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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.12.019 Byline: David Naso (a), Michele Surico (a), Biagio Turchiano (a), Uzay Kaymak (b) Keywords: Genetic algorithms; Scheduling; Heuristics; Supply chain management Abstract: The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspect of supply chain management. From a theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problems, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-mixed concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach. Author Affiliation: (a) Politecnico di Bari, Dipartimento di Elettrotecnica ed Elettronica, Via Re David, 200, 70125 Bari, Italy (b) Erasmus University Rotterdam, Faculty of Economics, Department of Computer Science, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark. Index Terms--Adaptive fuzzy control (AFC), genetic algorithms (GAs).

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness. Index Terms--Automated guided vehicles (AGVs), dispatching, fuzzy control, genetic algorithms, manufacturing automation.

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach. Index Terms--Cooperative systems, genetic algorithms, manufacturing scheduling.