Marc Gravel

Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times

We compare several heuristics for solving a single machine scheduling problem. In the operating situation modelled, setup times are sequence-dependent and the objective is to minimize total tardiness. We describe an Ant Colony Optimization (ACO) algorithm having a new feature using look-ahead information in the transition rule. This feature shows an improvement in performance. A comparison with a genetic algorithm, a simulated annealing approach, a local search method and a branch-and-bound algorithm indicates that the ACO that we describe is competitive and has a certain advantage for larger problems.

Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic

Abstract This paper presents an ant colony optimization metaheuristic for the solution of an industrial scheduling problem in an aluminum casting center. We present an efficient representation of a continuous horizontal casting process which takes account of a number of objectives that are important to the scheduler. We have incorporated the methods proposed in software that has been implemented in the plant.

Solving real car sequencing problems with ant colony optimization

An automobile assembly line is usually configured as three successive shops in which the body is constructed, painted, and then assembled together with all component parts into a finished vehicle. However, many published production sequencing models ignore the first two shops and base their results only on the requirements and constraints of the assembly shop. In this article, we propose to more closely follow the actual industrial structure. We therefore first propose a single objective mathematical model for scheduling the paint and assembly shops. We then propose an ACO metaheuristic for solving a multiple-objective formulation. Data provided by Groupe Renault show that the proposed approach offers better solutions than those of current practice.

Parallel Ant Colony Optimization on Graphics Processing Units

The purpose of this paper is to propose effective parallelization strategies for the Ant Colony Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max-Min Ant System (MMAS) algorithm augmented with 3-opt local search is used as a framework for the implementation of the parallel ants and multiple ant colonies general parallelization approaches. The four resulting GPU algorithms are extensively evaluated and compared on both speedup and solution quality on a state-of-the-art Fermi GPU architecture. A rigorous effort is made to keep parallel algorithms true to the original MMAS applied to the Traveling Salesman Problem. We report speedups of up to 23.60 with solution quality similar to the original sequential implementation. With the intent of providing a parallelization framework for ACO on GPUs, a comparative experimental study highlights the performance impact of ACO parameters, GPU technical configuration, memory structures and parallelization granularity.

Review and comparison of three methods for the solution of the car sequencing problem

The car sequencing problem is the ordering of the production of a list of vehicles which are of the same type, but which may have options or variations that require higher work content and longer operation times for at least one assembly workstation. A feasible production sequence is one that does not schedule vehicles with options in such a way that one or more workstations are overloaded. In variations of the problem, other constraints may apply. We describe and compare three approaches to the modeling and solution of this problem. The first uses integer programming to model and solve the problem. The second approaches the question as a constraint satisfaction problem (CSP). The third method proposes an adaptation of the Ant Colony Optimization for the car sequencing problem. Test-problems are drawn from CSPLib, a publicly available set of problems available through the Internet. We quote results drawn both from our own work and from other research. The literature review is not intended to be exhaustive but we have sought to include representative examples and the more recent work. Our conclusions bear on likely research avenues for the solution of problems of practical size and complexity. A new set of larger benchmark problems was generated and solved. These problems are available to other researchers who may wish to solve them using their own methods.

Using the Kanban in a job shop environment

Japanese industrial management techniques have been applied in a large number of large Western enterprises. In particular, the Kanban method has been used to control materials, production rate and volume, and to adjust production to requirements. The authors show how the Kanban method may be adapted to the job shop. This adaptation was extensively tested through simulation and then implemented. Actual performance is consistent with the simulation results, and shows marked improvement over previous practice.

A review of optimisation models of Kanban-based production systems

Abstract This paper deals with the Kanban method of production control used initially by Toyota Motors to replace traditional reorder point and economic lot size techniques. The method has had considerable practical success and various researchers have developed models to help production planners to choose various parameters associated with the method, principally the number of Kanban cards at a workstation and the size of the Kanban-lots. This paper reviews optimisation models of Kanban-based systems covering serial production lines, bottleneck workstations, assembly job-shop production.

Efficient solutions to the cell-formation problem with multiple routings via a double-loop genetic algorithm

We present a genetic approach for finding efficient solutions to the problem of forming manufacturing cells for products having multiple routings. We consider the case where there are two criteria. The method that we propose seeks to generate the efficient set of solutions, that is the set of non-dominated solutions. The manager may then choose a solution knowing the consequences for each of the objectives. We address the computational difficulty of this problem and present a numerical example.

Scheduling jobs in an Alcan aluminium foundry using a genetic algorithm

We present a genetic algorithm for the solution of an industrial scheduling problem in an Alcan aluminium foundry situated in Québec. We seek the best processing sequence for n orders on a m parallel machines. The set-up times are sequence dependent and we must deal with multiple criteria. There are also a number of structural constraints that distinguish this situation from the classical model. The performance of the solution approach is compared with the results of the scheduling process used by the firm according to three criteria: meeting due dates, number and duration of required set-ups and metal flow.

A multicriterion view of optimal resource allocation in job-shop production

Abstract This paper describes the use of the Kanban method for the control of production in a job-shop in order to reduce makespan, reduce work-in-progress, improve machine utilisation, and to control the number of machine setups. A knowledge base has been constructed from a large number of simulations and allows the analysis of the behaviour of the production system under different conditions. The choice of appropriate values for the parameters that control production is made via a multi-criteria outranking method based on stochastic dominance.