A Two-Warehouse Model for Deteriorating Items with Holding Cost under Particle Swarm Optimization

A deterministic inventory model has been developed for deteriorating items and Particle Swarm Optimization (PSO) having a ramp type demands with the effects of inflation with two-warehouse facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Particle Swarm Optimization (PSO) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Particle Swarm Optimization (PSO). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters

A SURVEY ON MACHINE SCHEDULING TECHNIQUES

ABSTRACT In this paper the study about the different methodologies and techniques implemented for different types of scheduling problems in single machine, job shop and flow shop scheduling. Every author tells about the different scenario and approach to minimize the Make span, Tardiness and different parameters in scheduling. Every author implements their own algorithms and the strategies to find out the result, it may be positive or negative. This paper gives the clear idea for the future research work

A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems

Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems (JSP) is proposed. Design/methodology/approach: In the algorithm, a number of sub-problems are constructed by iteratively decomposing the large-scale JSP according to the process route of each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality, a detection method for multi-bottleneck machines based on critical path is proposed. Therewith the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck operations. According to the principle of “Bottleneck leads the performance of the whole manufacturing system” in TOC (Theory Of Constraints), the bottleneck operations are scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are scheduled by dispatching rules for the improvement of the solving efficiency. Findings: In the process of the subproblems' construction, partial operations in the previous scheduled sub-problem are divided into the successive sub-problem for re-optimization. This strategy can improve the solution quality of the algorithm. In the process of solving the sub problems, the strategy that evaluating the chromosome's fitness by predicting the global scheduling objective value can improve the solution quality. Research limitations/implications: In this research, there are some assumptions which reduce the complexity of the large-scale scheduling problem. They are as follows: The processing route of each job is predetermined, and the processing time of each operation is fixed. There is no machine breakdown, and no preemption of the operations is allowed. The assumptions should be considered if the algorithm is used in the actual job shop. Originality/value: The research provides an efficient scheduling method for the large-scale job shops, and will be helpful for the discrete manufacturing industry for improving the production efficiency and effectiveness.Peer Reviewe

A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems

Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems (JSP) is proposed. Design/methodology/approach: In the algorithm, a number of sub-problems are constructed by iteratively decomposing the large-scale JSP according to the process route of each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality, a detection method for multi-bottleneck machines based on critical path is proposed. Therewith the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck operations. According to the principle of “Bottleneck leads the performance of the whole manufacturing system” in TOC (Theory Of Constraints), the bottleneck operations are scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are scheduled by dispatching rules for the improvement of the solving efficiency. Findings: In the process of the subproblems' construction, partial operations in the previous scheduled sub-problem are divided into the successive sub-problem for re-optimization. This strategy can improve the solution quality of the algorithm. In the process of solving the sub problems, the strategy that evaluating the chromosome's fitness by predicting the global scheduling objective value can improve the solution quality. Research limitations/implications: In this research, there are some assumptions which reduce the complexity of the large-scale scheduling problem. They are as follows: The processing route of each job is predetermined, and the processing time of each operation is fixed. There is no machine breakdown, and no preemption of the operations is allowed. The assumptions should be considered if the algorithm is used in the actual job shop. Originality/value: The research provides an efficient scheduling method for the large-scale job shops, and will be helpful for the discrete manufacturing industry for improving the production efficiency and effectiveness.Peer Reviewe

A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry

Production planners usually aim to satisfy multiple objectives. This paper describes the development of a genetic algorithm tool that finds optimum trade-offs among delivery performance, resource utilisation, and workin-progress inventory. The tool was specifically developed to meet the requirements of capital goods companies that manufacture products with deep and complex product structures with components that have long and complicated routings. The model takes into account operation and assembly precedence relationships and finite capacity constraints. The tool was tested using various production problems that were obtained from a collaborating company. A series of experiments showed the tool provides a set of non-dominated solutions that enable the planner to choose an optimum trade-off according to their preferences. Previous research had optimised a single objective function. This is the first scheduling tool of its type that has simultaneously optimised delivery performance, resource utilisation and work-in-progress inventory. The quality of the schedules produced was significantly better than the approaches used by the collaborating company

