Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Improving urban deliveries via collaboration

Distribution of goods is essential for the economic development of cities but at the same time it entails several problems to the urban systems and different stakeholders. Carriers spend a significant portion of their cost in the last-mile distribution due to traffic congestion and lack of available loading/unloading facilities. In turn, citizens undergo environmental effects like pollution, noise or space competition. Collaborative transportation is currently one of the major trends in transportation research due to its potential benefits with little need for big infrastructure or costly investments. This thesis deals with three different situations that appear repeatedly in the urban context, which can be improved by means of collaboration among private companies and/or public authorities. The first part of the thesis studies a little-disruptive collaboration approach, which is based on sharing loading/unloading urban facilities via an in-advance booking system, managed by local public authorities. In this context, the Parking Slot Assignment Problem is the mathematical problem that finds assignments of carriers to parking places that satisfy their time windows requests. We propose a feasibility model first, and then four other models with various objective functions that penalize in different ways the deviation from the requested time windows. We propose and compare two different formulations: one with time as a continuous variable and a second one with time discretization. Finally, we evaluate and compare the different proposals with extensive computational experiments in a set of test instances. An intermediate level of collaboration among carriers is studied in the second part of this thesis. Urban areas have high customers density and usually there are shared customers (customers with demand from different carriers in the same time horizon). We propose an innovative problem: the Shared Customer Collaboration Vehicle Routing Problem, where several carriers are willing to collaborate transferring part of the demand of their shared customers, if the overall transportation cost is reduced. A vehicle-based and a load-based formulation are studied, and experimented over a specifically generated instance set. The highest level of collaboration in urban deliveries resorts to Urban Consolidation Centers, which are normally led by public authorities but need the collaboration of carriers for a successful implementation. Urban Consolidation Centers are urban terminals where the load from different carriers is consolidated and then, a unique neutral carrier performs last-mile deliveries. In the third part of the thesis we propose continuous models that analyze the improvement in efficiency of urban distribution with the use of Urban Consolidation Centers under different assumptions. Continuous approximation models are known to produce robust solutions, which are useful to provide guidelines for general cases through sensitive analysis. In the three parts of the thesis, innovative models and approaches are proposed and validated on experiments that use data from real scenarios.La distribució urbana de mercaderies és una activitat essencial pel desenvolupament de les ciutats. Al mateix temps, però, comporta diversos problemes als nuclis urbans i als diferents actors involucrats. Els costos de la distribució urbana resulten una part molt significativa dels costos dels transportistes, especialment a causa de la congestió i la manca de zones de càrrega i descàrrega. Per altre banda, els ciutadans pateixen els efectes de la pol¢lució, el soroll o la competició per l’espai públic. El transport col¢laboratiu és actualment una de les principals tendències de recerca en transport, doncs ofereix beneficis atractius amb poca inversió. Aquesta tesi tracta tres situacions que trobem repetidament en el context urbà, situacions on diverses formes de col¢laboració poden representar una millora, i que consideren tant col¢laboració entre empreses privades com la col·laboració conjunta d’empreses privades amb les administracions. La primera part de la tesi estudia un nivell de col·laboració baix, basat en compartir les zones de càrrega i descàrrega gràcies a un sistema de reserves gestionat per l’administració. En aquest context, sorgeix el Parking Slot Assignment Problem (Problema d’assignació de places de parking), com el problemamatemàtic que assigna transportistes a places de parking satisfent els seus requeriments a través de finestres temporals. En primer lloc proposem un model de factibilitat, i després proposem quatremodelsamb funcions objectius desiguals que penalitzen la desviació de les finestres temporals de formes diferents. Es proposen i comparen dues formulacions: una amb el temps com una variable contínua, i la segona amb discretització temporal. Finalment, s’avaluen i es comparen les diferents propostes a través d’uns extensos experiments computacionals en un conjunt de test basat en dades reals. Un nivell intermedi de col¢laboració entre transportistes s’analitza en la segona part d’aquesta tesi. Les