Applying genetic algorithm for hybrid job shop scheduling in a cosmetic industry

This work considers the problem of scheduling a given set of jobs in a Flexible Job Shop in a cosmetic industry, located in Colombia, taking into account the natural complexity of the process and a lot of amount of variables involved, this problem is considered as NP-hard in the strong sense. Therefore, it is possible to find and optimal solution in a reasonable computational time for only small instances, which in general, does not reflect the industrial reality. For that reason, it is proposed the use of metaheuristics as an alternative approach in order to determine, with a low computational effort, the best assignment of jobs in order to minimize the number of tardy jobs. This optimization objective will allow to company to improve their customer service. A Genetic Algorithm (GA) is proposed. Computational experiments are carried out comparing the proposed approach versus instances of literature by Chiang and Fu. Results show the efficiency of our GA Algorithm

Producción

La expansión de la dinámica de mercados y el crecimiento de la necesidad del sector productivo por satisfacer una demanda cada vez más exigente de productos y servicios, trae consigo retos para el ingeniero industrial que incluyen la optimización del uso del tiempo y los recursos y el aumento de la productividad y competitividad en las organizaciones. La Planeación, Programación y Control de la Producción son pilares importantes sobre los cuales se apoya el perfil académico y profesional de un Ingeniero Industrial. A través de la asignatura de Producción se busca propiciar el desarrollo de competencias para la planificación, mejoramiento y toma de decisiones en entornos productivos

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

Scheduling flexible flowshops with sequence -dependent setup times

This dissertation addresses the scheduling problem in a flexible flowshop with sequence-dependent setup times. The production line consists of S production stages, each of which may have more than one non-identical (uniform) machines. Prior to processing a job on a machine at the first stage, a setup time from idling is needed. Also sequence dependent setup times (SDST) are considered on each machine in each stage. The objective of this research is to minimize the makespan. A mathematical model was developed for small size problems and two heuristic algorithms (Flexible Flowshop with Sequence Dependent Setup Times Heuristic (FFSDSTH) and Tabu Search Heuristic (TSH)) were developed to solve larger, more practical problems. The FFSDSTH algorithm was developed to obtain a good initial solution which can then be improved by the TSH algorithm. The TSH algorithm uses the well-known Tabu Search metaheuristic. In order to evaluate the performance of the heuristics, two lower bounds (Forward and Backward) were developed. The machine waiting time, idle time, and total setup and processing times on machines at the last stage were used to calculate the lower bound. Computational experiments were performed with the application of the heuristic algorithms and the lower bound methods. Two quantities were measured: (1) the performance of the heuristic algorithms obtained by comparing solutions with the lower bounds and (2) the relative improvement realized with the application of the TSH algorithm to the results obtained with the FFSDSTH algorithm. The performance of the heuristics was evaluated using two measures: solution quality and computational time. Results obtained show that the heuristic algorithms are quite efficient. The relative improvement yielded by the TSH algorithm was between 2.95 and 11.85 percent

A Preventive Maintenance Framework in Dairy Production Operations

Dairy operations suffer frequent stops. Product shrinkage is a consequence of downtime, which includes losses of packaging material, scraped finish product and capacity. This work proposes a troubleshooting methodology to identify causes of downtime, estimation of waste cost, and minimization of operation disruptions by applying a combination of a cost function to assess waste, and performance measurements. The drinkable yogurt process is evaluated to find the principal areas for wasted bottles and yogurt. In order to make a decision about which of those sources to address, a General Cost Function is used to estimate waste cost which include measurements that evaluate the entire process. Further performance measure analysis such as Squared Coefficient of Variation, Utilization, etc. indicated the necessary maintenance strategy to normalize the process. After the root cause of shrink was found, improvements were implemented and the performance of the station was assessed again to confirm results

An investigation of changeover sensitive heuristics in an industrial job shop environment

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SIMAID: a rapid development methodology for the design of acyclic, bufferless, multi-process and mixed model agile production facilities for spaceframe vehicles

The facility layout problem (FL) is a non-linear, NP-complete problem whose complexity is derived from the vast solution space generated by multiple variables and interdependent factors. For reconfigurable, agile facilities the problem is compounded by parallelism (simultaneity of operations) and scheduling issues. Previous work has either concentrated on conventional (linear or branched) facility layout design, or has not considered the issues of agile, reconfigurable facilities and scheduling. This work is the first comprehensive methodology incorporating the design and scheduling of parallel cellular facilities for the purpose of easy and rapid reconfiguration in the increasingly demanding world of agile manufacturing. A novel three-stage algorithm is described for the design of acyclic (asynchronous), bufferless, parallel, multi-process and mixed-model production facilities for spaceframe-based vehicles. Data input begins with vehicle part processing and volume requirements from multiple models and includes time, budget and space constraints. The algorithm consists of a powerful combination of a guided cell formation stage, iterative solution improvement searches and design stage scheduling. The improvement iterations utilise a modified (rules-based) Tabu search applied to a constant-flow group technology, while the design stage scheduling is done by the use of genetic algorithms. The objective-based solution optimisation direction is not random but guided, based on measurement criteria from simulation. The end product is the selection and graphic presentation of the best solution out of a database of feasible ones. The case is presented in the form of an executable program and three real world industrial examples are included. The results provide evidence that good solutions can be found to this new type and size of heavily constrained problem within a reasonable amount of time