We describe an approximation method for solving the minimum makespan problem of job shop scheduling. It sequences the machines one by one, successively, taking each time the machine identified as a bottleneck among the machines not yet sequenced. Every time after a new machine is sequenced, all previously established sequences are locally reoptimized. Both the bottleneck identification and the local reoptimization procedures are based on repeatedly solving certain one-machine scheduling problems. Besides this straight version of the Shifting Bottleneck Procedure, we have also implemented a version that applies the procedure to the nodes of a partial search tree. Computational testing shows that our approach yields consistently better results than other procedures discussed in the literature. A high point of our computational testing occurred when the enumerative version of the Shifting Bottleneck Procedure found in a little over five minutes an optimal schedule to a notorious ten machines/ten jobs problem on which many algorithms have been run for hours without finding an optimal solution.

The Shifting Bottleneck Procedure for Job Shop Scheduling

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

Neural-network-based fuzzy logic control and decision system

computational bounds for local search in combinatorial local search algorithms for combinatorial problems local search algorithms for solving the combinatorial a dual local search framework for combinatorial the max-min ant system and local search for combinatorial a framework for local combinatorial optimization problems local search in combinatorial optimization mifou local search in combinatorial optimization radarx heuristics and local search paginas.fe.up local search in combinatorial optimization banani local search in combinatorial optimization integrating interval estimates of global optima and local local search in combinatorial optimization holbarto a fuzzy valuation-based local search framework for local search in combinatorial optimization gbv gentle introduction to local search in combinatorial e cient local search for several combinatorial introduction: combinatorial problems and search on application of the local search optimization online local search for combinatorial optimisation problems how to choose solutions for local search in multiobjective a hybrid of inference and local search for distributed on set-based local search for multiobjective combinatorial localsolver: black-box local search for combinatorial on local optima in multiobjective combinatorial sets of interacting scalarization functions in local introduction e r optimization online estimation-based local search for stochastic combinatorial advanced search combinatorial problems sas/or user's guide: local search optimization how easy is local search? univerzita karlova metaheuristic search for combinatorial optimization local search genetic algorithms for the job shop towards a formalization of combinatorial local search dynamic and adaptive neighborhood search in combinatorial global search in combinatorial optimization using a framework for the development of local search heuristics a feasibility-preserving local search operator for hybrid metaheuristics in combinatorial optimization: a survey consultant-guided search algorithms with local search for a new optimization algorithm for combinatorial problems hybrid metaheuristics in combinatorial optimization: a survey network algorithms colorado state university model-based search for combinatorial optimization: a local and global optimization stochastic local search algorithms for multiobjective corso (collaborative reactive search optimization local search in combinatorial optimization zaraa

Local Search in Combinatorial Optimization

This paper reviews recent studies of dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. The paper has two objectives. The first is to discuss the state of the art in the study of dispatching rules. The discussion includes analytical approaches, simulation techniques and evaluation criteria. The second objective of the paper is to compare several of the dispatching rules listed in the Appendix using the results of recently published studies. It is impossible to identify any single rule as the best in all circumstances. However, several rules have been identified as exhibiting good performance in general.

A state-of-the-art survey of dispatching rules for manufacturing job shop operations.

In this paper, we propose a branch and bound method for solving the job-shop problem. It is based on one-machine scheduling problems and is made more efficient by several propositions which limit the search tree by using immediate selections.

It solved for the first time the famous 10 × 10 job-shop problem proposed by Muth and Thompson in 1963.

An algorithm for solving the job-shop problem

(1977). Introduction to Sequencing and Scheduling. Journal of the Operational Research Society: Vol. 28, No. 2, pp. 352-353.

https://link.springer.com/content/pdf/10.1057/jors.1977.60.pdf

A. J. Clewett

Papers

Introduction to sequencing and scheduling