A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

Models and algorithms for decomposition problems

This thesis deals with the decomposition both as a solution method and as a problem itself. A decomposition approach can be very effective for mathematical problems presenting a specific structure in which the associated matrix of coefficients is sparse and it is diagonalizable in blocks. But, this kind of structure may not be evident from the most natural formulation of the problem. Thus, its coefficient matrix may be preprocessed by solving a structure detection problem in order to understand if a decomposition method can successfully be applied. So, this thesis deals with the k-Vertex Cut problem, that is the problem of finding the minimum subset of nodes whose removal disconnects a graph into at least k components, and it models relevant applications in matrix decomposition for solving systems of equations by parallel computing. The capacitated k-Vertex Separator problem, instead, asks to find a subset of vertices of minimum cardinality the deletion of which disconnects a given graph in at most k shores and the size of each shore must not be larger than a given capacity value. Also this problem is of great importance for matrix decomposition algorithms. This thesis also addresses the Chance-Constrained Mathematical Program that represents a significant example in which decomposition techniques can be successfully applied. This is a class of stochastic optimization problems in which the feasible region depends on the realization of a random variable and the solution must optimize a given objective function while belonging to the feasible region with a probability that must be above a given value. In this thesis, a decomposition approach for this problem is introduced. The thesis also addresses the Fractional Knapsack Problem with Penalties, a variant of the knapsack problem in which items can be split at the expense of a penalty depending on the fractional quantity

Optimization-based decision support systems for planning problems in processing industries

