Solving Assembly Line Balancing Problems by Combining IP and CP

Assembly line balancing problems consist in partitioning the work necessary to assemble a number of products among different stations of an assembly line. We present a hybrid approach for solving such problems, which combines constraint programming and integer programming.Comment: 10 pages, Sixth Annual Workshop of the ERCIM Working Group on Constraints, Prague, June 200

Grouping genetic algorithm for industrial engineering applications

Industry is inundated with grouping problems concerned with formation of groups or clusters of system entities for the purpose of improving the overall system efficiency and effectiveness. Various extant grouping problems include cell formation problem, vehicle routing problem, bin packing problem, truck loading, home healthcare scheduling, and task assignment problem. Given the widespread grouping problems in industry, it is important to develop a tool for solving such problems from a common view point. This paper seeks to identify common grouping problems, identify their common grouping structures, present an outline of group genetic algorithm (GGA), and map the problems to the GGA approach. The practicality of the GGA tool in is highly promising in Industrial Engineering applications

An Analogy between Bin Packing Problem and Permutation Problem: A New Encoding Scheme

Part 2: Knowledge Discovery and SharingInternational audienceThe bin packing problem aims to pack a set of items in a minimum number of bins, with respect to the size of the items and capacity of the bins. This is an NP-hard problem. Several approach methods have been developed to solve this problem. In this paper, we propose a new encoding scheme which is used in a hybrid resolution: a metaheuristic is matched with a list algorithm (Next Fit, First Fit, Best Fit) to solve the bin packing problem. Any metaheuristic can be used but in this paper, our proposition is implemented on a single solution based metaheuristic (stochastic descent, simulated annealing, kangaroo algorithm). This hybrid method is tested on literature instances to ensure its good results

An efficient genetic algorithm application in assembly line balancing.

The main achievement of this research is the development of a genetic algorithm model as a solution approach to the single model assembly line balancing problem (SMALBP), considered a difficult combinatorial optimisation problem. This is accomplished by developing a genetic algorithm with a new fitness function and genetic operators. The novel fitness function is based on a new front-loading concept capable of yielding substantially improved and sometimes optimum solutions for the SMALBP. The new genetic operators include a modified selection technique, moving crossover point technique, rank positional weight based repair method and dynamic mutation technique. The moving crossover point technique addressed the issue of propagating best attributes from parents to offspring and also supports the forward loading process. The new selection technique was developed by modifying the original rank-based selection scheme. This eliminates the high selective pressure associate with the original rank-based technique. Furthermore, the modified selection technique allows the algorithm to run long enough, if required, without premature convergence and this feature is very useful for balancing more complex real world problems. The repair technique included in this model repairs a higher proportion of distorted chromosomes after crossover than previous methods. Moreover, a third innovative feature, a moving adjacent mutation technique, strengthens the forward loading procedure and accelerates convergence. The performance of the front-loading fitness function currently outperforms the published fitness functions and fifty-four published test cases generated from sixteen precedence networks are used to assess the overall performance of the model. Encompassing the new genetic algorithm concepts, forty-four test problems (81%) achieved the best solutions obtained by published techniques and twenty-four problems (44%) produced better results than the benchmark Hoffmann precedence procedure, the closest non-genetic algorithm method. The superiority of the genetic model over other heuristics is identified in this research and future developments of this genetic algorithm application for assembly line balancing problems is evident

Swelling kinetics of the onion phase

A theory is presented for the behavior of an array of multi-lamellar vesicles (the onion phase) upon addition of solvent. A unique feature of this system is the possibility to sustain pressure gradients by tension in the lamellae. Tension enables the onions to remain stable beyond the unbinding point of a flat lamellar stack. The model accounts for various concentration profiles and interfaces developing in the onion as it swells. In particular, densely packed `onion cores' are shown to appear, as observed in experiments. The formation of interfaces and onion cores may represent an unusual example of stabilization of curved interfaces in confined geometry.Comment: 13 pages, 10 PS figures, LaTeX using SVJour, submitted to Eur Phys J