Production-ratio oriented optimization for multi-recipe material handling via simultaneous hoist scheduling and production line arrangement

In multi-recipe and multi-stage material handling (M3H) processes, such as electroplating and polymeric coating, the productivity maximization under a customized production ratio of different types of jobs is an ultimate goal. Cyclic hoist scheduling (CHS) is certainly the most concerned aspect to improve the productivity. However, the production-ratio oriented productivity not only depends on the hoist scheduling, but also substantially relies on the production line arrangement (PLA), i.e., the spatial allocation of various processing units. This is because PLA determines the traveling time for the hoist to perform loaded and free moves among different processing units, which in turn inevitably affects the cyclic scheduling time and thus the total productivity. Therefore, CHS and PLA should be simultaneously optimized for the production-ratio oriented productivity maximization for M3H processes.In this paper, an integrated modeling methodology for productivity maximization of M3H processes has been developed with simultaneous consideration of CHS, PLA, and the customized production ratio. It introduces an MILP model that can successfully address all the major concerned issues for M3H processes, such as multiple recipes, multiple jobs, multi-capacity processing units, diverse processing time requirements, production line arrangement, and the customized production ratio in each cycle production. The efficacy of the proposed methodology is demonstrated by various case studies with in-depth analysis.     

Moving horizon approach of integrating scheduling and control for sequential batch processes

Online integration of scheduling and control is crucial to cope with process uncertainties. We propose a new online integrated method for sequential batch processes, where the integrated problem is solved to determine controller references rather than process inputs. Under a two‐level feedback loop structure, the integrated problem is solved in a frequency lower than that of the control loops. To achieve the goal of computational efficiency and rescheduling stability, a moving horizon approach is developed. A reduced integrated problem in a resolving horizon is formulated, which can be solved efficiently online. Solving the reduced problem only changes a small part of the initial solution, guaranteeing rescheduling stability. The integrated method is demonstrated in a simulated case study. Under uncertainties of the control system disruption and the processing unit breakdown, the integrated method prevents a large loss in the production profit compared with the simple shifted rescheduling solution. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1654–1671, 2014     

A reactive optimization strategy for the simultaneous planning,scheduling and control of short-period continuous reactors

The efficient and economic operation of processing systems ideally requires a simultaneous planning, scheduling and control framework. Even when the optimal simultaneous solution of this problem can result in large scale optimization problems, such a solution can represent economic advantages making feasible its computation using optimization decomposition and/or few operating scenarios. After reducing the complexity of the optimal simultaneous deterministic solution, it becomes feasible to take into account the effect of model and process uncertainties on the quality of the solution. In this work we consider those changes in product demands that take place once the process is already under continuous operation. Therefore, a reactive strategy is proposed to meet the new product demands. Based on an optimization formulation for handling the simultaneous planning, scheduling, and control problem of continuous reactors, we propose a heuristic strategy for dealing with unexpected events that may appear during operation of a plant. Such a strategy consists of the rescheduling of the products that remain to be manufactured after the given disturbance hits the process. Such reactive strategy for dealing with planning, scheduling and control problems under unforeseen events is tested using two continuous chemical reaction systems.     

A cost‐effective model for the gasoline blend optimization problem

Gasoline blending is a critical process with a significant impact on the total revenues of oil refineries. It consists of mixing several feedstocks coming from various upstream processes and small amounts of additives to make different blends with some specified quality properties. The major goal is to minimize operating costs by optimizing blend recipes, while meeting product demands on time and quality specifications. This work introduces a novel continuous‐time mixed‐integer linear programming (MILP) formulation based on floating time slots to simultaneously optimize blend recipes and the scheduling of blending and distribution operations. The model can handle non‐identical blenders, multipurpose product tanks, sequence‐dependent changeover costs, limited amounts of gasoline components, and multi‐period scenarios. Because it features an integrality gap close to zero, the proposed MILP approach is able to find optimal solutions at much lower computational cost than previous contributions when applied to large gasoline blend problems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3002–3019, 2016     

