Alain Delchambre

A genetic algorithm for bin packing and line balancing

The authors present an efficient genetic algorithm for two NP-hard problems, the bin packing and the line balancing problems. They define the two problems precisely and specify a cost function suitable for the bin packing problem. It is shown that the classic genetic algorithm performs poorly on grouping problems and an encoding of solutions of fitting these problems is presented. Efficient crossover and mutation operators are introduced for bin packing. The modification necessary to fit these operators for line balancing is given. Results of performance tests on randomly generated data are included. The line balancing tests cover real-world problem sizes. The results and areas of further research are discussed.<<ETX>>

State of art of optimization methods for assembly line design

The problem of optimal design of the assembly lines is considered. The paper is especially focused on the line balancing and resource planning step for the preliminary design stage. A survey of existing methods is given.

Computer-aided Assembly Planning

The assembly sector is one of the least automated in the manufacturing industry. Automation is essential if industrial companies are to be competitive in the future. In assembly, an integrated and flexible approach is needed because 75% of the applications are produced in small and medium batches. The methodologies developed in this book deal with the integration of the assembly process from the initial design of the product to its production. In such an integrated system, assembly planning is one of the most important features. A well-chosen assembly plan will reduce both the number of tool changes and the fixtures within the assembly cell. It will prevent the handling of unstable subassemblies, simplify the design of the robot grippers and reduce production costs. An automatic generator of assembly sequences can be an efficient aid to the designer. Whenever he or she modifies features of the product, the influence of these modifications can immediately be checked on the sequences. For small batch production, the automatic generation of assembly sequences is faster, more reliable and more cost-effective than manual generation. By using this latter method interesting sequences could be missed because of the combinatorial explosion of solutions. The main subjects treated in this book are as follows. 1. Presentation and classification of existing systems of automatic generation of assembly sequences. Automatic assembly planning is, indeed, a very recent research area and, in my experience, no systematic study has been carried out up to now.

Designing mixed-product assembly lines

The purpose of this paper is to describe the main problems concerning the design and operation of mixed-product assembly lines. We will focus on the design of assembly lines, which is most of the time composed in three steps. The first is the elaboration of the logical layout of the line, which consists in the distribution of operations among workstations along the line. The second is the choice of the most efficient product mix and production sequence in relation to the operation phase. The third is the subsequent elaboration of the physical layout of the line, i.e., deciding about the disposition of the workstations, conveyor(s), possible buffers, etc., on the shop floor. In this paper, the accent will be put exclusively on the first two steps. The concept of balance for ordering (BFO) is introduced to solve these two interrelated problems. After an overview of the current work in this area, this paper presents in detail a very promising approach to solve the BFO: the grouping and ordering genetic algorithms (genetic algorithms heavily modified to solve grouping/ordering problems). In all cases, the main concern is the quality of the resulting line in terms of cost and its suitability to the line designers needs (special user preferences are followed as closely as possible). Academic and experimental results are presented, and they confirm the efficiency of the proposed approaches.

Handicapped Person Transportation: An application of the Grouping Genetic Algorithm

Abstract An effective method based on the Genetic Algorithms is proposed to solve the Handicapped Person Transportation problem, which is a real-life application for pickup and delivery problems. In these problems, vehicles have to transport (clients, loads, etc.,) from their locations to different destinations (hospitals, shop centres, etc.). The objective of this paper is to implement Grouping Genetic Algorithm to find optimal (or close to optimal) routes for transporting handicapped people in terms of service quality and number of used vehicles. This algorithm is a stochastic search method based on randomized operators for combining solutions and producing better ones. The proposed algorithm has been applied on the handicapped persons transportation problem in the city of Brussels, Belgium. The obtained results are better than the manually generated solutions in terms of service quality and computational effort.

A multiple objective grouping genetic algorithm for assembly line design

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with users preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.

Grouping genetic algorithms: an efficient method to solve the cell formation problem

The layout problem arises in a production plant during the study of a new production system, but also during a possible restructuring. The main aim of layout design is to reduce transportation and maintenance, which simplifies management, shortens lead time, improves product quality and speeds up the response to market fluctuations. A principle of Group Technology (GT) advocates the division of a unity into small groups or cells. As it is most of the time impossible to design totally independent cells, the problem is to minimise traffic of items between the cells, for a fixed maximum cell size. This problem is known as cell formation problem (CFP). We propose here an original approach to solve this NP-hard problem. It is based on a Grouping Genetic Algorithm (GGA), a special class of genetic algorithms, heavily modified to suit the structure of grouping problems. The crucial advantage of this GGA is that it is able to deal with large instances of the problem thus becoming a powerful tool for an engineer determining a plant layout, allowing him or her to try several plant options, without the limitation of huge computation times.

Comparison between Two Capillary Forces Models

Surface tension effects are dominant in miniaturization. Therefore, a lot of capillary forces models have been recently discussed in the literature. The work reported in this paper intends to prove the equivalence between two methods which are very widespread in capillary forces computation at equilibrium: the energetic method based on the derivation of the total interfacial energy and a second method summing both pressure and tension terms obtained from the meniscus profile (based on the Laplace equation). The results are supported by different qualitative arguments, an analytical proof in the case of a prism-plate configuration, numerical simulation, and experiments in the case of two millimetric spheres.

Temperature monitoring of nuclear reactor cores with multiplexed fiber Bragg grating sensors

In-core temperature measurement is a critical issue for the safe operation of nuclear reactors. Classical thermocouples require shielded connections and are known to drift under high neutron fluence. As an alternative, we propose to take advantage of the multiplexing ca- pabilities of fiber Bragg grating (FBG) temperature sensors. Our experi- ments show that sensitivity to radiation depends on both the radiation field and the grating characteristics. For some FBGs installed in an air- cooled graphite-moderated nuclear reactor the difference between the measurements and the readings of calibrated backup thermocouples was within the measurement uncertainty. In the worst case, the differ- ence saturated after 30 h of reactor operation at about 5°C. To reach megagray per hour level gamma-dose rates and 10 19 neutron/cm 2 flu- ences, we irradiated multiplexed FBG sensors in a material testing nuclear reactor. At room temperature, FBG temperature sensors can sur- vive in such radiation conditions, but at 90°C a severe degradation is observed. We evidence the possibility to use FBG sensing technology for in-core monitoring of nuclear reactors with specific care under well- specified conditions.

A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.