Scheduling Of Batch Processes Research Articles

Stochastic Scheduling for Batch Processes With Downstream Queue Time Constraints

Stochastic Scheduling for Batch Processes With Downstream Queue Time Constraints

DoSRA: A Decentralized Approach to Online Edge Task Scheduling and Resource Allocation

With the proliferation of novel Internet of Things (IoT) mobile applications and advanced communication technologies, nowadays we are surrounded by ubiquitous sensors and smart devices. These smart IoT devices generate a large volume of data day and night at the edge of the network, create a huge demand for edge computing resources, and thus, promote the emergence of the multiaccess edge computing (MEC) paradigm. In MEC environments, IoT devices or mobile users are allowed to offload their computational tasks to nearby edge servers to overcome the limitation of local computing resources. Though edge servers could provide low-latency service with high-responsible computing capabilities, they are still facing many challenges posed by the limited hardware resources and diverse offloading requests. However, traditional approaches are usually based on the centralized architecture and batch-processing scheduling mode, which might lead to low efficiency and high communication overhead. Besides, they also lack the consideration of task diversity and priorities, which are crucial in real-world application scenarios. Thus, smart task scheduling and resource provision strategies with a high real-time property are urgently needed for better user experience and higher resource utilization. In this article, we target the online edge IoT task scheduling and resource allocation problem and propose a decentralized approach (DoSRA). The experiments based on real-world edge environments have demonstrated that the proposed approach could achieve at most a 35.34% reduction on the average weighted offloading response time.

An Adaptive Large Neighborhood Search for Single-Machine Batch Processing Scheduling With 2-D Rectangular Bin-Packing Constraints

Spatial resource allocation is common in batch scheduling problems. It is usually modeled as simple capacity constraints, which hurts the accuracy of a solution if a geometric layout is required in multidimensional space. In this article, a multiobjective mixed-integer linear programming model including total weighted waiting time and resource utilization is proposed for single batch processing machine scheduling, in which the two-dimensional (2-D) rectangular packing constraints are introduced to handle a 2-D layout more precisely. An adaptive large neighborhood search algorithm with three kinds of destroy operators and repair operators is developed to approximately solve the problem. Experiments based on lockage scheduling show that our algorithm found exact optimal solutions in most cases of no more than 30 items and outperformed typical genetic algorithm, simulated annealing, and two kinds of variable neighborhood descent algorithms in all cases of no more than 50 items.

Cyclic scheduling and heat integration of batch process: Design of heat storage vessels

Scheduling over longer time horizons has limitations such as large problem size and computational complexity. Therefore, there is universal concern to establish novel modeling approaches to efficiently manage various complex characteristics. In this work, a rigorous Unit Specific Event Based model (USEB) is proposed for long-term scheduling of batch plants. The proposed approach addresses the complete scheduling of long-term operational horizon by considering start-up, cycle and finishing periods. The start-up period takes care of intermediate material states requirement at the beginning of first cycle and finishing period effectively utilizes the leftover intermediate states in final cycle. Using active task concept, the cyclic scheduling, direct and indirect heat integration possibilities are accurately modelled by considering design of heat storage vessels. This comprehensive computational approach highlights the importance of judicious use of direct and indirect heat integration in process industries and cyclic scheduling for complex and long term scheduling problems.

Feasibility Analysis in Batch Process: A Machine Learning Approach

Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasible design space can be described by the decision-maker as per the process constraints and objectives. A machine learning approach using support vector machines is implemented for the classification of the feasible region from a data set. An algebraic equation is obtained using support vectors (data points participating in creating classification boundary), which could be incorporated into the mathematical model. The equation will lead to attaining several objectives in the finite obtained region. In this paper, kernel-based support vector classification is tailored to a continuous-time formulation for the short-term scheduling of batch processes. First, using a classification SVC over a data set, it was observed that the mathematical model is infeasible for a set of production combination of two different products. Using the proposed methodology, a design space is presented graphically for feasible production targets. In the second case, the study is applied to perform optimization operations to target feasible region within a specified limit as a choice of decision-maker.

Auction-based approach to the job-shop problem with parallel batch processing and a machine availability constraint

In military manufacturing enterprises, exceptionally high product performance standards are generally met by using heat treatment followed by machining. These procedures are typically parallel batch-processing (BP) scheduling problems and job-shop problems, respectively. A mixed-integer nonlinear programming model is created to describe the problem with a machine availability constraint, which is divided into two stages: BP and machine processing (MP). Furthermore, an auction-based approach is developed in which jobs are categorized into batches during the BP stage and resources are allocated to operating machines during the MP stage. A local search operator is then applied to optimize the obtained feasible solutions. Benchmark instances are enlarged to adapt to the proposed problem. The approach is tested and compared to existing algorithms, and statistical analysis is performed using SPSS Statistics. The results show that the auction-based approach is effective and stable, and has absolute advantages in solving large-scale instances.

