Neighborhood Search Technique Research Articles

Production Control of a Flexible Manufacturing System in a Job Shop Environment

This paper provides an application oriented analysis of local search procedures for Operation Scheduling and Shop Floor Management of a major German manufacturer of cigarette machines. The heuristics applied are the Threshold‐ and Simulated Annealing‐Algorithm considering Job Shop as well as embedded FMS production features. In this context a new neighbourhood search technique is developed, which is based on a small set of local neighbourhoods and is flexible with respect to the performance measurements of production control. By this approach the scheduling, loading and workload allocation problems of a production facility consisting of an embedded FMS and a conventional Job Shop can be solved simultaneously.

Production control of a flexible manufacturing system in a job shop environment

This paper provides an application oriented analysis of local search procedures for Operation Scheduling and Shop Floor Management of a major German manufacturer of cigarette machines. The heuristics applied are the Threshold- and Simulated Annealing-Algorithm considering Job Shop as well as embedded FMS production features. In this context a new neighbourhood search technique is developed, which is based on a small set of local neighbourhoods and is flexible with respect to the performance measurements of production control. By this approach the scheduling, loading and workload allocation problems of a production facility consisting of an embedded FMS and a conventional Job Shop can be solved simultaneously.

A comparison of neighborhood search techniques for multi-objective combinatorial problems

In this paper we compare the performance of simulated annealing and tabu search when both are applied to a large, complex multi-objective flow-shop problem. Repeated descent, an older neighborhood search technique, is used as a basis for comparison. The best parameters needed for the methods and their subsequent performance are compared on two further levels: as the total number of perturbations allowed increases and as the number of objectives increases. It is shown that, as the number of objectives (and thus the complexity of the problem) increases, simulated annealing (which epitomizes randomized search methods) becomes more attractive than tabu search (which epitomizes deterministic search methods). In addition, the relative value of simulated annealing is greater when only a small number of perturbations are allowed. Reasons are offered for these phenomena. These experiments identify the need for appropriate measures of complexity of a combinatorial problem. Two such measures are described, and it is shown that these can lead us to revise our a priori notions about the complexity of multi-objective combinatorial problems. In particular, the complexity of a combinatorial problem may be strongly influenced by the type of objectives as well as their number.

An Object-Oriented Methodology for Solving Assignment-Type Problems with Neighborhood Search Techniques

Because of its specificity, it is usually difficult to reuse computer code developed for a given combinatorial problem to deal with another one. We use the Object Oriented Programming methodology to derive general purpose software including four different neighborhood search techniques (descent method, tabu search, exchange procedure, simulated annealing) to deal with any assignment-type problem with a bare minimum of coding effort. The structure of the software even allows a more advanced user to play around with several parameters of these techniques and to modify the techniques to create specific variants.

Statistical analysis of parallel randomized algorithms for VLSI placement and implementation on workstation networks

A massively parallel optimization approach based on simple neighbourhood search techniques is developed and applied to the problem of VLSI cell placement. Statistical models are developed to analyse the performance of the approach in general, and to derive statistical bounds on the quality of obtainable results. Specific questions addressed are: (1) Given a solution with a known cost, how can we measure its quality? (2) Given a target cost for the solution, how likely is the algorithm to generate a solution with that cost for better? (3) Are there any performance bounds for the solutions obtainable by neighbourhood search methods? (4) How can we measure for quantify the performance of different neighbourhood search methods? The results of these analyses suggest a simple framework for approximate solution of difficult problems. The approach is inherently parallel, and it can be implemented on any type of parallel computer. We implemented it on the PVM environment running on a network of workstations connected by Ethernet. The method is empirically verified by testing its performance on a number of sample problems and by comparing the results found to earlier results reported in the literature.

A genetic algorithm for flowshop sequencing

The basic concepts of Genetic Algorithms are described, following which a Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem. The performance of the algorithm is then compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm on some carefully designed sets of instances of this problem.

Scheduling of batch digesters according to different control targets and servicing limitations

The scheduling problem for a set of batch digesters with cyclic repetition of phases is considered. The specific problem addressed is the on-line scheduling of pulp digesters that operate in two parallel production lines under common resource limitations. Each line has a storage tank which is installed after the batch section and feeds continuously operated line downstream. The problem of scheduling is to determine the digester sequence and the corresponding batch start and completion times in order to maintain continuous production downstream while also maximize batch cycle time to improve pulp quality and reduce energy consumption. The proposed solution procedure is based on a heuristic algorithm combining neighborhood search technique and linear programming. Searching for the batch sequence enables to set the production rate for each line individually. Using linear programming, the problem is formulated generally enough to be adapted to frequent changes of control requirements quickly and easily. The algorithm was first tested off-line for a set of different operating conditions. Also, it was included into a pulp cooking simulation package and tested as a part of control algorithms designed for computer automation.

The application of the simulated annealing algorithm to the solution of the n/ m/ Cmax flowshop problem

In this paper we present our experience in using both heuristic and randomly generated solutions as initial seeds to a simulated annealing optimisation process which we propose for solving (approximately) the n-job, m-machine flowshop scheduling problem with minimum completion time (makespan) as the measure of performance. Two neighbourhood search routines are employed as perturbation schemes and we compare their performance. We show that for the proposed simulated annealing algorithm, the choice of a perturbation technique affects the quality of the final solution. In particular, a neighbourhood search technique that yields a large neighbourhood will perform better than one with a smaller neighbourhood. The proposed simulated annealing process is shown to provide as good quality solutions as the conventional (Metropolis) scheme (where the acceptance probability depends on the change in objective function); however it does this in less computation time. Finally, we compare the performance of the proposed annealing process with a repetitive iterative improvement algorithm to investigate the benefits since it has been shown that both algorithms asymptotically converge to global optima. It is found that the proposed simulated annealing algorithm provides better solutions than repeated iterative improvement algorithm, for a fixed total computational time.

ON METHODS FOR DISCRETE STRUCTURAL OPTIMIZATION

Abstract Three algorithms for discrete structural optimization are considered. The original discrete structural optimization problem is replaced by a sequence of approximate discrete subproblems. For small size problems a Branch and Bound method is used to find a global minimum for each subproblem. The use of a generalized Lagrangean function and nonconvex duality is compared to the use of the Branch and Bound method. The generalized Langrangean function is minimized over the discrete set using a neighbourhood search technique. The dual function is maximized using a subgradient method. A method for rounding off the continuous solution is also used. The performance of the algorithms is investigated on several numerical examples. Global discrete minima are found for two convex test problems by using Branch and Bound on the original discrete structural optimization problem.