Summary Optimization-based decision support systems for planning problems in processing industries Nowadays, efficient planning of material flows within and between supply chains is of vital importance and has become one of the most challenging problems for decision support in practice. The tremendous progress in hard- and software of the past decades was an important gateway for developing computerized systems that are able to support decision making on different levels within enterprises. The history of such systems started in 1971 when the concept of Decision Support Systems (DSS) emerged. Over the years, the field of DSS has evolved into a broad variety of directions. The described research in this thesis limits to the category of model-driven or optimization-based DSS. Simultaneously with the emergence of DSS, software vendors recognized the high potentials of available data and developed Enterprise Systems to standardize planning problems. Meanwhile, information oriented systems like MRP and its successors are extended by the basic concepts of optimization based decision support. These systems are called Advanced Planning Systems (APS). The main focus of APS is to support decision making at different stages or phases in the material flow, i.e. from procurement, production, distribution to sales (horizontal-axis), on different hierarchical aggregation levels (vertical-axis) ranging from strategic (long-term) to operational (short- term) planning. This framework of building blocks decomposes planning tasks hierarchically into partial planning problems. This basic architecture of the planning processes in APS is known as the Supply Chain Planning Matrix (SCPM). Compared to, for instance, discrete parts manufacturing, planning tasks are much more complicated in processing industries due to a natural variation in the composition of raw materials, the impact of processing operations on properties of material flows, sequence dependent change-over times, the inevitable decline in quality of product flows and relatively low margins. These specific characteristics gave rise to focus on optimization-based decision support in the domain of processing industries. The problems to be addressed in this field call for (inter-related) decisions with respect to the required raw materials, the production quantities to be manufactured, the efficient use of available resources, and the times at which raw materials must be available. Although different APS modules can interact directly, coordination and integration is often restricted to the exchange of data flows between different modules. Given the need for specific integrated decision support, the research presented in this thesis focusses particularly on medium to short term decision support at production stage in processing industry, including the vertical and horizontal integration and coordination with adjacent building blocks in the SCPM. Extensive reviews from literature show that the gap between research and practice of DSS is widening. As the field of DSS was initiated as an application oriented discipline, the strategy of what is referred to as “application-driven theory” was taken as the preferred approach for this thesis. “Application-driven” refers to a bottom-up approach which means that the relevance of the research should both be initiated and obtained from practice. The intended successful use of the proposed approaches should, where possible, be represented by tests of adequacy. Simultaneously, the contribution to “theory” aims to be a recognizable part of the research effort, i.e. obtained understanding and insights from problems in practice should provide the basis for new approaches. Based on the preceding considerations we defined the following general research objective: General research objective To support medium- to short term planning problems by optimization-based models and solution techniques such that: i) The applicability and added value of (prototype) systems is recognized and carried by decision makers in practice ii) The proposed approaches contribute to knowledge, understanding and insights from a model building and – solving point of view. In order to link the general objective with the different studies in the thesis, we defined five, recurring research premises, i.e. Professional relevance and applicability (P1), Aggregation (P2), Decomposition and reformulation (P3), Vertical integration at production level (P4), and Horizontal coordination and integration (P5). The overarching premise P1 refers to the first part of the research objective. All other premises refer to the second part of the research objective, i.e. model building and/or – solving. Several planning issues are studied to give substance to the research objective and each study is connected to at least two research premises. Study 1: Planning and scheduling in food processing industry The main question in Chapter 2 was:” How to apply aggregation, decomposition and reformulation in model-based DSS at planning and scheduling level such that the aspect of decision support is recognized and appreciated by decision makers in practice, and which level of aggregation is needed to integrate production planning (i.e. lot-sizing) and scheduling problems in a single model? The study consists of two parts. The first part of the study refers to a case study for the bottleneck packaging facilities of a large dairy company. The goal was to develop, implement and test a pilot DSS which was able to deliver solutions recognized and carried by decision makers at lower decision levels. The latter aim implied that a straight-forward aggregation on time, product type, resources or product stage, was not preferred. The key to develop an approach for regular use was to identify and take advantage of specific problem characteristics. Clustering of numerous jobs, while retaining information at order level, could be exploited in a reformulation approach. The inclusion of (combined) generalized- and variable upper bound constraints gave very tight lower bounds and sparse search trees. An extensive test phase in daily practice showed that the main benefit of the DSS was the initial quality of the generated plans including the time needed to generate these schedules. Hence, decision makers could i) postpone their planning tasks, ii) conveniently cope with rush orders or planned maintenance and iii) easily generate alternatives or revised plans when unforeseen disturbances occur. Moreover, the graphical presentation and overview of the (future) working schedule enabled order acceptance to make use of remaining capacity. The study also showed that planning problems in practice cannot be captured exhaustively by a (simplified) model. Decision makers need the opportunity to modify automatically generated plans manually and use human judgement and experience such that the solution is tuned to the actual situation. Hence, the DSS should not be considered as an optimizer but rather as a tool for generating high quality plans to be used for further analysis. Within this context the various options of a user-friendly, graphical, and fully interactive user interface, were of major importance. Although the case study clearly demonstrates the validity of earlier case based DSS research for current days APS, the proposed approach is hardly a generic solution for a complete vertical integration between lot-sizing and scheduling. If lot-size decisions are strongly affected by the sequence of jobs, production planning and scheduling should be performed simultaneously. As the described case refers to an earlier study and today’s APS do not provide modules for integrated lot-sizing and scheduling, the second part of the study gives an overview of developments in literature regarding lot-sizing and scheduling models and assess their suitability for addressing sequence-dependent setups, non-triangular setups and product decay. The review shows a tendency in which so-called Big Bucket (BB) models are currently proposed for short term time horizons too. However, we argue that segmentation of the planning horizon is a key issue for simultaneous lot-sizing and scheduling. The advantage of BB models may become a major obstacle for i) the effectiveness of simultaneous lot-sizing and scheduling, and ii) addressing specific characteristics in food processing industry. Study 2: Vertical integration of lot-sizing and scheduling in