àrees urbanes presenten una alta densitat de clients i és comú trobar clients compartits (és a dir, clients que reben mercaderies a través de diferents transportistes en el mateix interval temporal). Proposem un problema innovador: el Shared Customer Collaboration Vehicle Routing Problem (Problema de rutes de vehicles amb col·laboració de clients compartits), on diferents transportistes estan disposats a col¢laborar transferint part de la demanda dels seus clients compartits, si el cost total de transport es redueix. S’estudien dues formulacions: una basada en els vehicles i una altra basada en la càrrega, i s’experimenta en un conjunt d’instàncies generades. El màxim nivell de col¢laboració en distribució urbana de mercaderies és l’ús de centres de consolidació urbana. Aquests centres estan normalment liderats per l’administració pública però necessiten l’activa col·laboració dels transportistes per aconseguir una implantació amb èxit. Els centres de consolidació urbana són terminals urbanes on es consolida la càrrega dels diferents transportistes i després, un únic transportista neutral realitza la distribució d’última milla. En aquesta tercera part de la tesi proposem models continus que analitzen la millora de l’eficiència en la distribució urbana a través de l’ús de centres de consolidació urbana amb diferents hipòtesis. Els models continus produeixen solucions robustes, que són útils per proporcionar guies en casos genèrics a través de l’anàlisi de sensibilitat. En les tres parts de la tesi es proposen nous enfocs i models que es validen a través d’experiments utilitzant dades obtingudes d’escenaris realsLa distribución urbana de mercancías es una actividad esencial para el desarrollo de las ciudades, aunque al mismo tiempo conlleva diversos problemas en los núcleos urbanos y los distintos actores involucrados. Los costes de la distribución urbana resultan una parte muy significativa de los costes de los transportistas, especialmente a causa de la congestión y la falta de zonas de carga y descarga. Por otro lado, los ciudadanos sufren los efectos de la contaminación, el ruido y la competición por el espacio público. El transporte colaborativo es actualmente una de las principales tendencias en la investigación en transporte, pues ofrece beneficios atractivos con poca inversión. Esta tesis trata tres situaciones que se reproducen repetidamente en el contexto urbano, donde distintas formas de colaboración (tanto entre compañías privadas como con administraciones) pueden representar una mejora. La primera parte de la tesis estudia un nivel de colaboración bajo, basado en compartir las zonas de carga y descarga a través de un sistema de reservas gestionado por la administración. En este contexto surge el Parking Slot Assignment Problem (Problema de asignación de plazas de parking), como el problema matemático que asigna transportistas a plazas de parking satisfaciendo sus requerimientos a través de ventanas temporales. En primer lugar proponemos un modelo de factibilidad, y después cuatro modelos con funciones objetivo que penalizan la desviación de las ventanas temporales de formas distintas. Se proponen y comparan dos formulaciones: una con el tiempo como una variable continua, y la segunda con discretización temporal. Finalmente, se evalúa y compara las distintas propuestas a través de unos extensos experimentos computacionales en un conjunto de test basado en datos reales. Un nivel intermedio de colaboración entre transportistas se analiza en la segunda parte de esta tesis. Las áreas urbanas presentan una alta densidad de clientes, y es común encontrar clientes compartidos (es decir, clientes que reciben mercancías a través de distintos transportistas en el mismo intervalo temporal). Proponemos un problema innovador: el Shared Customer Collaboration Vehicle Routing Problem (Problema de rutas de vehículos con colaboración de clientes compartidos), donde los distintos transportistas están dispuestos a colaborar transfiriendo parte de la demanda de sus clientes compartidos, si el coste total del transporte se reduce. Estudiamos dos formulaciones: una basada en los vehículos y otra basada en la carga, y se experimenta en un conjunto de instancias generadas. El máximo nivel de colaboración en distribución urbana de mercancías es el uso de centros de consolidación urbana. Estos centros, normalmente liderados por la administración pública, necesitan la activa colaboración de los transportistas para conseguir una exitosa implantación. Se trata de terminales urbanas donde se consolida la carga de distintos transportistas y, después, un único transportista neutral realiza la distribución de última milla. En esta tercera parte de la tesis proponemos modelos continuos que analizan la mejora de la eficiencia en la distribución urbana a través del uso de centros de consolidación urbana con distintas hipótesis. Los modelos continuos producen soluciones robustas, que son útiles para proporcionar guías en casos genéricos a través del análisis de sensibilidad. En las tres partes de la tesis se proponen nuevos enfoques y modelos que se validan con experimentos utilizando datos obtenidos en escenarios reale

