Solving Multi-stage, Multi-machine, Multi-product Scheduling Problems (original) (raw)
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Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.
2010
In this paper, an Enhanced Single Objective Genetic Algorithm Scheduling Tool (ESOGAST) is presented that is capable of solving very large scheduling problems such as those encountered in the capital goods industry. The tool minimises the penalties caused by the early or late delivery of components, assemblies and final products. The tool is optimised for speed; it runs more than 5000 times faster than the tool developed by Pongcharoen et al [1]. It is therefore capable of solving much larger problems within a reasonable amount of time. The ESOGAST includes an enhanced repair process to optimise the performance of the tool for scheduling the production of very complex products with many levels of product structure under finite capacity conditions. This paper describes the Genetic Algorithm and the data structures used in detail. A case study that used data obtained from a collaborating capital goods company is presented. A series of experiments that were used to identify the best co...
International Journal of Production Economics, 2002
A Genetic Algorithm-based Scheduling Tool (GAST) has been developed for the scheduling of complex products with multiple resource constraints and deep product structure. This includes a repair process that identifies and corrects infeasible schedules. The algorithm takes account of the requirement to minimise the penalties due to both the early supply of components and assemblies and the late delivery of final products, whilst simultaneously considering capacity utilisation. The research has used manufacturing data obtained from a capital goods company. The Genetic Algorithm scheduling method produces significantly better delivery performance and resource utilisation than the Company plans. Genetic Algorithm programs include a number of parameters including the probabilities of crossover and mutation, the population size and the number of generations. A factorial experiment has been performed to identify appropriate values for these factors that produce the best results within a given execution time. The overall objective is to use the most efficient Genetic Algorithm parameters that achieve minimum total costs and minimum spread, to solve a very large scheduling problem that is computationally expensive. The results are compared to the corresponding plans produced by the collaborating company using simulation. It is demonstrated that in the case considered, the Genetic Algorithm scheduling method achieves on time delivery and a 63% reduction in costs.
Journal of Applied Statistics, 2001
Conventional optimization approaches, such as Linear Programming, Dynamic Programming and Branch-and-Bound methods are well established for solving relatively simple scheduling problems. Algorithms such as Simulated Annealing, Taboo Search and Genetic Algorithms (GA) have recently been applied to large combinatorial problems. Owing to the complex nature of these problems it is often impossible to search the whole problem space and an optimal solution cannot, therefore, be guaranteed. A Bi-Criteria Genetic Algorithm (BCGA) has been developed for the scheduling of complex products with multiple resource constraints and deep product structure. This GA identi® es and corrects infeasible schedules and takes account of the early supply of components and assemblies, late delivery of ® nal products and capacity utilization. The research has used manufacturing data obtained from a capital goods company. Genetic Algorithms include a number of parameters, including the probabilities of crossover and mutation, the population size and the number of generations. The BCGA scheduling tool provides 16 alternative crossover operations and eight diþ erent mutation mechanisms. The overall objective of this study was to develop an eý cient design-of-experiments approach to identify genetic algorithm operators and parameters that produce solutions with minimum total cost. The case studies were based upon a complex, computationally intensive scheduling problem that was insoluble using conventional approaches. This paper describes an eý cient sequential experimental strategy that enabled this work to be performed within a reasonable time. The ® rst stage was a screening experiment, which had a fractional factorial embedded
European Journal of Operational Research, 2004
In this paper, the development of a genetic algorithms based scheduling tool that takes into account multiple resource constraints and multiple levels of product structure is described. The genetic algorithms includes a repair process that rectifies infeasible chromosomes that may be produced during evolution process. The algorithm includes problem encoding, chromosome representation and initialisation, genetic operation, repair process, fitness measurement and chromosome selection. The data structure and algorithm are detailed step by step. The tool generates schedules that minimises the penalties caused by early and late delivery of for components, assemblies and final products. The method is applied using data obtained from a collaborating company that manufactures complex capital goods. It is demonstrated that the schedules produced perform significantly better than those produced by the company using a conventional planning method.
Computers & Industrial Engineering, 2006
An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order's production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed-and multi-product assembly environment.
13 -118 Using Genetic Algorithms for Production Scheduling
Scheduling is an important tool in the manufacturing area since productivity is inherently linked to how well the resources are used to increase efficiency and reduce waste. This paper presents the implementation of a genetic algorithm in manufacturing. The genetic algorithm's goal is to obtain a detailed plan that represents the tasks' order and the completion time for each resource. The application aim is the identification of the best allocation of resources that minimizes the makespan for a predetermined quantity of products, considering the system restrictions.
Integrated planning and scheduling for multi-product job-shop assembly based on genetic algorithms
1996
This paper introduces a simultaneous approach for dealing with assembly sequence planning and scheduling, which have been dealt with separately in research. Assembly planning consists of finding the optimal or best sequence to assemble a certain product, according to some product criteria. Assembly scheduling is concerned with finding the optimal or best schedule to perform the assembly operations by a given number of machines, according to some system criteria such as time-in-process(TIP) and idle time. However, a best sequence necessarily leads to an efficient operations schedule, and a best schedule might alter the feasibility precedence constraints of the assembly sequence. These cases can be best encountered in multi-product job-shop assembly. This paper introduces a genetic algorithm approach to integrate the two procedures together. A prototype example is solved to illustrate the new approach.
Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling
Strojniški vestnik – Journal of Mechanical Engineering, 2011
Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout.