Wilson L. Price

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.

Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings

We present a comprehensive model for designing a cellular manufacturing system. The model bridges several known problems in that it integrates the cell formation problem, the machine allocation problem, and the part routing problem. Multiple process plans for each part and multiple routing alternatives for each of those process plans are considered. The part demands can be satisfied from internal production or through outsourcing. Machines have limited capacities. We propose a solution methodology based on a combination of a genetic algorithm and large-scale optimization techniques. A computational study is conducted to evaluate the viability of our approach for solving large scale problems. A limited computational experiment involving smaller problems, that are special cases of our model, is also conducted to compare our solution approach with existing models.

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.

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.

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.

A review of mathematical models in human resource planning

To manage its manpower, the organization must be informed about its internal dynamics and about the dynamics of its environment. This involves the monitoring of internal personnel movements and the analysis of external supplies. The internal situation can largely be controlled through hirings, promotions, internal transfers, redundancies and retirement planning. The problem is precisely to plan and control these interrelated activities in order to achieve a stable organization capable of meeting its objectives. The influence of the environment, through the economic situation, legislation, competition and other factors complicates the problem further. To assist in the planning and control of these activities, the organization can have recourse to models that are either descriptive (Markov chains, renewal models) or normative (linear and goal programming, network methods, stochastic programming). Having reviewed a number of modeling approaches the authors are able to draw certain conclusions as to their applicability for solving various practical problems.

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.