Enhanced arc-flow formulations to minimize weighted completion time on identical parallel machines

We consider the problem of scheduling a set of jobs on a set of identical parallel machines, with the aim of minimizing the total weighted completion time. The problem has been solved in the literature with a number of mathematical formulations, some of which require the implementation of tailored branch-and-price methods. In our work, we solve the problem instead by means of new arc-flow formulations, by first representing it on a capacitated network and then invoking a mixed integer linear model with a pseudo-polynomial number of variables and constraints. According to our computational tests, existing formulations from the literature can solve to proven optimality benchmark instances with up to 100 jobs, whereas our most performing arc-flow formulation solves all instances with up to 400 jobs and provides very low gap for larger instances with up to 1000 jobs

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de TroyesLe problème de tournées de véhicules (VRP) implique de planifier les itinéraires d'une flotte de véhicules afin de desservir un ensemble de clients à moindre coût. Ce problème d'optimisation combinatoire NP-difficile apparait dans de nombreux domaines d'application, notamment en logistique, télécommunications, robotique ou gestion de crise dans des contextes militaires et humanitaires. Ces applications amènent différents contraintes, objectifs et décisions supplémentaires ; des "attributs" qui viennent compléter les formulations classiques du problème. Les nombreux VRP Multi-Attributs (MAVRP) qui s'ensuivent sont le support d'une littérature considérable, mais qui manque de méthodes généralistes capables de traiter efficacement un éventail significatif de variantes. Par ailleurs, la résolution de problèmes "riches", combinant de nombreux attributs, pose d'importantes difficultés méthodologiques. Cette thèse contribue à relever ces défis par le biais d'analyses structurelles des problèmes, de développements de stratégies métaheuristiques, et de méthodes unifiées. Nous présentons tout d'abord une étude transversale des concepts à succès de 64 méta-heuristiques pour 15 MAVRP afin d'en cerner les "stratégies gagnantes". Puis, nous analysons les problèmes et algorithmes d'ajustement d'horaires en présence d'une séquence de tâches fixée, appelés problèmes de "timing". Ces méthodes, développées indépendamment dans différents domaines de recherche liés au transport, ordonnancement, allocation de ressource et même régression isotonique, sont unifiés dans une revue multidisciplinaire. Un algorithme génétique hybride efficace est ensuite proposé, combinant l'exploration large des méthodes évolutionnaires, les capacités d'amélioration agressive des métaheuristiques à voisinage, et une évaluation bi-critère des solutions considérant coût et contribution à la diversité de la population. Les meilleures solutions connues de la littérature sont retrouvées ou améliorées pour le VRP classique ainsi que des variantes avec multiples dépôts et périodes. La méthode est étendue aux VRP avec contraintes de fenêtres de temps, durée de route, et horaires de conducteurs. Ces applications mettent en jeu de nouvelles méthodes d'évaluation efficaces de contraintes temporelles relaxées, des phases de décomposition, et des recherches arborescentes pour l'insertion des pauses des conducteurs. Un algorithme de gestion implicite du placement des dépôts au cours de recherches locales, par programmation dynamique, est aussi proposé. Des études expérimentales approfondies démontrent la contribution notable des nouvelles stratégies au sein de plusieurs cadres méta-heuristiques. Afin de traiter la variété des attributs, un cadre de résolution heuristique modulaire est présenté ainsi qu'un algorithme génétique hybride unifié (UHGS). Les attributs sont gérés par des composants élémentaires adaptatifs. Des expérimentations sur 26 variantes du VRP et 39 groupes d'instances démontrent la performance remarquable de UHGS qui, avec une unique implémentation et paramétrage, égalise ou surpasse les nombreux algorithmes dédiés, issus de plus de 180 articles, révélant ainsi que la généralité ne s'obtient pas forcément aux dépends de l'efficacité pour cette classe de problèmes. Enfin, pour traiter les problèmes riches, UHGS est étendu au sein d'un cadre de résolution parallèle coopératif à base de décomposition, d'intégration de solutions partielles, et de recherche guidée. L'ensemble de ces travaux permet de jeter un nouveau regard sur les MAVRP et les problèmes de timing, leur résolution par des méthodes méta-heuristiques, ainsi que les méthodes généralistes pour l'optimisation combinatoire.The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Practical routing applications are usually quite distinct from the academic cases, encompassing additional sets of specific constraints, objectives and decisions which breed further new problem variants. The resulting "Multi-Attribute" Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some "rich" VRPs, i.e. those that involve several attributes, may be difficult to address because of the wide array of combined and possibly antagonistic decisions they require. This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The "winning strategies" of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a unifying review. Another analysis is targeted on "timing" problems and algorithms for adjusting the execution dates of a given sequence of tasks. Such methods, independently studied in different research domains related to routing, scheduling, resource allocation, and even isotonic regression are here surveyed in a multidisciplinary review. A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is then introduced, which combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and a bi-criteria evaluation of solutions based on cost and diversity measures. Results of remarkable quality are achieved on classic benchmark instances of the capacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions of the method to VRP variants with constraints on time windows, limited route duration, and truck drivers' statutory pauses are also proposed. New route and neighborhood evaluation procedures are introduced to manage penalized infeasible solutions w.r.t. to time-window and duration constraints. Tree-search procedures are used for drivers' rest scheduling, as well as advanced search limitation strategies, memories and decomposition phases. A dynamic programming-based neighborhood search is introduced to optimally select the depot, vehicle type, and first customer visited in the route during local searches. The notable contribution of these new methodological elements is assessed within two different metaheuristic frameworks. To further advance general-purpose MAVRP methods, we introduce a new component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS), which relies on modular self-adaptive components for addressing problem specifics. Computational experiments demonstrate the groundbreaking performance of UHGS. With a single implementation, unique parameter setting and termination criterion, this algorithm matches or outperforms all current problem-tailored methods from more than 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To address rich problems, UHGS was included in a new parallel cooperative solution framework called "Integrative Cooperative Search (ICS)", based on problem decompositions, partial solutions integration, and global search guidance. This compendium of results provides a novel view on a wide range of MAVRP and timing problems, on efficient heuristic searches, and on general-purpose solution methods for combinatorial optimization problems

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation