Solving scheduling problems in a multi-stage multi-product batch pharmaceutical industry

An iterative two-stage decomposition solution strategy for solving real-world scheduling problems in multi-stage multi-product batch plants is presented. The proposed method has as a core a mixed integer mathematical model, and consists of a constructive step, wherein a feasible and good solution is rapidly generated by following some insertion criteria, and an improvement step, wherein the initial solution is systematically enhanced by adopting several rescheduling techniques. The proposed strategy performance is tested on a number of problem instances of a complicated real-world multi-stage multi-product pharmaceuticals scheduling problem. High quality solutions are reported within reasonable computational time.Fil: Kopanos, Georgios. Universidad Politecnica de Catalunya; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); ArgentinaFil: Puigjaner, Luis. Universidad Politecnica de Catalunya; Españ

Effective decomposition algorithm for multistage batch plant scheduling

This paper presents a new algorithm for the scheduling of batch plants with a large number of orders and sequence-dependent changeovers. Such problems are either intractable or yield poor solutions with full-space approaches. We use decomposition on the entire set of orders and derive the complete schedule in several iterations. The key idea is to allow for partial rescheduling without altering the main decisions in terms of unit assignments and sequencing, so that the complexity is kept at a manageable level. It has been implemented with a unit-specific continuous-time model and tested for different decomposition settings. The results show that a real-life 50-order, 17-unit, 6-stage problem can effectively be solved in roughly 6 minutes of computational time

An Analytical Review Of The Relationship Between Dispatching Rules And Performance For Dynamic Scheduling In An Identical Parallel Machine Environment

This report consist an analytical review of the relationship between dispatching rules and performance measure for dynamic scheduling problem. The review is focused on an identical parallel machine environment. The dynamic issues that considered are machine breakdown and operator absent. The main objective of the research work is to analyze the performance of dispatching rules against different performance measures. Five suitable dispatching rules that considering the priority of weighs which called weighted dispatching rules is used. Dispatching rule is used to specify which job should be selected for work next from among a queue of jobs. Simulation model is developed based on real world case study using WITNESS software. Than the experiment model is developed by adding disturbance parameter likes machine breakdown and operator absent. The experiment is done to determine the effects of the disturbance parameter to the performance of dispatching rules used. The experiment result is proved by analysis of variance (ANOVA). From the experiment analysis, the different dispatching rules provide the different result of performance measures. Dispatching rules can be used to determine the minimum throughput time, minimum lateness and earliness of the jobs. The performance of dispatching rules also depends on disturbance parameter that used in the experiment model and the quantity of jobs required

Modelos lineales mixtos para la programación de la producción con una sola etapa: estado del arte

En este documento se presenta una revisión bibliográfica y una posterior taxonomía para modelos de programación lineal entera mixta (PLEM) en planificación de la producción. En concreto, se analizan modelos de una sola etapa por su interés en diversos tipos de procesos productivos. Se han estudiado un total de 30 modelos que se clasifican en cuanto a los objetivos perseguidos, a su formulación, a su representación y, además, según qué características y restricciones han sido tenidas en cuenta. Como resultado, estos modelos se presentan de forma clara y concisa dando un marco de trabajo para futuros desarrollos

Optimisation approaches for supply chain planning and scheduling under demand uncertainty

This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, Local Search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work

Decision support for the production and distribution of electricity under load shedding

Every day national power system networks provide thousands of MW of electric power from generating units to consumers. This process requires different operations and planning to ensure the security of the entire system. Part of the daily or weekly operation system is the so called Unit Commitment problem which consists of scheduling the available resources in order to meet the system demand. But the continuous growth in electricity demand might put pressure on the ability of the generation system to sufficiently provide supply. In such case load shedding (a controlled, enforced reduction in electricity supply) is necessary to prevent the risk to system collapse. In South Africa at the present time, a systematic lack of supply has meant that regular load shedding has taken place, with substantial economic and social costs. In this research project we study two optimization problems related to load shedding. The first is how load shedding can be integrated into the unit commitment problem. The second is how load shedding can be fairly and efficiently allocated across areas. We develop deterministic and stochastic linear and goal programming models for these purposes. Several case studies are conducted to explore the possible solutions that the proposed models can offer

Optimal synthesis of storageless batch plants using the process intermediate storage operational policy

A novel operational policy, the Process Intermediate Storage (PIS) operational policy, is introduced and used to synthesize, schedule and design multipurpose batch plants. The model is based on the State Sequence Network (SSN) and non-uniform discretization of the time horizon of interest model developed by Majozi&Zhu (2001). Two cases are studied to determine the effectiveness of the operational policy. A plant without dedicated intermediate storage is considered in the first case. In this case the throughput is maximized with and without the use of the PIS operational policy. The use of the PIS operational policy results in a 50% improvement in the throughout. The second case is used to determine the minimum amount of intermediate storage while maintaining the throughput achieved with infinite intermediate storage. This resulted in a 33% reduction in the amount of dedicated intermediate storage. The models developed for both cases are MILP models. A design model is then developed to exploit the attributes of the PIS operational policy. The design model is a MINLP due to the capital cost objective function. This model is applied to a literature example and an industrial case study and in both cases results in improved flowsheets and reduced capital cost.Dissertation (MEng)--University of Pretoria, 2008.Chemical Engineeringunrestricte

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Methods and algorithms for integrated multi-scale optimisation of production planning and scheduling

Imperial Users onl