Cyclic Scheduling Research Articles

Scheduling in Industrial environment toward future: insights from Jean-Marie Proth

According to [Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer.], advancing tactical levels in production systems has led to the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. [Proth, Jean Marie. 2007. “Scheduling: New Trends in Industrial Environment.” Annual Reviews in Control 31 (1): 157–166. https://doi.org/10.1016/j.arcontrol.2007.03.005.] emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains.

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem with cyclic schedules

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem is a hub location problem variant faced by a logistics service provider operating in the context of synchromodal logistics. The provider must decide where and when to locate transshipment facilities in order to manage many customers’ origin–destination shipments with release and due dates while minimizing a total cost given by location costs, transportation costs, and penalties related to unmet time constraints. The considered synchromodal network involves different transportation modes (e.g., truck, rail, river and sea navigation) to perform long-haul shipments and the freight synchronization at facilities for transshipment operations. To the best of our knowledge, this variant has never been studied before. Considering a time horizon in which both transportation services and demand follow a cyclic pattern, we propose a time–space network representation of the problem and an ad-hoc embedding of the time-dependent parameters into the network topology and the arcs’ weight. This allows to model the flow synchronization required by the problem through a Mixed-Integer Linear Programming formulation with a simplified structure, similar to well-known hub location problems and avoiding complicating constraints for managing the time dimension. Through an extensive experimental campaign conducted over a large set of realistic instances, we present a computational and an economic analysis. In particular, we want to assess the potential benefits of implementing synchromodal logistics operations into long-haul supply-chains managed by large service providers. Since flexibility is one of the main features of synchromodality, we evaluate the impact on decisions and costs of different levels of flexibility regarding terminals’ operations and customers’ requirements.

A data-driven strategy for industrial cracking furnace system scheduling under uncertainty

Cyclic scheduling of ethylene cracking furnace system (CSECFS) has a significant impact on raising economic performance of ethylene plants. However, in actual plants, optimal results of deterministic models may be suboptimal or ineffective because of various uncertainties. This paper proposes a novel data-driven adaptive robust optimization (DDARO) strategy that effectively bridges robust optimization and machine learning methods. A mixed-integer nonlinear programming (MINLP) model for CSECFS is developed initially. Second, data-driven uncertainty sets are generated using the historical data of processing flow rates: maximally correlated principal component analysis (MCPCA) is employed to partition the data into two subspaces, which are then delineated by support vector clustering (SVC) and generalized norm approaches, respectively. Third, a multi-stage DDARO model is established using the derived uncertainty sets and afterward transformed as a tractable single-stage model. Finally, a real-world case is performed to exemplify the effectiveness of the proposed framework.

Optimal Cyclic Scheduling of Wafer-Residency-Time-Constrained Dual-Arm Cluster Tools by Configuring Processing Modules and Robot Waiting Time

Optimal cyclic scheduling problems of wafer-residency-time-constrained dual-arm cluster tools in wafer fabrication are challenging and remain to be fully solved. Existing studies assume that all processing modules (PMs) of a required type are used to process the same type of wafers. This sometimes brings unneeded conservation to scheduling results, because we may be able to make a tool schedulable by reducing the number of PMs in some steps if the original one is not. In some cases, we may use fewer PMs to reach the same result if the original one is schedulable. This work selects a proper number of PMs of needed types to process wafers with the highest productivity. It proposes the necessary and sufficient conditions under which a tool is schedulable and develops a polynomial-complexity algorithm that finds an optimal cyclic schedule. Examples are given to show its superiority over existing ones, thus advancing this field greatly.

Optimal infrastructure in microgrids with diverse uncertainties based on demand response, renewable energy sources and two-stage parallel optimization algorithm

