Supply chain management for the process industry

This thesis investigates some important problems in the supply chain management (SCM) for the process industry to fill the gap in the literature work, covering production planning and scheduling, production, distribution planning under uncertainty, multiobjective supply chain optimisation and water resources management in the water supply chain planning. To solve these problems, models and solution approaches are developed using mathematical programming, especially mixed-integer linear programming (MILP), techniques. First, the medium-term planning of continuous multiproduct plants with sequence-dependent changeovers is addressed. An MILP model is developed using Travelling Salesman Problem (TSP) classic formulation. A rolling horizon approach is also proposed for large instances. Compared with several literature models, the proposed models and approaches show significant computational advantage. Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based formulation is adapted to model the sequence-dependent changeovers between product groups. An edible-oil deodoriser case study is investigated. Later, the proposed TSP-based formulation is incorporated into the supply chain planning with sequence-dependent changeovers and demand elasticity of price. Model predictive control (MPC) is applied to the production, distribution and inventory planning of supply chains under demand uncertainty. A multiobjective optimisation problem for the production, distribution and capacity planning of a global supply chain of agrochemicals is also addressed, considering cost, responsiveness and customer service level as objectives simultaneously. Both ε- constraint method and lexicographic minimax method are used to find the Pareto-optimal solutions Finally, the integrated water resources management in the water supply chain management is addressed, considering desalinated water, wastewater and reclaimed water, simultaneously. The optimal production, distribution and storage systems are determined by the proposed MILP model. Real cases of two Greek islands are studied

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 discrete-event simulation and production scheduling system for an FMCG plant manufacturing liquid products

Unilever South Africa plans to build a new factory in Boksburg. This factory will manufacture products in the Home Care and Personal Care market categories. It is intended to be a state-ofthe art facility that incorporates best practices from facilities all over the world and produces at very low cost. The purpose of this project is to provide Unilever with a system for effectively planning and managing the new factory. Ideally, it would be possible to adapt the system to other projects in future. Specifically, some mechanism for predicting the number and size of machines needed in the new factory is required. To this end, a discrete-event simulation is being developed to study the proposed investment. This will be used to verify that the proposed factory will be able to meet projected demand. Since production output in this facility will depend heavily on the equipment used as well as the quality of the scheduling, a production scheduling system will also be developed. This scheduling system will utilise various techniques to compile optimal or near-optimal schedules. The generated schedules, along with one from the planning department, can then be run through the simulation to further study and improve both the scheduling system and the simulation. The project will be successful if it can verify the required investment levels.Thesis (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2012

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

Integrated feedstock optimisation for multi-product polymer production

Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: A chemical complex can have multiple value chains, some of which may span across geographical locations. Decisions regarding the distribution of feedstock and intermediate feedstock to different production units can occur at different time intervals. This is highlighted as two problems, a feedstock distribution problem and an intermediate feedstock distribution problem. Unexpected events can cause an imbalanced value chain which requires timely decision-making to mitigate further adverse consequences. Scheduling methods can provide decision support during such events. The purpose of this research study is to develop an integrated decision support system which handles the two problems as a single problem and maximises profit in the value chain for hourly and daily decision-making. A high-level DSS architecture is presented that incorporates metaheuristic algorithms to generate production schedules for distribution of feedstock through the value chain. The solution evaluation process contains a balancing period to enable the application of metaheuristics to this type of problem and a novel encoding scheme is proposed for the hourly interval problem. It was found that metaheuristics algorithms can be used for this problem and integrated into the proposed decision support system.AFRIKAANSE OPSOMMING: ’n Chemiese kompleks kan verskeie waardekettings hê, waarvan sommige oor geografiese gebiede strek. Besluite rakende die verspreiding van grondstowwe en intermediêre grondstowwe na verskillende produksie-eenhede kan op verskillende tydsintervalle plaasvind. Dit word uitgelig as twee probleme: ’n probleem met die verspreiding van grondstowwe en ’n intermediêre grondstowwe verspreidingsprobleem. Onverwagte gebeure kan ’n ongebalanseerde waardeketting veroorsaak wat tydige besluitneming benodig om verdere gevolge te versag. Beplanningsmetodes kan ondersteuning bied tydens sulke geleenthede. Die doel van hierdie navorsingstudie was om ’n geïntegreerde stelsel vir besluitnemingsondersteuning oor die twee probleme as een probleem te ontwikkel, wat wins in die waardeketting vir uurlikse en daaglikse besluitneming maksimeer. ’n Hoëvlak DSS-argitektuur word aangebied met metaheuristieke om produksieskedules vir verspreidingstowwe deur die waardeketting te genereer. Die oplossingsevalueringsproses bevat ’n balanseerperiode om die metaheuristiek op hierdie tipe probleme toe te pas, en ’n nuwe koderingskema word voorgestel vir die uurlikse intervalprobleem. Die gevolgtrekking word gemaak dat metaheuristieke vir hierdie probleem gebruik kan word en ge¨ıntegreer kan word in die voorgestelde ondersteuningsstelsel vir besluitneming.Doctora

Study on application possibilities of Case-Based Reasoning on the domain of scheduling problems

Ces travaux concernent la mise en place d'un système d'aide à la décision, s'appuyant sur le raisonnement à partir de cas, pour la modélisation et la résolution des problèmes d'ordonnancement en génie des procédés. Une analyse de co-citation a été exécutée afin d'extraire de la littérature la connaissance nécessaire à la construction de la stratégie d'aide à la décision et d'obtenir une image de la situation, de l'évolution et de l'intensité de la recherche du domaine des problèmes d'ordonnancement. Un système de classification a été proposée, et la nomenclature proposée par Blazewicz et al. (2007) a été étendue de manière à pouvoir caractériser de manière complète les problèmes d'ordonnancement et leur mode de résolution. Les difficultés d'adaptation du modèle ont été discutées, et l'efficacité des quatre modèles de littérature a été comparée sur trois exemples de flow-shop. Une stratégie de résolution est proposée en fonction des caractéristiques du problème mathématique. ABSTRACT : The purpose of this study is to work out the foundations of a decision-support system in order to advise efficient resolution strategies for scheduling problems in process engineering. This decision-support system is based on Case-Based Reasoning. A bibliographic study based on co-citation analysis has been performed in order to extract knowledge from the literature and obtain a landscape about scheduling research, its intensity and evolution. An open classification scheme has been proposed to scheduling problems, mathematical models and solving methods. A notation scheme corresponding to the classification has been elaborated based on the nomenclature proposed by Blazewicz et al. (2007). The difficulties arising during the adaptation of a mathematical model to different problems is discussed, and the performances of four literature mathematical models have been compared on three flow-shop examples. A resolution strategy is proposed based on the characteristics of the scheduling problem

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

Imperial Users onl