A novel stochastic programming approach for scheduling of batch processes with decision dependent time of uncertainty realization

A novel stochastic programming approach for scheduling of batch processes with decision dependent time of uncertainty realization

Control-aware batch process scheduling

Batch processes are inevitably affected by disturbances. The process control system can often reject them and ensure that the final product is “in spec.” Nevertheless, processing times and the quality of the final product and any intermediates can fluctuate as a result. In turn, this impacts the computation and execution of production schedules. We propose a new scheduling framework that considers the effect of disturbances and the impact of process control on short-term production scheduling of batch processes. Processing time expressions are derived using historical closed-loop process operating data. Subsequently, a two-stage stochastic scheduling problem is formulated, whereby exogenous and endogenous uncertainties are considered at the first stage, with the exception of product demand, which is dealt with in the second stage. Two case studies are presented in detail.

Minimizing Freshwater Usage in Batch Process Scheduling: S-Graph Approach

Water is one of the most important natural resources of life. While it is considerably cheap and vastly available currently, except for extreme locations, this is not guaranteed in the future. Being provident with water has multitude of advantages in both short and long term. Using less clean water not only brings immediate financial benefits, it simultaneously reduces wastewater production, related treatment costs, and the impact on the environment. Reducing the water footprint of a batch system is not a trivial task, as water sources and sinks need to be matched not only in quantity and quality, but in time as well. In this paper, the S-graph scheduling framework is extended to address simultaneous scheduling and water minimization in batch processing systems. The proposed approach tackles truly batch processes with a single contaminant, and allows only a single water source to be reused for each sink. The presented algorithm and S-graph extension have been implemented and tested on various case studies from the literature. The results of this paper provide an opportunity for further extensions to address a wider range of problems with multiple contaminants, semi-continuous behavior, cyclic operations, etc.

Prospects for Novel Pinch Analysis Application Domains in the 21st Century

Pinch Analysis was originally developed as a thermodynamically-based methodology for targeting and design of industrial heat recovery systems. Subsequent extensions of pinch analysis have since been developed; for example, mass pinch analysis is derived directly from analogies between heat and mass transfer, and has led to application for the efficient use of mass separating agents (MSAs) in process plants. More recent literature shows Pinch extensions for numerous applications, including energy sector planning using diverse quality measures. Other Pinch techniques have also been developed using time as the quality index, for applications such as production planning, financial analysis, supply chain management, isolated energy system design, batch process scheduling, carbon dioxide sequestration, and human resource allocation. These applications demonstrate how common problem structures allow an elegant solution approach to be developed for seemingly diverse applications. In this paper, past applications of Pinch Analysis are reviewed, and its further prospective extensions that may be developed, based on similar analogies, for non-conventional problem domains are discussed.

Integrated process design, scheduling, and model predictive control of batch processes with closed‐loop implementation

AbstractSimultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed‐integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed‐loop implementation. We use multi‐parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base‐2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.

Addressing storage time restrictions in the S-graph scheduling framework

Storage planning and utilization are among the most important considerations of practical batch process scheduling. Modeling the available storage options appropriately can be crucial in order to find practically applicable solutions with the best objective value. In general, there are two main limitations on storage: capacity and time. This paper focuses on the latter and investigates different techniques to tackle limited storage time within the S-graph framework. The S-graph framework is a collection of combinatorial algorithms and a directed graph based model that has been introduced three decades ago and has been under development ever since. In this work, several options for addressing storage time limitations within the framework were implemented and tested for efficiency. The empirical results over a huge number of tests have unequivocally favored one of the approaches, which will be applied in later developments.

Reinforcement learning of adaptive online rescheduling timing and computing time allocation

Mathematical optimization methods have been developed to a vast variety of complex problems in the field of process systems engineering (e.g., the scheduling of chemical batch processes). However, the use of these methods in online scheduling is hindered by the stochastic nature of the processes and prohibitively long solution times when optimized over long time horizons. The following questions are raised: When to trigger a rescheduling, how much computing resources to allocate, what optimization strategy to use, and how far ahead to schedule? We propose an approach where a reinforcement learning agent is trained to make the first two decisions (i.e., rescheduling timing and computing time allocation). Using neuroevolution of augmenting topologies (NEAT) as the reinforcement learning algorithm, the approach yields, on average, better closed-loop solutions than conventional rescheduling methods on three out of four studied routing problems. We also reflect on expanding the agent’s decision-making to all four decisions.

Optimal production and maintenance scheduling for a multiproduct batch plant considering degradation

Performance decay due to asset degradation is an important constraint in industrial production and therefore needs to be actively considered. This paper focuses on short-term scheduling for multiproduct batch processes with sequence-dependent degradation and is motivated by a case study in which the sequence of multiple-grade batch runs impacts evolution of fouling. A continuous-time scheduling formulation is proposed to incorporate realistic features of the case study processes. The precedence scheduling concept for the sequential process is employed to model sequences of multiproduct orders and maintenance and is implemented using the general disjunctive programming method. The scheduling formulation is applied to the case study and further analyzed through comprehensive computational tests, which illustrates the efficacy of the proposed formulation.