food processing industry Chapter 3 focused on a complete integration of lot-sizing and scheduling decisions in a single model. The main question was:” How to integrate production planning (i.e. lot- sizing) and scheduling problems in a single model, such that common assumptions regarding the triangular setup conditions are relaxed and issues of product decay and limited shelf lives are taken into account?” The literature research in Chapter 2 revealed that the computational advantage of time oriented aggregation in BB models may become a major obstacle in addressing the identified characteristics in FPI. In addition, product decay is primarily associated with the “age” of products and consequently relates to the segmentation of the time- horizon. Therefore, two SB models are developed to demonstrate the impact of non- triangular setups and product decay on the generated solutions. Small scale examples were used to demonstrate how a small change in the balance between inventory - and changeover costs may generate significantly different solutions, especially when the triangular setup conditions do not hold. The developed models are potentially very large formulations and, as expected, hard to solve. Exploratory research was conducted with a Relax-and-Fix (R&F) heuristic. The heuristic is based on a decomposition of the time horizon. Numerical results of small to medium sized problem instances are promising. However, solving real-size problem instances is not possible yet. Study 3: Integrated planning between procurement and production The case study in Chapter 4 focussed on the need for horizontal coordination and integration between the phases procurement and production, which is of particular importance in inter-organizational supply chains. The main question was:” How to model and solve an integrated planning problem between procurement and production, both on a mid-term and short-term planning level, in an inter-organizational supply chain? The research question was projected on an illustrative milk collection problem in practice. The aim was to develop a pilot DSS that lifted decision support for a “weaker” partner in a food supply chain to a higher level, and to illustrate the importance of horizontal integration between the phases procurement and production in an APS framework. Problem analysis revealed that the problem can be classified as an extension of the Periodic Vehicle Routing Problem (PVRP). The problem was decomposed into more tractable sub problems on different hierarchical levels, i.e. the daily (vehicle) routing problem was separated from a medium-term planning problem. On the higher planning level, numerous suppliers were aggregated such that total supply within a cluster met (multiple) vehicle loading capacities. The continuous supply of relatively small amounts from many suppliers had to be balanced with strict delivery conditions at processing level. A model was developed to assign a single (stable) collection rhythm to each cluster such that the total, weighted deviation of desired processing levels on various days in the planning horizon was minimized. The applied aggregation on the higher planning level turned out to be very beneficial for the required disaggregation at the lower planning level. Once supplier farms were geographically grouped into clusters and the aggregated supply within a cluster was assigned to a single collection rhythm with fixed collection days, the (initial) daily routing problem was considerably easier to solve for vehicle schedulers. The computational complexity of the problem was reduced by exploiting application-based properties algorithmically in a specific branch-and-bound scheme, i.e. a customized approach of Special Ordered Sets type 1 (SOS1) This approach made it possible to solve the generated problems exactly for real-size problem instances. The various facilities of a user-friendly and interactive man-machine interface (i.e. an input, planning, simulation and analysing module) turned out to be essential. Decision makers could easily change the data, and the generated plans, in a separate simulation module. However, the impact of any modification was immediately visualised by several (conflicting) indicators in the output screens, both on supply and demand level. Study 4: Mixed Integer (0-1) Fractional Programming in Paper Production Industry The study in Chapter 5 focussed on the impact of technical settings of production units on material flows. The main question was:” How to support decision-makers in practice if crucial properties of end products simultaneously depend on (endogenous) types of raw materials with different chemical or physical properties and (endogenous) technical settings of processing units? The goal of the study was to revise and upgrade an existing, locally used DSS, to a tailored and flexible tool for decision support within the enterprise. The study revealed that the aimed extension towards multi-objective decision support, together with new physical insight for calculating properties of end products due to process operations, had a substantial impact on the optimization module. The proposed solution procedure takes advantage of the problem characteristics and gives rise i) to apply and extend a classical reformulation approach for continuous linear fractional programming (FP) problems to a more general class of mixed integer (binary) FP problems and ii) to exploit the special structure between the original non- linear mixed integer model and the continuous, linear reformulation by applying the concept of Special Ordered Sets type 1 (SOS1). Although Chapter 5 focusses in particular on the reformulation and solution approach, the DSS consists of four main building blocks, i.e. the user interface, a scenario manager, a simulation- and optimization routine. The optimization module provides a powerful tool to find feasible solutions and the best (unexpected) recipes for any available set of raw materials. Moreover, it provides an innovative way of decision support for purchasing (new) pulps on the market, for assigning available pulps to different paper grades, and for attuning available stock levels of raw materials to (changing) production targets for different paper grades. The results of the optimization routine are mainly used to obtain alternative recipes for different paper grades. Usually, these recipes are stored as base scenarios and adapted to daily practice in the simulation module. Main conclusions and future research Based on the studies in the Chapters 2 and 3 we conclude that no generically applicable models and/or solution approaches exist for simultaneous planning and scheduling in processing industries. More industry-specific solutions are needed incorporating specificities of different production environments into those models. The key to develop solvable approaches for contemporary practice may be i) to use knowledge and experience from practice and take advantage of specific characteristics in different problem domains during model construction, and/or ii) to identify and exploit special problem structures for solving the related models. We conclude that surprisingly little research has been devoted to issues of coordination and integration between “procurement” and “production”. The studies in the chapters 4 and 5 confirm that sourcing of (raw) materials flows needs more attention in processing industries, particularly in push-oriented, inter-organizational networks. The valorisation of raw materials can be improved even more if the composition of raw materials is considered too in future planning problems at production level. In the second part of this thesis we focused on extensions for the applicability of Special Ordered Sets type 1 (SOS1), both from an algorithmic (Chapter 4) and modelling (Chapter 5) point of view. We conclude that the concept of SOS1 can extend a classical reformulation approach for continuous fractional programming (FP) problems, to a specific class of mixed integer (0-1) FP problems. Moreover, we conclude that a natural ordering of the variables within the sets is not necessary to make their use worthwhile. A separate (user defined) reference row or weights associated to the variables in the sets might be omitted for an efficient use of SOS1 in commercially available mathematical programming packages. However, this requires further research and extensive computational tests.</p

