Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

Anticipation and flexibility in dynamic scheduling

Many real-world optimization problems change over time and require frequent re-optimization. We suggest that in such environments, an optimization algorithm should reflect the problem's dynamics and explicitly take into account that changes to the current solution are to be expected. We claim that this can be achieved by having the optimization algorithm search for solutions that are not only good, but also flexible, i.e. easily adjustable if necessary in the case of problem changes. For the example of a job-shop with jobs arriving non-deterministically over time, we demonstrate that avoiding early idle times increases flexibility, and thus that the incorporation of an early idle time penalty as secondary objective into the scheduling algorithm can greatly enhance the overall system performance.     

Value creation dynamics in a project alliance

Project alliance requires all parties to work together in good faith, share project risks, and make unanimous decisions for the betterment of the project. A key feature of successful implementation of a project alliance is a focus on value creation and value for money. This paper proposes a qualitative system dynamics model to specify and explain dynamics of value creation processes in the context of project alliance. By synthesizing the existing literature and reports on project alliancing, this paper identifies four processes that have a strong influence on the value created in the project alliance context: work progression, rework, redesign and innovation, and rescheduling. In addition, we show how these value creation processes are interrelated and evolve over time. The effectiveness of these processes is influenced by the capability and motivation of the project alliance partners to discover works that do not fully utilize the available resources, and make quick decisions to capture these benefits.     

Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.     

Self-adaptive dynamic scheduling of virtual production systems

To enhance the agility of virtual production systems (VPSs) under today's dynamic and changing manufacturing environment, a self-adaptive dynamic scheduling method based on event-driven is proposed for VPSs in this paper. This method is composed of the mechanisms and algorithm of self-adaptive dynamic scheduling. In the mechanisms, the dynamic events faced by VPSs are determined through users’ inputs or supervisory controllers’ detections, the local effects made on the schedule are analysed according to the dynamic events, and the self-adaptive measures and rules are specified correspondingly. To implement the dynamic scheduling of VPSs under the guidance of self-adaptive rules, a modified heuristic rescheduling algorithm is proposed for affected operations. A case study illustrates that the proposed method can well accomplish the dynamic scheduling of VPSs in a self-adaptive manner.     

Rescheduling parallel machines with stepwise increasing tardiness and machine assignment stability objectives

Nervousness in machine assignments during rescheduling can cause problems for the implementation of a scheduling system. This paper examines rescheduling due to the arrival of new jobs to the system. Parallel machine scheduling problems with stepwise increasing tardiness cost objectives, non-zero machine ready times, constraints that limit machine reassignments, and machine reassignment costs are considered. Simulation experiments and individual scheduling problems indicate that nervousness can be controlled at a low cost in some parallel machine scheduling environments. The rescheduling problems in the simulation are solved with a branch-and-price algorithm. Significant gains in schedule stability can be achieved by selecting the alternative optimal solution with the fewest machine reassignments.     

Integrating a decomposition procedure with problem reduction for factory scheduling with disruptions: a simulation study

Dispatching rules are widely used in industry because schedules obtained from optimization procedures can be difficult to implement in the face of executional uncertainties. Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) implement global schedules obtained from an optimization-based heuristic using a dispatching rule, and outperform myopic dispatching rules in the face of disruptions. However, the computation of the global schedules is still time-consuming for realistic instances. Upasani et al. (Upasani, A., Uzsoy, R. and Sourirajan, K., A problem reduction approach for scheduling semiconductor wafer fabrication facilities. IEEE Trans. Semicon. Manuf., 2006, 19, 216–225) develop a problem reduction scheme based on load disparity between work centres, and report significant reduction in CPU times with minimal loss of solution quality in deterministic experiments. In this paper we integrate the problem-reduction scheme to obtain global schedules with the dispatching approach of Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) in a multi-product environment with stochastic machine breakdowns and job arrivals. A simulation model of a scaled-down wafer fabrication facility is used to evaluate the performance of the proposed procedures. Results show that the integrated procedure outperforms the benchmark dispatching rules while significantly reducing computation times.     

Dynamic scheduling for complex engineer-to-order products

The current paper considers dynamic production scheduling for manufacturing systems producing products with deep and complex product structures and complicated process routings. It is assumed that manufacturing and assembly processing times are deterministic. Dynamic scheduling problems may be either incremental (where the schedule for incoming orders does not affect the schedule for existing orders) or regenerative (where a new schedule is produced for both new and existing orders). In both situations, a common objective is to minimize total costs (the sum of work-in-progress holding costs, product earliness and tardiness costs). In this research, heuristic and evolutionary-strategy-based methods have been developed to solve incremental and regenerative scheduling problems. Case studies using industrial data from a company that produces complex products in low volume demonstrate the effectiveness of the methods. Evolution strategy (ES) provides better results than the heuristic method, but this is at the expense of significantly longer computation times. It was found that performing regenerative planning is better than incremental planning when there is high interaction between the new orders and the existing orders.     

An empirical study of moving horizon closed-loop demand response scheduling

The potential of electricity-intensive chemical plants to engage in demand response (DR) initiatives in support of power grid operations has been the subject of many conceptual studies. In this work, using an industrially-relevant model of an air separation unit, we undertake an extensive simulation study of moving horizon (MH) scheduling approaches, where we “close the scheduling loop” based on updated information regarding disturbances such as changes in electricity prices, ambient conditions and chemical product demand. Our study produces several unexpected findings regarding the non-periodic nature of rescheduling solutions, the impact of the accuracy of disturbance forecasts on the economics of DR scheduling, and the interplay of simultaneously dealing with fluctuations on both the supply side (i.e., electricity prices) and the product demand side of the plant. We posit that the latter fluctuations pose significant limitations to the potential of a chemical plant to engage in DR.