Simultaneous Batching and Scheduling of Batch Plants That Operate in a Campaign-Mode, Considering Nonidentical Parallel Units and Sequence-Dependent Changeovers (original) (raw)
Related papers
Scheduling of Multistage Multiproduct Batch Plants Operating in a Campaign-Mode
Industrial & Engineering Chemistry Research, 2012
I am very much thankful to my supervisor Professor I. A. Karimi for his enthusiasm, constant encouragement, insight and invaluable suggestions, patience and understanding during my research at the National University of Singapore. His recommendations and ideas have helped me very much in completing this research project successfully. I would like to express my heartfelt thanks to Professor I. A. Karimi for his guidance on writing scientific papers including PhD thesis. I gratefully acknowledge the Research Scholarship from the National University of Singapore. A special thanks to all my lab mates especially, Reddy,
A new methodology for the optimal design and production schedule of multipurpose batch plants
Industrial & Engineering Chemistry Research, 1989
A nonlinear mathematical programming formulation for the multipurpose batch plant design problem is presented. It simultaneously provides both the optimal equipment sizes and the best series of multiproduct campaigns a t one step by taking into account the whole space of feasible production runs. By making minor changes to some problem constraints, the proposed modeling can consider the use of parallel units at certain stages. The units can be assigned to either the same or distinct products. Moreover, the proposed modeling can also handle practical situations where intermediate and salable products are to be manufactured. This algorithmic approach has been successfully applied to discover the best design and production policy in several examples. Optimal solutions to problems involving as many as 7 products and 10 stages have been determined by solving a single, small-size, nonlinear program. T h e method is computationally efficient even if the starting point is far from the optimum. Chemical batch plants are generally grouped into two classes, multiproduct and multipurpose batch facilities. In the former ones, a range of products are manufactured by running a sequence of single-product campaigns. Each of the N desired products undergoes a series of M processing tasks. These are accomplished in a set of M equipment modules or batch stages, each one carrying out a distinct physical/chemical task. A batch of product is transferred to the next stage after completion of the longest task. There is no intermediate storage between stages, and the plant is operated on the zero-wait mode. Important contributions to the mathematical problem description and the understanding of the optimal problem patterns have already been made by several authors (
Optimal production strategy and design of multiproduct batch plants
Industrial & Engineering Chemistry Research, 1990
Methods of sizing batch equipment for multiproduct batch plants usually assume production of a single product at a time. In some cases, however, lower unit sizes are achieved instead by running multiproduct campaigns. This paper proposes a nonlinear mathematical program to search for both the best production strategy and the minimum equipment sizes simultaneously. Since the number of multiproduct campaigns becomes extremely high for large problems, a preliminary step eliminating nonefficient campaigns is performed. Besides that, one usually can still find the problem optimal solution. On the basis of the equal-batch-size assumption, simple analytical expressions are derived to find an initial feasible solution. Several examples have been successfully solved in a short number of iterations. Analysis of the results shows that the use of multiproduct campaigns often brings about a significant savings in capital cost.
An MILP model for planning of batch plants operating in a campaign-mode
Annals of Operations Research, 2016
A mixed integer linear programming (MILP) for the detailed production planning of multiproduct batch plants is presented in this work. New timing decisions are incorporated to the model taking into account that an operation mode based in campaigns is adopted. This operation mode assures a more efficient production management adjusted to the specific context conditions of the considered time horizon. In addition, special considerations as sequence-dependent changeover times and different unit sizes for parallel units in each stage are taken into account. The problem consists of determining the amount of each product to be produced, stored and sold over the given time horizon, the composition of the production campaign (number of batches and their sizes), the assignment, sequencing and timing of batches, and the number of repetitions of the campaign, for a given plant with known product recipes. The objective is to maximize the net profit fulfilling the minimum and maximum product demands. The proposed model provides a useful tool for solving the optimal campaign planning of installed facilities in reasonable computation time, taking different decisions about the operations management.
New Alternatives in the Design and Planning of Multiproduct Batch Plants in a Multiperiod Scenario
Industrial & Engineering Chemistry Research, 2007
New alternatives for the simultaneous design and planning of multiproduct batch plants in a multiperiod scenario are presented in this article. This formulation allows the flexible configuration of the plant in every time period for each product considering the assignment of parallel units of different sizes operating either in-phase or out-of-phase. Capacity expansion during the time horizon is also allowed in order to satisfy new variable requirements. For each batch stage, following the usual procurement policy, units are selected from a set of standard and discrete sizes that are available to perform each operation. The model is formulated through a mixed-integer linear programming (MILP) formulation that maximizes the net present value of profit. From the planning point of view, product sales, raw materials purchases, inventories, waste disposal, and late deliveries are taken into account. Thus, this model simultaneously solves both design and production planning for given forecasts of product demands and pricing in each time period.
