Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm

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

Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations

This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

Blockchain-based life cycle assessment: An implementation framework and system architecture

Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications

Genetic programming for manufacturing optimisation.

A considerable number of optimisation techniques have been proposed for the solution of problems associated with the manufacturing process. Evolutionary computation methods, a group of non-deterministic search algorithms that employ the concept of Darwinian strife for survival to guide the search for optimal solutions, have been extensively used for this purpose. Genetic programming is an evolutionary algorithm that evolves variable-length solution representations in the form of computer programs. While genetic programming has produced successful applications in a variety of optimisation fields, genetic programming methodologies for the solution of manufacturing optimisation problems have rarely been reported. The applicability of genetic programming in the field of manufacturing optimisation is investigated in this thesis. Three well-known problems were used for this purpose: the one-machine total tardiness problem, the cell-formation problem and the multiobjective process planning selection problem. The main contribution of this thesis is the introduction of novel genetic programming frameworks for the solution of these problems. In the case of the one-machine total tardiness problem genetic programming employed combinations of dispatching rules for the indirect representation of job schedules. The hybridisation of genetic programming with alternative search algorithms was proposed for the solution of more difficult problem instances. In addition, genetic programming was used for the evolution of new dispatching rules that challenged the efficiency of man-made dispatching rules for the solution of the problem. An integrated genetic programming - hierarchical clustering approach was proposed for the solution of simple and advanced formulations of the cell-formation problem. The proposed framework produced competitive results to alternative methodologies that have been proposed for the solution of the same problem. The evolution of similarity coefficients that can be used in combination with clustering techniques for the solution of cell-formation problems was also investigated. Finally, genetic programming was combined with a number of evolutionary multiobjective techniques for the solution of the multiobjective process planning selection problem. Results on test problems illustrated the ability of the proposed methodology to provide a wealth of potential solutions to the decision-maker

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Scheduling of serial multiproduct batch processes

Master'sMASTER OF ENGINEERIN

Production planning mechanisms in demand-driven wood remanufacturing industry

L'objectif principal de cette thèse est d'étudier le problème de planification de la production dans le contexte d'une demande incertaine, d’un niveau de service variable et d’approvisionnements incontrôlables dans une usine de seconde transformation du bois. Les activités de planification et de contrôle de production sont des tâches intrinsèquement complexes et difficiles pour les entreprises de seconde transformation du bois. La complexité vient de certaines caractéristiques intrinsèques de cette industrie, comme la co-production, les procédés alternatifs divergents, les systèmes de production sur commande (make-to-order), des temps de setup variables et une offre incontrôlable. La première partie de cette thèse propose une plate-forme d'optimisation/simulation permettant de prendre des décisions concernant le choix d'une politique de planification de la production, pour traiter rapidement les demandes incertaines, tout en tenant compte des caractéristiques complexes de l'industrie de la seconde transformation du bois. À cet effet, une stratégie de re-planification périodique basée sur un horizon roulant est utilisée et validée par un modèle de simulation utilisant des données réelles provenant d'un partenaire industriel. Dans la deuxième partie de cette thèse, une méthode de gestion des stocks de sécurité dynamique est proposée afin de mieux gérer le niveau de service, qui est contraint par une capacité de production limitée et à la complexité de la gestion des temps de mise en course. Nous avons ainsi développé une approche de re-planification périodique à deux phases, dans laquelle des capacités non-utilisées (dans la première phase) sont attribuées (dans la seconde phase) afin de produire certains produits jugés importants, augmentant ainsi la capacité du système à atteindre le niveau de stock de sécurité. Enfin, dans la troisième partie de la thèse, nous étudions l’impact d’un approvisionnement incontrôlable sur la planification de la production. Différents scénarios d'approvisionnement servent à identifier les seuils critiques dans les variations de l’offre. Le cadre proposé permet aux gestionnaires de comprendre l'impact de politiques d'approvisionnement proposées pour faire face aux incertitudes. Les résultats obtenus à travers les études de cas considérés montrent que les nouvelles approches proposées dans cette thèse constituent des outils pratiques et efficaces pour la planification de production du bois.The main objective of this thesis is to investigate the production planning problem in the context of uncertain demand, variable service level, and uncontrollable supply in a wood remanufacturing mill. Production planning and control activities are complex and represent difficult tasks for wood remanufacturers. The complexity comes from inherent characteristics of the industry such as divergent co-production, alternative processes, make-to-order, short customer lead times, variable setup time, and uncontrollable supply. The first part of this thesis proposes an optimization/simulation platform to make decisions about the selection of a production planning policy to deal swiftly with uncertain demands, under the complex characteristics of the wood remanufacturing industry. For this purpose, a periodic re-planning strategy based on a rolling horizon was used and validated through a simulation model using real data from an industrial partner. The computational results highlighted the significance of using the re-planning model as a practical tool for production planning under unstable demands. In the second part, a dynamic safety stock method was proposed to better manage service level, which was threatened by issues related to limited production capacity and the complexity of setup time. We developed a two-phase periodic re-planning approach whereby idle capacities were allocated to produce more important products thus increasing the realization of safety stock level. Numerical results indicated that the solution of the two-phase method was superior to the initial method in terms of backorder level as well as inventory level. Finally, we studied the impact of uncontrollable supply on demand-driven wood remanufacturing production planning through an optimization and simulation framework. Different supply scenarios were used to identify the safety threshold of supply changes. The proposed framework provided managers with a novel advanced planning approach that allowed understanding the impact of supply policies to deal with uncertainties. In general, the wood products industry offers a rich environment for dealing with uncertainties for which the literature fails to provide efficient solutions. Regarding the results that were obtained through the case studies, we believe that approaches proposed in this thesis can be considered as novel and practical tools for wood remanufacturing production planning