A framework for schedule evaluation with processing uncertainty

The scheduling of batch chemical processes has been the focus of a substantial amount of research, focused primarily on building schedules for processes which are assumed to operate in a deterministic manner. This work describes an approach to directly incorporate schedules into a simulator for the purposes of schedule validation and testing of rescheduling methodologies when stochastic events occur. Although other types of uncertainty could be considered within the framework, the deviations presented in this work are those which can be represented as processing time variations and equipment breakdowns. The framework, when used open-loop, is an effective tool for evaluating the expected performance and robustness of various scheduling strategies. Two scheduling tools are compared, one which uses the uniform time discretized model and the other uses a non uniform discretization of time. In addition, user written logic has been incorporated into the simulator which allows the performance of various rescheduling techniques to be compared and evaluated prior to implementation in a processing facility. Numerical results of Monte Carlo simulation studies for both uses of the framework are presented.     

On deterministic online scheduling: Major considerations,paradoxes and remedies

Despite research in the area, the relationship between the (open-loop) optimization problem and the quality of the (closed-loop) implemented schedule is poorly understood. Accordingly, we first show that open-loop and closed-loop scheduling are two different problems, even in the deterministic case. Thereafter, we investigate attributes of the open-loop problem and the rescheduling algorithm that affect closed-loop schedule quality. We find that it is important to reschedule periodically even when there are no “trigger” events. We show that solving the open-loop problem suboptimally does not lead to poor closed-loop solutions; instead, suboptimal solutions are corrected through feedback. We also observe that there exist thresholds for rescheduling frequency and moving horizon length, operating outside of which leads to substantial performance deterioration. Fourth, we show that the design attributes work in conjunction, hence, studying them simultaneously is important. Finally, we explore objective function modifications and constraint addition as methods to improve performance.     

Greedy algorithm for scheduling batch plants with sequence‐dependent changeovers

This article presents a new algorithm for scheduling multistage batch plants with a large number of orders and sequence‐dependent changeovers. Such problems are either intractable when solved with full‐space approaches or poor solutions result. We use decomposition on the entire set of orders and derive the complete schedule in several iterations, by inserting a couple of orders at a time. The key idea is to allow for partial rescheduling without altering the main decisions in terms of unit assignments and sequencing (linked to the binary variables) so that the combinatorial complexity is kept at a manageable level. The algorithm has been implemented for three alternative continuous‐time mixed integer linear programing models and tested through the solution of 10 example problems for different decomposition settings. The results show that an industrial‐size scheduling problem with 50 orders, 17 units distributed over six stages can effectively be solved in roughly 6 min of computational time. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Scheduling of displacement batch digesters using discrete time formulation

This paper provides mathematical programming based optimization model and computational results for short-term scheduling of displacement batch digesters in a pulp industry. The scheduling problem involves development of an optimal solution that yields the best sequence of operations in each of the parallel batch digesters sharing common resources. The constraints are imposed on meeting the demand of pulp of different qualities within a specified time horizon. The problem comprises of both fixed-time and variable time durations of the tasks, different storage policies, zero-wait and finite wait times, and handling of shared resources. The scheduling problem is formulated using a state-task-network (STN) representation of production recipes, based on discrete time representation resulting in a mixed-integer linear programming (MILP) problem which is solved using GAMS software. The basic framework is adapted from the discrete-time model of Kondili et al. (Comput. Chem. Eng., 1993, 17, 211–227). Different case studies involving parallel digesters in multiple production lines are considered to demonstrate the effectiveness of the proposed formulation using two different objective functions.     