A Novel Back-off Algorithm for Integration of Scheduling and Control of Batch Processes under Uncertainty

A novel decomposition algorithm for the integration of scheduling and control of multiproduct, multiunit batch processes under stochastic parameter uncertainty is presented. This iterative algorithm solves a scheduling and dynamic optimization problem around a nominal point while approximating uncertainty through back-off terms, embedded in the operational process constraints. Monte Carlo simulations are performed to propagate uncertainty and to evaluate dynamic feasibility; statistical information is drawn from these simulations to update the back-off terms. Convergence of the algorithm results in a set of scheduling and control decisions that aim to keep the plant dynamically feasible under the effect of uncertainty up to a user-defined tolerance criterion. The proposed algorithm is shown to be successfully applied to a multiproduct, multiunit batch plant under the effects of different probability density functions in the uncertain parameters. The algorithm’s performance is gauged against a fully integr...

A new approach for scheduling of multipurpose batch processes with unlimited intermediate storage policy

The increasing demand of goods, the high competitiveness in the global marketplace as well as the need to minimize the ecological footprint lead multipurpose batch process industries to seek ways to maximize their productivity with a simultaneous reduction of raw materials and utility consumption and efficient use of processing units. Optimal scheduling of their processes can lead facilities towards this direction. Although a great number of mathematical models have been developed for such scheduling, they may still lead to large model sizes and computational time. In this work, we develop two novel mathematical models using the unit-specific event-based modelling approach in which consumption and production tasks related to the same states are allowed to take place at the same event points. The computational results demonstrate that both proposed mathematical models reduce the number of event points required. The proposed unit-specific event-based model is the most efficient since it both requires a smaller number of event points and significantly less computational time in most cases especially for those examples which are computationally expensive from existing models.

A novel unit-specific event-based formulation for short-term scheduling of multitasking processes in scientific service facilities

Scientific service facilities examine a number of samples from different customers for several physical and chemical properties using processing units with large capacities. A processing unit can process a great number of samples simultaneously. The process in such scientific service facility can be treated as a multi-tasking multipurpose batch process. Despite the great interest in developing models for scheduling of process industry during the past three decades, scheduling of multi-tasking multipurpose batch processes in a scientific service facility has not been considered adequately. In this work, we develop three novel mathematical models using the well-established unit-specific event-based modelling approach. The computational results demonstrate that the proposed mathematical models are able to reduce the number of event points required, which leads to a significant reduction in the model size and computational time. One of the proposed models in which the timing variables are defined based on processing units is the most efficient in most cases especially when minimization of makespan is used as the objective, where at least one order of magnitude less computational time than all other models is required to generate the optimum solution compared to other existing models.

Continuous‐time scheduling formulation for multipurpose batch plants

AbstractShort‐term scheduling of batch processes is a complex combinatorial problem with remarkable impact on the total revenue of chemical plants. It consists of the optimal allocation of limited resources to tasks over time in order to manufacture final products following given batch recipes. This article addresses the short‐term scheduling of multipurpose batch plants, using a mixed integer linear programming formulation based on the state‐task network representation. It employs both single‐grid and multi‐grid continuous‐time representations, derived from generalized disjunctive programming. In comparison to other multigrid scheduling models in the literature, the proposed multi‐grid model uses no big‐M constraints and leads to more compact mathematical models with strong linear relaxations, which often results in shorter computational times. The single‐grid counterpart of the formulation is not as favorable, as it leads to weaker linear relaxations than the multi‐grid approach and is not capable of handling changeover time constraints.

Optimal Coordination Of Batch Processes with Shared Resources

Abstract This paper deals with the scheduling of batch processes that share common resources having consumption profiles that change over time and are subjected to some global constraints. In order to approach the problem, the formulation mixes three different time basis. Scheduling is performed in continuous time, but the description of the shared resources and the global constraints uses discrete values. The method is applied to a challenging problem in a tuna canning factory that mixes continuous flows of cans with the batch operation of a set of sterilizers sharing the steam supply line.

Single Reference Grid Continuous-Time Formulation for Batch Scheduling

Abstract Short term scheduling of multipurpose batch processes has received growing attention over past decades. It concerns the optimal allocation of a set of limited resources to tasks over time in order to enhance the revenue of plants. This paper addresses the short term scheduling of batch processes through a continuous-time mixed integer linear programming (MILP) formulation based on the state-task network (STN) representation that allows to consider multiple intermediate due dates for market requirements. The proposed formulation can be classified as a slot-based approach that views the time horizon as a set of ordered blocks of unknown and variable lengths. Compared to previous similar approaches, it is simpler and leads to a smaller mathematical model without decoupling tasks from units. A few benchmark problems are used to illustrate the computational advantages of the proposed optimization approach.