Scheduling Multiproduct Chemical Batch Processes using Matrix Representation

Batch process plants are usually designed for the production of specialty and fine chemicals such as paint, food and pharmaceutical to meet specific product requirements as set by current market demand. Batch process plants can be operated as single product in which only one product is produced and multiple products which allow production of more than one product using same batch facility. The economics of the batch process heavily depends on efficient scheduling of the different tasks involved in manufacturing the range of products. The main objective of scheduling is generally to minimize completion time known as the makespan of the batch process. Product sequencing, which is used to set order of products to be produced, has a direct impact on the makespan particularly in the multiple products case. Another effect on makespan is observed for different transfer policies used to transfer the product intermediates between process stages. The generally adopted intermediate transfer policies are (i) zero wait (ZW), (ii) no intermediate storage (NIS), (iii) unlimited intermediate storage (UIS) and (iv) finite intermediate storage (FIS). In the past, the determination of makespan for each transfer policy has been done using a number of mathematical and heuristics approaches. Although these approaches are very efficient and are currently being applied in many chemical process industries but most of them end up with the solution in terms of complex mathematical models that usually lack user interactions for having insights of the scheduling procedure. This motivated the current work to develop relatively simple and interactive alternate approaches to determine makespan. The proposed approach uses matrix to represent the batch process recipe. The matrix is then solved to determine the makespan of a selected production sequence. Rearrangement of the matrix rows according to the varied production sequences possible for the specified batch process recipes enables the makespan to be determined for each sequence. Designer is then provided with the production sequence options with its corresponding makespan from which a selection could be made according to the process requirements

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Integrated management of hierarchical levels: towards a CAPE tool

The integration of decision-making procedures usually assigned to different hierarchical production systems requires the use of complex mathematical models and high computational efforts, in addition to the need of an extensive management of data and knowledge within the production systems. This work addresses this integration problem and proposes a comprehensive solution approach, as well as guidelines for Computer Aided Process Engineering (CAPE) tools managing the corresponding cyberinfrastructure. This study presents a methodology based on a domain ontology which is used as the connector between the introduced data, the different available formulations developed to solve the decision-making problem, and the necessary information to build the finally required problem instance. The methodology has demonstrated its capability to help exploiting different available decision-making problem formulations in complex cases, leading to new applications and/or extensions of these available formulations in a robust and flexible way.Peer ReviewedPostprint (author's final draft

Amendment of Heuts-Selen's lotsizing and sequencing heuristic for single stage process manufacturing systems

Productivity;Production Scheduling;Manufacturing;production

A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry

Abstract This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry. The production process consists of three production stages from spinning, weaving, and dyeing. All stages have one processor. Setup time between two consecutive jobs with different color is considered. This paper also proposes attribute’s decomposition of a single job to classify available jobs to be processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm is proposed in which the permutation of set of jobs with same attribute and the permutation among set of jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution, then is compared with other known optimal methods

Comparative study of different approaches to solve batch process scheduling and optimisation problems

Effective approaches are important to batch process scheduling problems, especially those with complex constraints. However, most research focus on improving optimisation techniques, and those concentrate on comparing their difference are inadequate. This study develops an optimisation model of batch process scheduling problems with complex constraints and investigates the performance of different optimisation techniques, such as Genetic Algorithm (GA) and Constraint Programming (CP). It finds that CP has a better capacity to handle batch process problems with complex constraints but it costs longer time

Combined make-to-order and make-to-stock in a food production system

The research into multi-product production/inventory control systems has mainly assumed one of the two strategies: Make-to-Order (MTO) or Make-to-Stock (MTS). In practice, however, many companies cater to an increasing variety of products with varying logistical demands (e.g. short due dates, specific products) and production characteristics (e.g. capacity usage, setup) to different market segments and so they are moving to more MTO-production. As a consequence they operate under a hybrid MTO-MTS strategy. Important issues arising out of such situations are, for example, which products should be manufactured to stock and which ones on order and, how to allocate capacity among various MTO-MTS products. This paper presents the state-of-the-art literature review of the combined MTO-MTS production situations. A variety of production management issues in the context of food processing companies, where combined MTO-MTS production is quite common, are discussed in details. The authors propose a comprehensive hierarchical planning framework that covers the important production management decisions to serve as a starting point for evaluation and further research on the planning system for MTO-MTS situations.

Discrete Event Simulation Modelling for Dynamic Decision Making in Biopharmaceutical Manufacturing

With the increase in demand for biopharmaceutical products, industries have realised the need to scale up their manufacturing from laboratory-based processes to financially viable production processes. In this context, biopharmaceutical manufacturers are increasingly using simulation-based approaches to gain transparency of their current production system and to assist with designing improved systems. This paper discusses the application of Discrete Event Simulation (DES) and its ability to model the various scenarios for dynamic decision making in biopharmaceutical manufacturing sector. This paper further illustrates a methodology used to develop a simulation model for a biopharmaceutical company, which is considering several capital investments to improve its manufacturing processes. A simulation model for a subset of manufacturing activities was developed that facilitated ‘what-if’ scenario planning for a proposed process alternative. The simulation model of the proposed manufacturing process has shown significant improvement over the current process in terms of throughout time reduction, better resource utilisation, operating cost reduction, reduced bottlenecks etc. This visibility of the existing and proposed production system assisted the company in identifying the potential capital and efficiency gains from the investments therefore demonstrating that DES can be an effective tool for making more informed decisions. Furthermore, the paper also discusses the utilisation of DES models to develop a number of bespoke productivity improvement tools for the company

Non-continuous and variable rate processes: Optimisation for energy use

The need to develop new and improved ways of reducing energy use and increasing energy intensity in industrial processes is currently a major issue in New Zealand. Little attention has been given to optimisation of non-continuous processes in the past, due to their complexity, yet they remain an essential and often energy intensive component of many industrial sites. Novel models based on pinch analysis that aid in minimising utility usage have been constructed here through the adaptation of proven continuous techniques. The knowledge has been integrated into a user friendly software package, and allows the optimisation of processes under variable operating rates and batch conditions. An example problem demonstrates the improvements in energy use that can be gained when using these techniques to analyse non-continuous data. A comparison with results achieved using a pseudo-continuous method show that the method described can provide simultaneous reductions in capital and operating costs

Tightness of lead times

This paper introduces a general, formal treatment of dynamic constraints, i.e., constraints on the state changes that are allowed in a given state space. Such dynamic constraints can be seen as representations of "real world" constraints in a managerial context. The notions of transition, reversible and irreversible transition, and transition relation will be introduced. The link with Kripke models (for modal logics) is also made explicit. Several (subtle) examples of dynamic constraints will be given. Some important classes of dynamic constraints in a database context will be identified, e.g. various forms of cumulativity, non-decreasing values, constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several properties of these dependencies will be treated. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.