An MILP-based reordering algorithm for complex industrial scheduling and rescheduling

The challenging structure as well as the extensive economical potential of solving various scheduling problems has fascinated numerous researchers during the recent decades. Despite the significant progress made in the fields of operations research and process systems engineering (Puigjaner, Comp. Chem. Eng., 23 (1999): S929–S943; Shah, Proceedings of FOCAPO'98, Snowbird, Utah, USA, 1998) the complexity of many industrial-size scheduling problems means that a global optimal solution cannot be reached within a reasonable computational time. In these cases, the production schedule must be generated using e.g. some kind of sophisticated heuristics, which can often lead to suboptimal solutions. In this paper, we introduce a Mixed Integer Linear Programming (MILP) based algorithm, which can be efficiently used to improve an existing feasible, but non-optimal, production schedule or to reschedule jobs in the case of changed operational parameters. The algorithm has been successfully applied to certain scheduling problems in both the paper-converting and pharmaceutical industry.     

A state-space model for chemical production scheduling

We express a general mixed-integer programming (MIP) scheduling model in state-space form, and show how common scheduling disruptions, which lead to rescheduling, can be modeled as disturbances in the state-space model. We also discuss how a wide range of scheduling models, with different types of decisions and processing constraints, can be expressed in state-space form. The proposed framework offers a natural representation of dynamic systems, thereby enabling researchers in the chemical process control area to study scheduling problems. It also facilitates the application of known results for hybrid systems, as well as the development of new tools necessary to address scheduling applications. We hope that it will lead to the development of scheduling solution methods with desired closed-loop properties, a topic that has received no attention in the process operations literature.     

Inventory pinch algorithm for gasoline blend planning

Current gasoline blend scheduling practice is to optimize blend plans via fixed duration (e.g., days) multiperiod NLP or MINLP models and schedule blends via interactive simulation. Solutions of multiperiod models typically have different blend recipes for each time period. We introduce inventory pinch points and use them to construct an algorithm based on single‐period nonlinear model to minimize the number of different blend recipes. The algorithm optimizes multigrade blend recipes for each period delimited by the inventory pinch points and then uses a fine‐grid multiperiod fixed‐recipe MILP to compute blend volumes profile. If MILP is infeasible, a corresponding period between the pinch points is subdivided and recipes are reoptimized. In our case studies, solutions are computed in significant less time and are most often within 0.01% of the solutions by multiperiod MINLP. Reduced number of blend recipes makes it easier for the blend scheduler to create a schedule by interactive simulation. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3748–3766, 2013     

Continuous-time scheduling formulation for multipurpose batch plants

Short-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.     

Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed‐integer dynamic optimization problem or a large‐scale mixed‐integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed‐integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2379–2406, 2013     

Inventory pinch based,multiscale models for integrated planning and scheduling‐part II: Gasoline blend scheduling

Integration of planning and scheduling optimizes simultaneous decisions at both levels, thereby leading to more efficient operation. A three‐level discrete‐time algorithm which uses nonlinear models and integrates planning and detailed scheduling is introduced: first level optimizes nonlinear blend models via multiperiod nonlinear programming (NLP), where period boundaries are initially determined by the inventory pinch points; second level uses fixed recipes (from the first level) in a multiperiod mixed‐integer linear program to determine first an optimal production plan and then to optimize an approximate schedule which minimizes the total number of switches in blenders and swing tanks; third level computes detailed schedules that adhere to inventory constraints computed in the approximate schedule. If inventory infeasibilities appear at the second or the third level, the first‐level periods are subdivided and blend recipes are reoptimized. Algorithm finds the same or better solutions and is substantially faster than previously published full‐space continuous‐time model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2475–2497, 2014     

Novel genetic algorithm for short-term scheduling of sequence dependent changeovers in multiproduct polymer plants

Polymer plants generally operate to produce different grades of product from the same reactor. Such systems commonly require short-term scheduling to meet market demand. One important requirement in continuous-time scheduling of such systems is to satisfy a variety of constraints, including identifying feasible sequences of the predecessor and successor jobs to effectively handle changeovers. In this study, a new genetic algorithm (GA) is proposed to solve such job sequencing problems. The proposed GA uses real-coded chromosome to represent job orders and their sequences in the schedule. The novelty is that the representation ensures that all constraints are satisfied a priori, except the sequence constraint which is handled by penalizing violations. Three important problems relevant to polymer industry are solved to obtain optimal schedules. The first deals with the sequencing constraint between individual product orders, the second with sequencing constraint between groups of product orders, while the third incorporates batching with scheduling.     

