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The paper presents a genetic algorithm for multicriteria optimization of parameters of a technological process, which finds a solution not from a certain point but from a specified population. The objective functions are used without their derivatives and probabilistic choice rules are applied. The multicriteria optimization is based on finding a solution that simultaneously optimizes the machining parameters defined by functions of productivity, tool consumption, tool cost, etc. [ABSTRACT FROM AUTHOR]

In this paper we address the Swap-Body Vehicle Routing Problem (SB-VRP), a variant of the truck and trailer routing problem. It was introduced in the VeRoLog Challenge 2014. We develop a solution approach that we coin Relax-and-Repair. It consists in solving a relaxed version of the SB-VRP and deriving a feasible solution by repairing the relaxed one. We embed this approach within a population-based heuristic. During computation we store all feasible routes in order to derive better solutions by solving a set-partitioning problem. In order to take advantages of nowadays multi-core machines, our algorithm is designed as a collaborative parallel population-based heuristic. Experimental results show that our relax-and-repair algorithm is very competitive and point the impact of each phase on the quality of the obtained solutions. The advantage of our approach is that it can be adapted to solve complex industrial routing problems. [ABSTRACT FROM AUTHOR]

This paper uses genetic algorithm to handle the topology and sizing optimization of truss structures, in which a sparse node matrix encoding approach is used and individual identification technique is employed to avoid duplicate structural analysis to save computation time. It is observed that NSGA-II could not improve the convergence of non-dominated front at latter generations when solving multi-objective topology and sizing optimization of truss structures. Therefore, an adaptive multi-island search strategy for multi-objective optimization problem (AMISS-MOP) is developed to enhance the convergence. Meanwhile, an elitist strategy based on archive set is introduced to reduce the size of non-dominated sorting to improve computation efficiency. Two numeric examples are presented to demonstrate the performance of AMISS-MOP. Results show that the global Pareto front could be found by AMISS-MOP, the convergence is improved as generation increases, and the time spent on non-dominated sorting is reduced. [ABSTRACT FROM AUTHOR]

This paper focuses on an extension of the Economic Lot Scheduling Problem, which schedules productions of products on multiple identical machines. The objective is to minimize the total average production and inventory costs per unit time for all products. We develop a genetic algorithm under the Common Cycle policy and compare it with an existing heuristic under the same policy. Computational results show that our genetic algorithm outperforms the existing heuristic and its running time does not increase much even for high utilization problems, while the latter requires substantial time to solve most of the high utilization problems. In addition, a genetic algorithm under the Extended Basic Period and Power-of-Two policy is proposed. This new heuristic performs much better, especially when the number of machines is small and the machine utilization is not very high. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine a schedule for parallel flow line with bicriteria objective of minimizing the total tardiness and earliness of jobs. An enhancement to its basic greedy randomized adaptive search procedure (GRASP) is used in conjunction with genetic algorithm (GA) and particle swarm optimization (PSO). The feasible solution of GRASP construction phase is used as initial population for both GA and PSO. A number of problems are solved, by varying the number of jobs, lines, and machines, using the hybrid PSO, hybrid GA, PSO, and GA-based methods and the results are compared. [ABSTRACT FROM AUTHOR]

The major objective of advanced manufacturing control techniques is to provide efficient and accurate tools in order to maintain manufacturing systems in real-time operations. This research employs case-based reasoning (CBR) advocated by genetic algorithms (GAs) to solve optimal regulation problems with prescribed material uncertainties. An instrument for non-contact measuring machines, based on two laser sensors, has been designed for integrated product and process monitoring. Unfortunately, the measuring precision is not as good as the resolution of a single laser. In this paper, with the description of the model design of the measuring system, genetic algorithms and case-based reasoning are utilized to design a set membership model for optimal regulation. Experimental results for calibration of thickness have shown that the developed technique is both effective and efficient in achieving the enhancement in precision rather than labour regulation and it fulfills the aim of automation. [ABSTRACT FROM AUTHOR]

Open job shop scheduling is a kind of job shop scheduling in which operations can be performed in any order. In this paper an attempt is made to develop a heuristic for the open job shop scheduling problem using genetic algorithm to minimize makespan. Genetic algorithm operators are suitably modified to maintain feasibility. The results are statistically compared and found to be significantly better than the earlier reported results. [ABSTRACT FROM AUTHOR]

This paper studies dynamic selection of sequencing rules for a class-based unit-load automated storage and retrieval system (AS/RS). A multi-pass and genetic algorithm (MPGA) simulation system is developed and it divides storage and retrieval requests or dual commands into a series of blocks, and then conquers each block to find the most promising combination of sequencing rules. These rules are first come first served (FCFS), shortest total-travel time (STT) and shortest due time (SDT), and they can be chosen dynamically in any decision points in the system. An experiment shows that our approach with dynamic rules is much better than those approaches with any single rule used from the beginning to the end in the whole system. The results of this study provide a better way to control and manage the operation of AS/RS. [ABSTRACT FROM AUTHOR]

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance. [ABSTRACT FROM AUTHOR]

In the past three decades many studies have been carried out on cellular manufacturing. The main problem in the development of cellular manufacturing is that of machine cell formation. In this paper a new metaheuristic called a memetic algorithm (MA) is introduced to solve the machine cell formation problem in group technology. The objective functions considered in this work are (a) minimization of total number of moves and (b) minimization of cell load variation and the constraints considered are minimum number of machines in each cell as two and each machine should be assigned in one cell only. Effort has been made to develop an algorithm that is more reliable than conventional methods and some non-traditional optimization techniques like the genetic algorithm (GA) and the tabu search algorithm (TS) for solving machine cell formation problem. In the memetic algorithm approach local optimization is applied to each newly generated offspring at the end of genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we firstly reformulate the landscape theory of aggregation (Axelrod and Bennett, 1993) in terms of an optimization problem and then straightforwardly propose a fuzzy-set-theoretic based extension for it. To illustrate efficiency of the proposal we make a simulation with the proposed framework for the international alignment of the Second World War in Europe. It is shown that the obtained results are essentially comparable to those given by the original theory. Consequently. the fuzzy-set-theoretic based extension of landscape theory can allow us to analyze a wide variety of aggregation processes in politics, economics, and society in a more flexible manner. [ABSTRACT FROM AUTHOR]

The selection of optimal machining parameters plays an important part in computer-aided manufacturing. The optimisation of machining parameters is still the subject of many studies. Genetic algorithm (GA) and simulated annealing (SA) have been applied to many difficult combinatorial optimisation problems with certain strengths and weaknesses. In this paper, genetic simulated annealing (GSA), which is a hybrid of GA and SA, is used to determine optimal machining parameters for milling operations. For comparison, basic GA is also chosen as another optimisation method. An application example that has previously been solved using geometric programming (GP) method is presented. The results indicate that GSA is more efficient than GA and GP in the application of optimisation. [ABSTRACT FROM AUTHOR]

Conventional tolerance analysis is tedious and time consuming, which makes engineers resist doing it. Complex assembly problems are generally beyond the capabilities of most design and manufacturing engineers. In this paper, genetic algorithm, a kind of non-traditional optimization technique is used as the basic foundation for optimal tolerance allocation to help design and manufacturing engineers to overcome the shortcomings in the conventional tolerance stack analysis and allocation system. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks. [ABSTRACT FROM AUTHOR]