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approache

Implementación de un híbrido entre Grasp y Tabú search para la solución del problema de programación de la producción en un ambiente Job Shop para la minimización de la tardanza total ponderada

El presente trabajo resuelve un Job shop para la minimización de la tardanza total ponderada ya que ésta es una medida de desempeño que tiene en cuenta no solo el nivel de cumplimiento de los clientes sino la importancia de los mismos. Como método de solución se propone un algoritmo híbrido entre la metodología GRASP la cual no ha sido muy estudiada para la solución de éste problema (y es de gran ayuda para la construcción inicial de una solución), y la búsqueda tabú (con la cual se han obtenido muy buenos resultados para Job Shop) para la fase de búsqueda local del algoritmo. Los resultados obtenidos se comparan con el algoritmo de búsqueda local genética propuesto por (Essafi, Mati, - Dauzère-Pérès, 2008). Éste documento presenta inicialmente el planteamiento de problema y la justificación del mismo, seguido por una explicación del problema, la meta heurística realizada, y los antecedentes relacionados con investigación del problema y métodos de solución propuestos para éste. Posteriormente se plantean los objetivos y alcance del documento, junto con el desarrollo, análisis de resultados del mismo, y finalmente algunas recomendaciones para futuros trabajos.This paper addresses a job shop problem minimizing the total weighted tardiness as this is a performance measure that takes into account, not only the level of compliance with the customers but the importance of them. The paper proposes a hybrid solution method algorithm between the GRASP methodology which has not been studied for the solution of this problem (and it is helpful for the initial construction of a solution), and Tabu search (which have obtained very good results for Job Shop) for the local search phase of the algorithm. The results obtained are compared with the local search algorithm proposed by genetic (Essafi Mati, - DauzèrePeres, 2008). This paper first presents the problem approach and its justification, followed by an explanation of the problem, metaheuristics used, relating literature to research the problem and proposed methods of solution for this. Then the objectives and scope of the document, along with the development, analysis of results, and finally some recommendations for future work are suggested.Ingeniero (a) IndustrialPregrad

Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica

El objeto de estudio de la presente tesis es el taller de trabajo flexible en el sector metalmecánico. El problema de investigación se derivó a partir de la búsqueda sistemática de metodologías y algoritmos para programar sistemas productivos; se identificaron configuraciones de variables de proceso no abordadas en la literatura, lo que se considera un vacío en el conocimiento. Consecuente con lo anterior, se diseñó una metodología basada en un algoritmo evolutivo para programar los pedidos en un taller de trabajo flexible, con restricciones de tiempo, secuencia, mantenimiento, liberación de pedidos, disponibilidad, consumo y costo de recurso que varía en el tiempo, con el fin de minimizar tiempo de proceso y costo de producción; incluyó un proceso de ponderación para escoger la mejor secuencia de programación. Como aporte principal se propone una metodología novedosa que al compararla con otras metodologías encontradas en la bibliografía, demostró mejoras mayores al 10% en makespan y costo total del recurso consumidoAbstract: The study object of the present thesis is the flexible job shop in the metal mechanic sector. The research problem was derived from the systematic search of methodologies and algorithms to schedule production systems; configurations of process variables not addressed in the literature were identified, which is considered an empty in knowledge. Consequent with previous, a methodology was designed based on an evolutionary algorithm to schedule orders in a flexible job shop, with time restrictions, sequence, maintenance, liberation of orders, availability, consumption and cost of resource that varies in time, in order to minimize processing time and cost of production; it includes a weighting process to choose the best programming sequence. As main contribution a novel methodology was proposed which, compared with other methodologies found in the literature, it demonstrated greater improvements to 10% in Makespan and total cost of consumed resourceDoctorad