Scheduling Mixed-Model Production on Multiple Assembly Lines with Shared Resources Using Genetic Algorithms: The Case Study of a Motorbike Company (original) (raw)
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Scheduling mixed-model assembly lines with genetic algorithms: the Aprilia case study
2012
The two authors deal with the topic of the final assembly scheduling realized by the use of Genetic Algorithms (GAs). The objective of the research was to study in depth the use of GA for scheduling mixed-model assembly lines and to propose a model able to produce feasible solutions according to the particular requirements of an important Italian motorbike company, the Aprilia group, as well as to capture the results of this change in terms of better operational performances. In the Aprilia case study, the scheduling problem is made more complex by the “chessboard shifting” of work teams. Therefore, a complex model for scheduling mixed-model assembly lines is required. An application of the GAs is proposed in order to test their effectiveness and robustness. The short elaboration time and the robustness of the final assembly plans, obtained during the test-stage, confirm that the choice was right and suggest the use of GAs in other complex manufacturing systems.
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.
Costantino De Toni Di Gravio Nonino Genetic Algorithms ADS
The authors deal with the topic of the final assembly scheduling realized by the use of genetic algorithms (GAs). The objective of the research was to study in depth the use of GA for scheduling mixed-model assembly lines and to propose a model able to produce feasible solutions also according to the particular requirements of an important Italian motorbike company, as well as to capture the results of this change in terms of better operational performances. The "chessboard shifting" of work teams among the mixed-model assembly lines of the selected company makes the scheduling problem more complex. Therefore, a complex model for scheduling is required. We propose an application of the GAs in order to test their effectiveness to real scheduling problems. The high quality of the final assembly plans with high adherence to the delivery date, obtained in a short elaboration time, confirms that the choice was right and suggests the use of GAs in other complex manufacturing systems.
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.
2004
In this paper we present an optimization approach to a real-world production planning problem. Based on raw data that have been extracted from the Production, Planning and Control System of a company which produces special purpose vehicles and equipment, we have developed an architecture of an optimization system for production planning and scheduling in the manufacturing line of this company. The paper itself is divided into two major parts. The first part mainly deals with the theoretical background of production planning problems. In the second part of the paper we give an overview of the concrete scenario which is the subject of our research. Based on these fundamentals, we describe our approach to the problem, the modelling process and the architecture of the optimization system we plan to implement.
American Journal of Industrial and Business Management, 2016
Assembly line balancing is a key for organizational productivity in terms of reduced number of workstations for a given production volume per shift. Mixed-model assembly line balancing is a reality in many organizations. The mixed-model assembly line balancing problem comes under combinatorial category. So, in this paper, an attempt has been made to develop three genetic algorithms for the mixed-model assembly line balancing problem such that the combined balancing efficiency is maximized, where the combined balancing efficiency is the average of the balancing efficiencies of the individual models. At the end, these three algorithms and another algorithm in literature are compared in terms of balancing efficiency using a randomly generated set of problems through a complete factorial experiment, in which "Algorithm", "Problem Size" and "Cycle Time" are used as factors with two replications under each of the experimental combinations to draw inferences and to identify the best of the four algorithms. Then, through another set of randomly generated small and medium size data, the results of the best algorithm are compared with the optimal results obtained using a mathematical model. It is found that best algorithm gives the optimal solution for all the problems in the second set of data, except for one problem which cannot be solved using the model. This observation supports the fact that the best algorithm identified in this paper gives superior results.
Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm
2008
This paper is concerned about how to optimize the input sequence for a mixed-model assembly line (MMAL) with limited intermediate buffers. Three optimization objectives are considered simultaneously: minimizing the total production rate variation, the total setup, and the total assembly cost. The mathematical model is presented by incorporating the three objectives. Since the problem is NP-hard, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA), is proposed for solving the model. The performance of the proposed algorithm is compared with a genetic algorithm for different-sized sequencing problems in MMALs that consist of different number of machines and different production plans. The computational results show that the proposed hybrid algorithm finds solutions with better quality and often needs a smaller number of generations to converge to a final stable state, especially in the case of large-sized problems.
GA based static scheduling of multilevel assembly job shops
International Journal of …, 2009
This research focuses on scheduling operations of multi-level jobs, which undergo serial, parallel, and assembly operations in static assembly job shops with an objective of minimising makespan. A new optimisation heuristic based on Genetic Algorithm (GA) is proposed. Its performance is compared with some of the dispatch rules, which have best performances in scheduling multilevel jobs in dynamic assembly job shop. A simulation of assembly job shop is developed and integrated with a GA heuristic routine and analysed. It is found that the proposed algorithm performs well with respect to minimising the makespan compared to other dispatch rules.
Using genetic algorithms for dynamic scheduling
In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration and revision of pre-established schedules. Scheduling algorithms that achieve good or near optimal solutions and can efficiently adapt them to perturbations are, in most cases, preferable to those that achieve optimal ones but that cannot implement such an adaptation. This reality, motivated us to concentrate on tools, which could deal with such dynamic, disturbed scheduling problems, both for single and multi-machine manufacturing settings, even though, due to the complexity of these problems, optimal solutions may not be possible to find. We decided to address the problem drawing upon the potential of Genetic Algorithms to deal with such complex situations.
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.