MILP model for scheduling and design of a special class of multipurpose batch plants
Computers & Chemical Engineering, 1996
In this paper we propose a method for the integrated scheduling and design for a special class of multipurpose batch processes. The type of plants considered are the ones where not all the products use the same processing stages, and manufacturing of the products can be characterized through production routes. A novel representation for cyclic schedules is proposed that has the effect of aggregating the number of batches for each product. It is shown that the no-wait characteristics of subtrains can be exploited with a reduction scheme that has the effect of greatly decreasing the dimensionality of the problem. This reduction scheme can be complemented with a tight formulation of the underlying disjunctions in the MILP to reduce the computational expense. The proposed MILP model for scheduling is extended to design problems in which the potential existence of intermediate storage in the production paths is also considered. In addition to the rigorous scheduling of the process, the sizes of the equipment constituting the various production stages are determined. By using exact linearization schemes it is shown that the problem can be reformulated as an MILP model and solved rigorously to global optimality. Application of the proposed model is illustrated with several example problems. Literature review Because of its relative simplicity, the preliminary design of multiproduct plants has been in focus by many researchers. Rippin (1993) reviews most of the work in this area in recent years. In this review the need for a comprehensive algorithm that will automatically consider and select from all structural possibilities considered simultaneously is recognized. Voudouris and Grossmann (1993) developed a comprehensive MILP model for multiproduct batch plants that considers all the structural possibilities, and even further, considers final product inventories within a periodic scheduling approach. For multipurpose plants the mixed integer approaches for design and scheduling can be categorized in thrce broad arcas. The main difference between these approaches is the way with which the scheduling subproblem is dealt with. The first approach is based on a simplified campaign planning scheme as for instance with the work by Vaselenak et al (1987). In this approach a central issue is the production campaign formation. Namely, during a production campaign which consists of batches of the same product, two products are allowed to be produced in the same campaign only if their production paths do not share any processing equipment. Faqir and Karimi (1990) generalized this approach by allowing more than one path for the production of a particular product The model they developed was a nonconvex MINLP which was later reformulated as an MILP by Voudouris and Grossmann (1992). Papageorgaki and Reklaitis (1990) also developed a nonconvex MINLP model which incorporated many additional aspects like flexible task-to-equipment allocation, but still was based on a campaign planning mode. A variant of this campaign approach is proposed by Shah and Pantelides (1992) where the assumption of simultaneously utilizing production paths with noncommon equipment for the formation of production campaigns is applied to production stages instead of production paths. The main problem with these approaches is that the scheduling problem is solved based on a simplifying assumption, thus allowing underutilization of time, the generation of relatively large idle times for the processing equipment, and significant overdesign of the plant when the design subproblem is integrated. The second approach tries to tackle the problem of time underutilization. For this reason it is recognized that a rigorous scheduling of production paths has first to be performed and to serve as a lower level subproblem to the capacity allocation problem. The work by Wellons and Reklaitis (1991) is representative of this approach. Unfortunately the resulting models are highly intractable mainly because of the nonlinearities that arc involved. Furthermore, an arbitrary selection of the total number of batches that are considered may lead to suboptimai solutions.
Simultaneous production planning and scheduling in multiproduct batch plants
Industrial & Engineering Chemistry Research, 1990
Production planning and scheduling are intimately linked activities. T h e production goals set a t the planning level depend on marketing considerations but must account for the ability to implement them a t the scheduling level. Hence, ideally, planning and scheduling should be analyzed simultaneously. However, this is in general a very difficult task given the large combinatorial nature of just the scheduling problem in itself. In this work, based on a previously developed LP flowshop scheduling model by Birewar and Grossmann that can effectively aggregate the number of batches belonging to each product, a multiperiod LP model is proposed for the simultaneous production planning and scheduling of multiproduct batch plants that may consist of one or several nonidentical parallel lines. Inventory costs, sequence-dependent clean-up times and costs, and penalties for production shortfalls are readily accounted for in this model. The actual schedule to achieve the production goals predicted by the planning problem is derived by applying a graph enumeration method to the results from the simultaneous planning and scheduling model or by any other scheduling method. Several examples are presented to illustrate the proposed method.
2010
In this work, a novel sequence-based mixed-integer linear programming formulation for the simultaneous batching and scheduling in multi-product multi-stage batch plants is developed. The selection of batches, the allocation of batches to processing units and the sequencing of batches in each unit constitute the discrete decisions of our model. Batch processing times and sizes are variables. Batch size increment steps are included in an attempt to accommodate our model to real-life industrial practice.
Scheduling of multipurpose batch chemical plants with resource constraints
Industrial & Engineering Chemistry Research, 1993
An efficient procedure to identify the dominant multiple-product campaigns for multipurpose batch chemical plants is presented. On the basis of a linear dominance property, the dominant campaigns are identified as the noninferior extreme points of the associated multiobjective campaign formation problem. The noninferior set estimation method has been incorporated into a decomposition strategy which alternately solves an equipment group master problem to identify dominant equipment group profdes for the production lines of the campaign and a campaign formation subproblem with identifies the dominant campaigns for a particular equipment group profile. A multiperiod mixed integer linear programming production planning model for multipurpose batch plants that allots the available production time to a subset of the dominant campaigns and accounts for lost production time due to changeovers and startup times is also presented. The campaign formation and production planning procedures are illustrated with an example problem.
Simultaneous batching and scheduling of single stage batch plants with parallel units
AIChE Journal, 2008
This paper presents a new mixed integer linear program (MILP) for the optimal short-term scheduling of single stage batch plants with sequence dependent changeovers and optimal selection of the number of batches to produce. It is a continuous-time formulation employing multiple time grids that is based on the resource-task network (RTN) process representation. The main novelty is that aggregated processing and changeover tasks are considered that account for the time required to produce all batches of the product, plus the changeover time to the next product in the sequence. When compared to the traditional approach of considering a single processing task per batch, fewer event points are needed, which results in significantly lower computational effort as illustrated through the solution of several example problems. The new formulation is further compared to a continuous-time model with global precedence sequencing variables to a bounding model with immediate precedence sequencing variables and to a constraint programming model.