Carole Fayad

A fuzzy genetic algorithm for real-world job shop scheduling

In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the number of tardy jobs. Fuzzy sets are used to represent satisfaction grades for the objectives taking into consideration the preferences of the decision maker. A genetic algorithm is developed to search for the solution with maximum satisfaction grades for the objectives. The developed algorithm is tested on real-world data from a printing company. The experiments include different aggregation operators for combining the objectives.

Sensitivity analysis of a fuzzy multiobjective scheduling problem

This paper concerns sensitivity analysis of a class of complex job shop scheduling problems which are characterized by: (1) a large number of jobs and machines, (2) uncertain jobs processing times, and (3) multiple measures of schedule performance including average weighted tardiness, the number of tardy jobs, the total setup times, the total idle time of machines, and the total flow times of jobs. The base schedule is generated by applying a new fuzzy multiobjective genetic algorithm which takes into consideration batching of the jobs of a similar type, jobs’ lots sizing and load balancing of the machines. The aim of the proposed sensitivity analysis of a generated schedule is to investigate the consequences of prolongations of job processing times on the measures of schedule performance. The processing times are described by triangular fuzzy numbers and their prolongation is done by expanding the supports of fuzzy numbers. The sensitivity analysis is performed through a series of numerical experiments. The effects of prolongations of job processing times on the measures of performance of a generated schedule are recorded and analysed. It is shown that the sensitivity analysis is among the primaries in evaluating the quality of a generated schedule. The sensitivity analysis is used in identifying the critical jobs and the critical machines which have the properties that the prolongations of their processing times produce the largest deteriorations of the performance measures and the overall quality of a generated schedule.

Development of a hybrid crisp-fuzzy logic algorithm optimised by genetic algorithms for path-planning of an autonomous mobile robot

This paper deals with the problem of autonomous mobile robot navigating in unknown environments. The approach developed is reactive in a behaviour-based framework aimed at multiple obstacle avoidance. We use a novel hybrid logic integrating fuzzy and crisp to enable the robot with a human like decision-making ability and avoid failure when world set-ups mismatch with the fuzzy system designer expectancies. Finally, a genetic algorithm is used as means of optimised search within the space of the fuzzy parameters of the hybrid system.

A genetic algorithm for job shop scheduling with load balancing

This paper deals with the load-balancing of machines in a real-world job-shop scheduling problem with identical machines. The load-balancing algorithm allocates jobs, split into lots, on identical machines, with objectives to reduce job total throughput time and to improve machine utilization. A genetic algorithm is developed, whose fitness function evaluates the load-balancing in the generated schedule. This load-balancing algorithm is used within a multi-objective genetic algorithm, which minimizes average tardiness, number of tardy jobs, setup times, idle times of machines and throughput times of jobs. The performance of the algorithm is evaluated using real-world data and compared to the results obtained with no load-balancing.

Solution representation for job shop scheduling problems in ant colony optimisation

Production scheduling problems such as the job shop consist of a collection of operations (grouped into jobs) that must be scheduled for processing on different machines. Typical ant colony optimisation applications for these problems generate solutions by constructing a permutation of the operations, from which a deterministic algorithm can generate the actual schedule. This paper considers an alternative approach in which each machine is assigned a dispatching rule, which heuristically determines the order of operations on that machine. This representation creates a substantially smaller search space that likely contains good solutions. The performance of both approaches is compared on a real-world job shop scheduling problem in which processing times and job due dates are modelled with fuzzy sets. Results indicate that the new approach produces better solutions more quickly than the traditional approach.

The Design and Realisation of a Flexible Rapid Assembly Aero-Structure Manufacturing Cell – A Simulation Driven Approach

The use of simulation is a recognised part of the design process for automated systems and this has been particularly so in the development of the very large machines used in aero-structure manufacture. As part of an ongoing research project at the University of Nottingham a flexible rapid assembly cell is currently being developed that will be capable of manufacturing a number of different aero-structure sub-assemblies. The individual technologies required such as riveting, drilling and assembly have been developed and the complete cell is now being realised. A key enabler for the realisation of the cell has been the use of simulation, both in the development stage and as a central component of the operating and programming systems. This paper will describe the application of simulation techniques within the cell and during its design.