Proceedings of the third International Workshop of the IFIP WG5.7

Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment

MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento

Tesis por compendioEl sistema de planificación de requerimientos de materiales o MRP (Material Requirement Planning), desarrollado por Orlicky en 1975, sigue siendo en nuestros días y, a pesar de sus deficiencias identificadas, el sistema de planificación de la producción más utilizado por las empresas industriales. Las evoluciones del MRP se vieron reflejadas en el sistema MRPII (Manufacturing Resource Planning), que considera restricciones de capacidad productiva, MRPIII (Money Resource Planning), que introduce la función de finanzas; y la evolución comercial del mismo en el ERP (Enterprise Resource Planning), que incorpora modularmente todas las funciones de la empresa en un único sistema de decisión, cuyo núcleo central es el MRP. Los desarrollos posteriores de los sistemas ERP han incorporado las nuevas tecnologías de la información y comunicaciones. Asimismo, éstos se han adaptado al contexto económico actual caracterizado por la globalización de los negocios y la deslocalización de los proveedores desarrollando otras funciones como la gestión de la cadena de suministro o del transporte, entre otros. Por otro lado, existen muchos trabajos en la literatura académica que han intentado resolver algunas de las debilidades del MRP tales como la optimización de los resultados, la consideración de la incertidumbre en determinados parámetros, el inflado de los tiempos de entrega, etc. Sin embargo, tanto en el ámbito comercial como en el científico, el MRP y sus variantes se centran en el requerimiento de los materiales y en la planificación de las capacidades de producción, lo que es su desventaja principal en aquellas cadenas de suministro donde existe una gran deslocalización de los proveedores de materias primas y componentes. En estos entornos, la planificación del transporte adquiere un protagonismo fundamental, puesto que los elevados costes y las restricciones logísticas suelen hacer subóptimos e incluso infactibles los planes de producción propuestos, siendo la re-planificación manual una práctica habitual en las empresas. Esta tesis doctoral propone un modelo denominado MRPIV, que considera de forma integrada las decisiones de la planificación de materiales, capacidades de recursos de producción y el transporte, con las restricciones propias de este último, tales como diferentes modos de recogida (milk-run, camión completo, rutas) en la cadena de suministro con el objetivo de evitar la suboptimización de estos planes que en la actualidad se generan usualmente de forma secuencial e independiente. El modelo propuesto se ha validado en una cadena de suministro del sector del automóvil confirmando la reducción de costes totales y una planificación más eficiente del transporte de los camiones necesarios para efectuar el aprovisionamiento.Díaz-Madroñero Boluda, FM. (2015). MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48524TESISCompendi

