Many real-world problems in manufacturing system, for instance, the scheduling problems, are formulated by defining several objectives for problem solving and decision making. Recently, research on dispatching rules allocation has attracted substantial attention. Although many dispatching rules methods have been developed, multi-objective scheduling problems remain inherently difficult to solve by any single rule. In this paper, a hybrid genetic-based gravitational search algorithm (GSA) in weighted dispatching rule is proposed to tackle a scheduling problem by achieving both time and job-related objectives. Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function, while the GSA is used to optimize the contribution weightage of each rule in each stage of the flow shop. The results show that the proposed algorithm is significantly better than the traditional dispatching rules and the rules allocation algorithm. The proposed algorithm not only improved the quality of the schedule in multi-objective problems but also maintained the advantages of traditional dispatching rules in terms of ease of implementation.

This paper compares and evaluates the performance of five different conventional job selection dis-patching rules for scheduling of multi-load automatic guided vehicles (AGVs) serving for material handling operations in variable sized flexible manufacturing system (FMS) layout. Four sizes of FMS layout are considered for the performance evaluation of the five types of conventional job se-lection dispatching rules. The FMS layouts under consideration are served by the two multi-load AGVs. The multi-load AGVs cruises under machine initiated the nearest vehicle (NV) dispatching rule for the material handling activities at all work centers (WCs) for all four sizes of FMS layout. Four sizes of FMS layout produce five different types of parts and consist of three, six, nine and twelve work centers and loading-unloading centers, respectively. In the simulation test, it is found that the identical destination first (IDF) job selection rule having selection criterion based on the destination similarity of two picked up jobs outperforms all other job selection dispatching rules for an overall production rate of the FMS (parts/hr) in all four FMS layouts.

This article addresses the problem of dynamic sequencing on n identical parallel machines with stochastic arrivals, processing times, due dates and sequence-dependent setups. The system operates under a completely reactive scheduling policy and the sequence of jobs is determined with the use of dispatching rules. Seventeen existing dispatching rules are considered including standard and setup-oriented rules. The performance of the system is evaluated by four metrics. An experimental study of the system is conducted where the effect of categorical and continuous system parameters on the objective functions is examined. In light of the results from the simulation experiments, a parameterized priority rule is introduced and tested. The simulation output is analyzed using rigorous statistical methods and the proposed rule is found to produce significantly better results regarding the metrics of mean cycle time and mean tardiness in single machine cases. In respect to three machine cases, the proposed rule matches the performance of the best rule from the set of existing rules which were studied in this research for three metrics.

The problem of generating a train schedule for a single-track railway system is addressed in this paper. A three stage scheduling is proposed to reduce the total train tardiness. We derived an appropriate job-shop scheduling algorithm called DR-algorithm. In the first stage, by determining appropriate weights of the dispatching rules, a pre-schedule is constructed. In the second stage, on the basis of the pre-schedule, the departure times of the trains are modified to reduce the number of conflicts in using railway sections by different trains. In the third stage, a train speed control helps the scheduler to change the trains’ speeds in order to reduce the train tardiness and to reach other objectives. The factual train schedule is based on the modified train speeds and on the modified departure times of the trains. The experimental running of the DR-algorithm on the benchmark instances showed this algorithm can solve train scheduling problems in a close to optimal way. In particular, the total train tardiness was reduced about 20% due to controlling train speeds and the departure times of the trains.