贯。仿真结果表明,改进的飞蛾扑火算法能很好地求解批量手术调度模型,获得较好的调度结果。     

Scheduling trucks in container terminals using a genetic algorithm

Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.     

混合模拟植物生长算法在包装件配送中的应用

目的针对改进模拟植物生长算法(IPGSA)容易陷入局部最优解及其算法运行时间较长,提出混合模拟植物生长算法(HPGSA)来求解带时间窗车辆调度问题(VSPTW)。方法在IPGSA基础上,提出求解包装件物流配送中VSPTW的混合模拟植物生长算法(HPGSA)。改进IPGSA初始调度方案的构造方式,设计求解VSPTW的C-W算法用于构造HPGSA的初始调度方案;改进IPGSA的邻域搜索算子,选择插入搜索算子和互换搜索算子对HPGSA进行邻域搜索;对18个不同规模的Solomon算例进行仿真测试。结果相对于其他智能算法,HPGSA具有更好的求解性能,能够保证VSPTW对求解算法的要求。结论 HPGSA的全局优化能力、稳定性和运行速度均优于IPGSA、遗传算法、蚁群算法和禁忌搜索算法。     

Dynamic co-ordinated scheduling in the supply chain considering flexible routes

In Supply chain (SC) environments, schedules inevitably experience various unexpected disruptions. In these cases, rescheduling is essential to minimise the negative impact on the performance of the system. In this study, a hybrid rescheduling technique is developed for solving coordinated manufacturing tasks scheduling problems with due date changes and machine breakdowns. According to the dynamic features of this problem, a strategy combined with event and periodic driven methods is proposed to improve the stability and robustness of manufacturing performance in a coordinated SC. Moreover, an application case is introduced to test and evaluate the effect of different initialisations in two types of disruption scenarios. The experimental results show that the proposed rescheduling technique has good effectiveness and efficiency in the coordinated manufacturing environment.     

Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks

Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.      

Intelligent scheduling of discrete automated production line via deep reinforcement learning

The reinforcement learning (RL) is being used for scheduling to improve the adaptability and flexibility of an automated production line. However, the existing methods only consider processing time certain and known and ignore production line layouts and transfer unit, such as robots. This paper introduces deep RL to schedule an automated production line, avoiding manually extracted features and overcoming the lack of structured data sets. Firstly, we present a state modelling method in discrete automated production lines, which is suitable for linear, parallel and re-entrant production lines of multiple processing units. Secondly, we propose an intelligent scheduling algorithm based on deep RL for scheduling automated production lines. The algorithm establishes a discrete-event simulation environment for deep RL, solving the confliction of advancing transferring time and the most recent event time. Finally, we apply the intelligent scheduling algorithm into scheduling linear, parallel and re-entrant automated production lines. The experiment shows that our scheduling strategy can achieve competitive performance to the heuristic scheduling methods and maintains stable convergence and robustness under processing time randomness.     

Optimization-based parts-machines matching in flexible manufacturing systems

A new dynamic scheduling strategy, Parts-Machines Matching (PMM), is developed and tested in simulated flexible manufacturing systems. This strategy is aimed to achieve globally optimal matching between parts and machines by a semi-qualitative optimization algorithm, originally developed for the Stable Marriage Problem. Global and Partial implementations of PMM are presented and compared with other conventional part-flow rules. They are found to achieve better shop performance than conventional rules, in terms of system throughput, robustness against travel time uncertainties, and recovery from machine breakdowns. The prospect of bringing about system-wide optimization-based performance improvements into bidding schemes makes the proposed framework very significant.     

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper. With this control scheme, the performance against disturbances, uncertainties, and attenuation is enhanced. Linear active disturbance rejection control (LADRC) is mainly based on an extended state observer (ESO) technology. A fractional integral (FOI) action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC. Incorporating this FOI action improves the robustness of the standard LADRC. The set-point tracking of the proposed FO-LADRC scheme is designed by Bode's ideal transfer function (BITF) based robust closed-loop concept, an appropriate pole placement method. The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system (LFJS). The results show the enhancement of the robustness with disturbance rejection. Furthermore, a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.     

Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming

At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a signi?cant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.     

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.