Future electric grids will face severe uncertainties with the unprecedented penetration of renewable energy sources, which may cause problems in grid exploitation. It is essential to evaluate the uncertainty of system performance in this grid; therefore, traditional exploitation methods may not be suitable for distributions grid such as multi-carrier microgrids when considering the electricity and gas grid constraints. This paper proposes an effective method for simultaneously optimizing different types of energy infrastructure in an environment with various uncertainties while considering grid constraints. To address the multi-energy synergy supply optimization problem in the micro energy grid, a coordinated two-stage programming approach is also suggested. A day-ahead and real-time phases are separated in the scheduling cycle to overcome the effects of the unpredictability of wind energy and solar power. The findings of the day-ahead forecast are then taken into account as uncertain variables for the higher-layer model. The demand response programming model and the energy storage revision model are lower-layer models considering the actual value as the realization of uncertain variables. The competitive swarm optimization algorithm is used to solve the problem mentioned above The Cumulative search optimization (CSO) uses global and local systems in particle swarm optimization and adopts a novel learning mechanism for creating competition between particle pairs. Comprehensive numeric examinations show that convergence speed and precision are critical, especially in solving large-scale problems. Therefore, we use chaos theory to improve its performance. Finally, the proposed method is tested and discussed on a system. The results showed that: (1) power-to-gas could convert extra electricity into natural gas; (2) the proposed two-stage optimization model and algorithm achieved different energy optimizations; (3) price-based demand response (DR) can level the energy load curve and maximize multi energy grid (MEG) revenue by using complementary specifications of energy price; and (4) the micro energy grid could communicate with the high-degree energy grid in order to gain more economic benefits. Accordingly, the incentive-based demand response (IBDR) output is reduced. The electricity and heating provided by convention gas turbine (CGT) is increased, which is fed to upper power grid (UPG) and upper heating grid (UHG). Since MEG’s purchasing power from UPG during the peak period generates more total revenue than island operations. Based on the obtained results, it can be seen that the proposed algorithm was about 50% faster compared to other methods and its standard deviation was about 0.0013 with different implementations, which was much better compared to other models.

Look-ahead based reinforcement learning for robotic flow shop scheduling

Scheduling of a robotic flow shop, where a dual-gripper robot transports parts between machines, is addressed with a makespan measure. Most previous studies on robotic flow shop scheduling have focused on cyclic scheduling where the robot repeats a certain sequence to process identical parts. Recently, noncyclic scheduling of robotic flow shops is strongly required due to the need for producing customized products and smaller order sizes efficiently. This study therefore considers noncyclic scheduling of a dual-gripper robotic flow shop with a given part sequence and proposes a novel solution approach, look-ahead based reinforcement learning (LARL). The LARL consists of deep Q-learning for training a Q-network based on a given set of instances and the look-ahead search used for testing new instances. The look-ahead search in the LARL is efficient, especially for robotic flow shop scheduling where future state information can be used for determining the current robot task. The experimental results comparing the LARL with an optimal algorithm and the well-known robot task sequence show the effectiveness of the LARL.

Scalable timing-aware network design via lagrangian decomposition

This paper addresses instances of the temporal fixed-charge multi-commodity flow (tfMCF) problem that arise in a very large scale dynamic transportation application. We model the tfMCF as a discrete-time Resource Task Network (RTN) with cyclic schedule, and formulate it as a mixed-integer program. These problems are notoriously hard to solve due to their time-expanded nature, and their size renders their direct solution difficult. We exploit synergies between flows of certain commodities in the formulation to devise model condensation techniques that reduce the number of variables and constraints by a factor of 25%–50%. We propose a solution algorithm that includes balanced graph partitioning, Lagrangian decomposition and a linear programming filtering heuristic. Computational results show that the proposed algorithm allows the solution of previously intractable instances, and the primal solution obtained by the heuristic step is within 2% duality gap of the linear relaxation of the original problem.

The cyclic production routing problem

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher.

A Novel Control-Theory-Based Approach to Scheduling of High-Throughput Screening System for Enzymatic Assay

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given.

Carbon-free energy optimization in intelligent communities considering demand response

With the gradual improvement in people’s quality of life, intelligent communities are received widespread attention and research. However, the massive use of various smart appliances causes a rapid increase in energy demand and carbon emissions. Therefore, in this paper, the novel carbon-free energy optimization method under the designed energy structure suitable for an intelligent community is proposed to deal with energy depletion and environmental pollution. First, the energy supply side makes full use of clean energy generation technologies to provide users with adequate energy, which is composed of photovoltaic (PV), wind turbine (WT) and hydrogen steam turbines. Second, considering the demand response (DR) of the load side, household appliances are modeled according to the working characteristics. Then, the energy treatment mode of renewable energy sources (RESs) grid-connected reverse transmission is changed. The power to hydrogen (P2H) technology is introduced into the energy structure of intelligent residential quarters to increase the proportion of hydrogen energy in the region. Moreover, the energy storage device and electrification technology are combined to realize the production and application of regional hydrogen and achieve the goal of non-carbonization of regional energy structure. Finally, in simulation part, the optimal solution in the scheduling cycle is obtained by YALMIP solver, and time-of-use (TOU) electricity prices are used to calculate the final electricity cost. It is displayed that carbon emissions and energy consumption cost can be reduced, and the pressure of peak power consumption relieved based on the designed non-carbonization energy structure and energy optimization method.

