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This paper studies a hybrid artificial bee colony (ABC) algorithm for finding high quality solutions of the job-shop scheduling problem with no-wait constraint (JSPNW) with the objective of minimizing makespan among all the jobs. JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. ABC algorithm is a swarm intelligence technique based on intelligent foraging behavior of honey bee swarm. The proposed hybrid approach effectively coordinates the various components of ABC algorithm such as solution initialization, selection and determination of a neighboring solution with the local search in such a way that it leads to high quality solutions for the JSPNW. The proposed approach is compared with the two best approaches in the literature on a set of benchmark instances. Computational results show the superiority of the proposed approach over these two best approaches.

Finite capacity scheduling (FCS) has been widely implemented in manufacturing companies for detailed shop floor operation management. Most of the traditional and artificial intelligence-based FCS systems lack the ability to provide a satisfying and practical solution for the large-scale scheduling problem in a complex environment. This paper describes a rule-based FCS system for the daily production scheduling (DPS) of a semiconductor backend assembly company. DPS is the FCS module of the gintic scheduling system (GSS), which has been enhanced and implemented successfully in a semiconductor backend assembly company, which is a world market leader in semiconductor packaging technology. Since the focus of this paper is on the DPS module, the overall framework and the other core modules of GSS are briefly covered. The detailed design of the six DPS modules, namely data input/integration, model configuration, data pre-processing, job prioritization (JP) executive, machine selection (MS) executive and result o...

The vehicle routing problem is a central issue in transportation planning and optimization systems. The objective is to determine the most effective routes for a fleet of vehicles in order to service a set of geographically distributed customers while minimizing costs and adhering to capacity restrictions. Due to its inherent complexity, many heuristics have been proposed to solve this combinatorial problem in an effective way. In this paper, a novel hybrid algorithm that combines the Ant Colony Optimization (ACO) meta-heuristics with two local optimization heuristics, namely two-opt and lambda-interchange, is proposed. Experiments are conducted for seven benchmark instances of the vehicle routing problem in order to set up the ACO parameters, with which the reported results are generally outperforms other meta-heuristics. In addition, the proposed hybrid ACO is applied to solve a real life mail delivery network problem. The results from applying the proposed algorithm show that improvement has been achieved in terms of reducing the total distance by 11.7% and minimizing the total over time by 51.4%.