An Adaptive Recipe Implementation in Case‐Based Formalism for Abnormal Condition Management

This paper deals with accurate recipe implementation for abnormal condition management in a batch process using a case‐based reasoning (CBR) approach. A set of new problems can be solved by reusing proven process solutions. The proposed system integrates quantitative and qualitative parameters for adaptation of cases. A novel methodology to generate accurate recipes and to adapt to the processes is introduced during normal and abnormal conditions. In particular, the differences between current conditions and the references (recipes) should be managed to prevent any hazardous conditions arising. The processes are evaluated using their similarity to the past cases. This intelligent approach distinguishes plausible cases, generates accurate recipes, and adapts to new situations. The aim is to use the offline historical process data and safety related information in order to propose changes and adjustments in the processes.     

ENVIRONMENTALLY CONSCIOUS HOIST SCHEDULING FOR ELECTROPLATING FACILITIES

Hoist scheduling in electroplating operations has long been considered a key factor for improving the production rate. It has recently been recognized that hoist scheduling can also play an important role in waste minimization. In this work, a new hoist scheduling method is introduced for simultaneously achieving both the economic and environmental goals. A two-step dynamic optimization algorithm is introduced for identifying an optimal hoist schedule that can minimize the quantity and toxicity of wastewater streams from an electroplating line without loss of production rate. To improve computational efficiency, an engineering approach is adopted to reduce the number of binary decision variables in the optimization problem. An application to an actual electroplating process shows a significant reduction of both chemical and water consumption, which equates to a simultaneous realization of wastewater reduction and increase of profits.     

Dynamic models and fault diagnosis‐based triggers for closed‐loop scheduling

Establishing an explicit feedback connection between production management and process control decisions is a key requirement for more nimble and cost effective process operations in today's variable market conditions. Past research efforts focused on embedding dynamic process information in the production scheduling problem. In this article, we propose a novel framework for closing the scheduling loop, based on considering the process‐level events and disturbances that impact the implementation of scheduling decisions. We emphasize the role of a comprehensive fault detection, isolation and reconstruction mechanism as a trigger for rescheduling decisions and for reflecting the process capabilities altered by these events in the rescheduling problem formulation. Our framework is agnostic to the process type, and we present two (continuous process, sequential batch process) case studies to demonstrate its applicability. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1959–1973, 2017     

考虑切料过程的乙烯裂解炉炉群调度建模与优化

乙烯裂解炉炉群通常由多台裂解炉并行运行,将烃类原料裂解成小分子烃类化合物。由于随着裂解炉连续运行不可避免地在炉管内壁产生结焦,结焦导致裂解炉运行效率下降,所以需要对裂解炉进行周期性的停炉清焦。对于不同价格参数的多种原料不同清焦费用的多台裂解炉来说,整个乙烯裂解炉炉群系统的循环调度应是求得最优解使得收益最大化。本文对此类裂解炉炉群循环调度问题提出了一个新的混合整数非线性（MINLP）模型,相比较以前的研究该模型能够得到更好的求解多原料多裂解炉的问题,同时解决了裂解过程中切料时机选择的问题。最后,以某乙烯厂为研究实例进行切料时机的优化,优化后裂解炉全周期的运行效益显著提高,为操作人员选择最佳切炉时机提供了理论依据,说明了此模型的有效性。     

Moving horizon closed‐loop production scheduling using dynamic process models

The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017