Integrated energy scheduling using wind-storage cascade control strategy in dynamic peak regulation right

• Wind-storage integration and electric-heat units participate in dynamic peak regulation right transaction. • Establish wind-storage cascade control strategy for deeply peak regulation. • Based on whole process peak regulation model of electric-heat units to establish scheduling model. • Quantitatively analyzed the benefits of the participants in different scenarios. • Verified the new scheduling strategy can reduce wind curtailment In view of the serious problem of wind curtailment in heating season of the "Three North Regions" in China, an intraday rolling optimization scheduling method is carried out according to wind-storage cascade control strategy combined with the dynamic peak regulation rights transaction which is based on the wind curtailment situation. Determine the operation state of the energy storage by judging when the wind curtailment is generated, and dynamically adjust the capacity of peak regulation rights transaction in-day according to the amount of wind curtailment. Considering the whole process peak regulation cost model of electric-heat units and wind-storage integration cost, and the goal is to maximize the overall benefit in the scheduling cycle, optimize the operation. Finally modeling by YALMIP and solved by CPLEX. The simulation results shown that the benefit improvement verifies the market feasibility, and the reduction of wind curtailment verifies the technical feasibility.

Two-Level Decoupled Ethylene Cracking Optimization of Batch Operation and Cyclic Scheduling

Two-Level Decoupled Ethylene Cracking Optimization of Batch Operation and Cyclic Scheduling

Blocking Cyclic Job-Shop Scheduling Problems

Cyclic scheduling is of vital importance in a repetitive discrete manufacturing environment. We investigate scheduling in the context of general cyclic job shops with blocking where there are no intermediate buffers between the machines. We also consider sequence-dependent setups (anticipatory and nonanticipatory), which commonly appear in different manufacturing environments. The choice of blocking condition, that is whether the sequence-dependent setups are anticipatory or not, significantly impacts the optimal schedules. We provide a novel mixed-integer programming (MIP) model for the above problem, namely blocking cyclic job-shop scheduling. Furthermore, we study the impact of sequence-dependent setups in this research. The problem is analysed in detail with respect to anticipatory and nonanticipatory setups and the efficiency of the proposed model is investigated via a computational study that is conducted on a set of randomly generated problem instances. The proposed MIP models are capable of solving small-to-medium-sized problems. Moreover, the analysis presented demonstrates that anticipatory setups directly affect blocking conditions, since intermediate buffers between the machines are not present. Hence, in systems with anticipatory setups, cycle times increase to a greater extent compared to systems with nonanticipatory setups.

Regularity-Based Virtualization Under the ARINC 653 Standard for Embedded Systems

In embedded real-time virtualized systems (ERTVS), the ARINC 653 standard specifies a cyclic scheduling policy to guarantee the real-time performance of tasks in multiple Virtual Machines (VMs) residing on shared hardware. Based on this policy, the Regularity-based Resource Partitioning (RRP) model defines an efficient interface specification to hierarchically partition and assign resource slices among VMs. Although this model has received plenty of attention recently, three major pieces remain missing for applying this model in ERTVS. (1) Embedded systems are more sensitive to resource utilization efficiency since this may drastically affect their deployment cost for including additional cores. Therefore, this paper proposes an optimal and an approximate RRP resource scheduler for multi-core platforms. (2) A resource reconfiguration is required when an embedded system has to switch between operating modes, resulting in the current cyclic schedule being replaced by another pre-configured and verified cyclic schedule. This paper formalizes a new One-Hop Reconfiguration (OHR) problem tailored for mode-switch-capable embedded systems and introduces a corresponding optimal solution. (3) No RRP-based toolset is currently available for embedded systems. This paper thus presents an optimized RRP toolset tailored for embedded systems. Numerous experiments are conducted to evaluate the efficacy of this toolset.

5G Smart Sensor Network-Based e-Commerce Investment Risk Management System for SMEs

The rapid development of Internet technology in the new era has led to the prosperous development of the e-commerce industry, and more and more traditional retail enterprises are transforming into e-commerce enterprises in line with the development of the times to survive. Based on the relevant financial risk management theories, this paper designs an e-commerce investment risk management system for small and medium-sized enterprises based on the 5G intelligent sensor network, identify the financial risks arising from the e-commerce model, and takes appropriate countermeasures to manage the identified risks. For the optimal coverage problem of homogeneous terminals in the subnetwork of a smart sensing network, this paper introduces the concept of area coverage contribution, transforms the problem of selecting the optimal coverage subset in a large area into the problem of selecting the substrate particles, and organically combines the particle swarm algorithm in the bionic algorithm to consider the network survival period, area sensing coverage, and iteration cost for the hierarchical distributed large-scale IoT architecture. A minimum coverage subset dormancy scheduling algorithm based on homogeneous terminals is proposed. The simulation results are compared with the greedy algorithm and the multioptimized target task assignment algorithm, and it is demonstrated that this algorithm can consider multiple optimization objectives such as energy balance, scheduling cycle, timeout rate, network survival cycle, and system reliability and perform well in the case of large-scale deployment. Finally, an appropriate financial risk evaluation model is constructed to objectively evaluate the identified financial risks, and targeted financial risk control measures and suggestions are proposed based on four aspects: financing, investment, operation, and others.

