Revitalising the Single Batch Environment: A 'Quest' to Achieve Fairness and Efficiency

In the realm of computer systems, efficient utilisation of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimise process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. This research proposes a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilising periodic sorting technique to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimised solution for batch scheduling while ensuring system responsiveness

Batch Scheduling Using Matrix Approach Under Supply Change

Batch processing is the predominant mode of production operations for the low volume manufacturing of chemical, polymers and food products. Batch processing can be classified as single product batch process or multiple product batch process. Single product batch process in which single product is produce as compared to multiple product batch process where more than one product is produced using the same batch facility in successive campaigns. More recent works have considered the more complicated cases of processes in which each of the products has its own production sequence and make use of processing units in different combinations. In batch processmg, the profitability in economics lies heavily on the scheduling of the production sequence. Scheduling optimization normally aimed at minimizing the makespan (i.e. completion time of the batch process.), leading to overall optimization of the production cost. The complication in scheduling is amplified when the feed change is taken into account. Disruption of feed typically requires a large amount of time to generate an optimal schedule. The proposed approach to address these issues in order to optimize batch production uses matrix to represent the batch recipes which is then solved optimal makespan based on a selected sequence. The arrangement of the matrix rows is according to the best sequence based on the availability or the disruption of supply. The user is then provided with production sequence options based on process requirement and supply

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

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

Proactive management of uncertainty to improve scheduling robustness in proces industries

Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés.Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment

A CAPACITY MODEL FOR RESEARCH BASED GOVERNMENT MANUFACTURING SYSTEMS

Manufacturing systems take longer than necessary to be designed and implemented, hence the greater developmental cost. A class of manufacturing systems exist which would benefit from the concepts of reverse engineering, to reduce lead times for establishing critical manufacturing capabilities essential to national safety and security. There is a need to reverse engineer these manufacturing systems as no current system and/or body of knowledge exists. Manufacturing systems vary in their ability to deliver products in an efficient and reliable manner and hence the variability in national readiness. Presently the design of manufacturing systems for some critical operations ranges from an educated trial and error process to duplicating from documentation and professional expertise. The literature search highlights the non-existence of a current systematic operational reverse engineering model that could be the standard for designing manufacturing systems. One of the main constraints in the manufacturing is that the time for production is limited and denoted by time available (TA). The time to finish (TF) is the time needed to complete the manufacturing operations in a facility so that the entire quantity demanded is produced, from start to end, in the production line. If the TF is less than the TA there is sufficient capacity to meet the demand. Literature search indicates that no study has been conducted to compute the TF. Further, it also indicates that no study has been carried out focusing on the vi impact of variations and disruptions at the design stage, even though these topics are covered in analysis of existing operational systems. The algorithms and mathematical model were developed. The model will compute the exact TF taking into account variation, disruption and flow issues. The equation for TF was developed. The model to be designed is validated using information from a government manufacturing system

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations. Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency

Synthesis and Stochastic Assessment of Cost-Optimal Schedules

We present a novel approach to synthesize good schedules for a class of scheduling problems that is slightly more general than the scheduling problem FJm,a|gpr,r_j,d_j|early/tardy. The idea is to prime the schedule synthesizer with stochastic information more meaningful than performance factors with the objective to minimize the expected cost caused by storage or delay. The priming information is obtained by stochastic simulation of the system environment. The generated schedules are assessed again by simulation. The approach is demonstrated by means of a non-trivial scheduling problem from lacquer production. The experimental results show that our approach achieves in all considered scenarios better results than the extended processing times approach