Análisis del problema de dimensionado de lotes con remanufactura e incertidumbre en la demanda y los retornos

El problema económico de dimensionamiento de lotes con remanufactura (ELSR por sus siglas en inglés) es una extensión del problema tradicional, en el que la demanda también puede satisfacerse mediante la remanufactura de productos usados (también llamados retornos) que son devueltos al origen. En la práctica, las decisiones de planificación deben tomarse en condiciones de incertidumbre. En este contexto, las decisiones deben tener en cuenta todas las realizaciones posibles de los parámetros de interés. Nuestro trabajo considera dos variantes del ELSR con incertidumbre en las cantidades de demanda y de retornos: con una sola línea tanto para la producción como para la remanufacturación, y con líneas dedicadas para cada una de estas dos actividades. Como parte de la revisión de la literatura, se presentan diferentes reformulaciones existentes y enfoques de solución para el caso determinista y de variantes estocásticas. Esta revisión se amplía mediante el diseño de formulaciones de programación entera mixta estocástica para las que se utilizan árboles de escenarios. La formulación natural del problema se reformula como un problema de localización de instalaciones, el cual se ha demostrado que es capaz de brindar buenos resultados para ELSR en su versión determinista. Nuestro objetivo luego, fue fortalecer las formulaciones naturales mediante la incorporación de inecuaciones válidas de camino y de árbol. También presentamos una modificación de la heurística relax-and-fix para el problema. Finalmente, presentamos los resultados obtenidos de la experimentación numérica llevada a cabo para el caso de líneas dedicadas, junto con las conclusiones y algunas posibles líneas para investigaciones futuras. Los hallazgos más relevantes que surgen del estudio computacional son los siguientes: la formulación con inecuaciones válidas de árbol tiene el potencial de mejorar la relajación a Programación Lineal del problema; en comparación con una formulación natural, el rendimiento de las inecuaciones de camino y de las reformulaciones de localización de las instalaciones disminuyen a medida que aumenta el horizonte de planificación del problema; el procedimiento heurístico sugerido parece ser prometedor para problemas de gran tamaño

Proceedings from the 1992 national conference

Presented at Irrigation and water resources in the 1990's: proceedings from the 1992 national conference held on October 5-7, 1992 in Phoenix, Arizona.Includes bibliographical references.Sponsored by U.S. Committee on Irrigation and Drainage.Interdisciplinary teams for assessing the performance of irrigated agriculture systems -- Putah South Canal remote acoustic water level monitoring and flow measurement -- Decentralized constant-volume control of irrigation canals -- Field manufacture and application of reinforced plastic canal and pipe linings -- Improving channel maintenance methods for Egypt's irrigation systems -- Routing flood water through an irrigation delivery system -- Experience with flexible schedules and automation on pilot projects -- Canal linings used by the Bureau of Reclamation with emphasis on rehabilitation -- The California Farm Water Coalition: telling thirsty Californians why agriculture needs water -- Institutional framework and challenges in management of agricultural water use in South Florida -- Technology transfer lessons from a U.S. water district -- Management of water conservation through irrigation system modernization and rehabilitation -- Artificial recharge of groundwater -- Long-term storage through indirect recharge -- Mitigating agricultural impacts on groundwater through desalination -- Agriculture's impact on water resources in Eastern Europe: Bulgaria, Hungary, and Romania -- How probiotic fertilizers improve irrigation efficiency, buffer salts, and reduce nitrate infiltration into groundwater -- Drought, supply shortages and E.S.A.: can the farmer survive? -- Avoiding pitfalls in canal automation -- AZSCHED computer software for irrigation scheduling -- Determination of irrigated crops consumptive water use by remote sensing and GIS techniques for river basins -- GIS and conjunctive use for irrigated agriculture -- Mapping technology in the '90's for GIS applications to irrigation and drainage

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order