High Throughput Screening for Drug Discovery and Virus Detection.

High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) at the turn of 2019-2020 presented extraordinary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. This paper develops innovative mixed-integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. The method developed in this article can reduce screening time by as much as 91.67%. The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

Data-driven robust optimization for cyclic scheduling of ethylene cracking furnace system under uncertainty based on kernel learning

For an ethylene cracking furnace system (ECFS), the cyclic scheduling is significant for increasing the benefit. However, the scheduling period is always more than three hundred days, the frequently changed prices of raw materials and products may lead to suboptimal results. This paper proposes a data-driven robust optimization (DDRO) approach to handle price uncertainty. The uncertainty set is constructed from historical data by the multiple kernel learning-based one-class support vector machine. With introducing the uncertainty set in the deterministic model, a tractable mixed integer nonlinear programming model is derived by the dual approach for robust optimization. An actual case study shows that the proposed approach is better hedged against uncertainties with less conservatism than the classical methods. The robust cyclic schedule obtained by the DDRO approach increases the net profit by 3.8% than the schedule generated by the deterministic model when the price system deteriorates.

An Efficient Binary Integer Programming Model for Residency Time-Constrained Cluster Tools With Chamber Cleaning Requirements

Cluster tools play a significant role in the entire process of wafer fabrication. As the width of circuits in semiconductor chips shrinks down to less than 10nm, strict operational constraints are imposed on the operations of cluster tools in order to ensure the quality of processed wafers. Particularly, wafer residency time constraints and chamber cleaning requirements are commonly seen in etching, chemical vapor deposition, coating processes, etc. They make the scheduling problem of cluster tools more challenging. This work aims to provide a solution for dual-arm cluster tools with wafer residency time constraints and chamber cleaning requirements. To do so, it proposes a novel virtual wafer-based scheduling method. By this method, under a steady state, a PM processes either a real or virtual wafer at a time. When a PM processes a virtual one, its chamber can perform a cleaning operation. In this way, we can meet not only the strict residency time constraints for real wafers, but also innovatively meet chamber cleaning requirements. Based on such a novel scheduling method, an efficient binary integer programming model is established to optimize the throughput of cluster tools. Finally, experiments are performed to show the efficiency and effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To ensure wafer quality in semiconductor manufacturing, engineers have to impose wafer residency time constraints and chamber cleaning requirements on the operations of cluster tools. In order to tackle their scheduling problem with these constraints, this work proposes a novel method based on the use of virtual wafers. Under a one-cyclic schedule obtained for time-constrained cluster tools without chamber cleaning requirements, virtual wafers are loaded into the tool such that when a PM processes a virtual wafer, a chamber cleaning operation can be performed in practice. The key to solve this scheduling problem is to find a wafer loading sequence with the highest performance in terms of cycle time. To do so, this work establishes an efficient binary integer programming model to search for such a solution. Since the obtained solution is a periodical wafer loading sequence based on a one-wafer cyclic schedule, it can be easily implemented. Therefore, this work has a high practical value to numerous semiconductor manufacturers.

A Workload and Machine Categorization-Based Resource Allocation Framework for Load Balancing and Balanced Resource Utilization in the Cloud

This paper proposes a workload and machine categorization based resource allocation framework for balancing the load across active physical machines as well as utilizing their different resource capacities in a balanced manner. The workload, essentially independent and non-preemptive tasks are allocated resources on the physical machines whose resource availability complements the resource requirement of tasks. Simulation based experiments are performed using CloudSim simulator to execute three different set of tasks comprising 10000, 20000, and 30000 number of tasks. The metric of load imbalance across active physical machines and the metric of utilization imbalance among their considered resource capacities (i.e., CPU and RAM) are measured in different scheduling cycles of a simulation run. Simulation results show that the proposed resource allocation method outperforms the compared methods in terms of balancing the load across active physical machines and utilizing their different resource capacities in a balanced manner.