Dynamic Job Shop Environment Research Articles

1408 動的ジョブショップ環境における複数 AGV のハイブリッド型経路探索法

1408 動的ジョブショップ環境における複数 AGV のハイブリッド型経路探索法

Auction-based distributed scheduling in a dynamic job shop environment

Developments in computing and communication technology coupled with the inability to address real-time issues in scheduling algorithms based on central control, has led to an interest in solving the problem of a distributed decision-making environment. This paper presents a new job shop formulation that schedules jobs using auctions for distributing control. A theoretical basis is presented for problem decomposition, bid construction, and bid evaluation for the auction using standard mathematical programming tools. Numerical results show that the auction-based approach outperforms the distributed dispatching approaches and can be used to create schedules for a wide range of scheduling objectives.

Routing-based reactive scheduling policies for machine failures in dynamic job shops

A scheduling and control system can be viewed as a vital component of modern manufacturing systems that determines companies' overall performance in their respective supply chains. This paper studies reactive scheduling policies developed against unexpected machine failures. These reactive policies are based on rerouting the jobs to their alternative machines when their primary machine fails. Depending on the subset of the jobs considered for rerouting, the long-term performance of four policies are tested under various conditions. Expecting that these rerouting policies would bring an extra load for a material-handling system (MHS), a dynamic job shop environment was studied with and without a MHS. It is shown that the proper selection of a good reactive policy is based not only on the system characteristics such as utilization, machine down times and frequency of machine failures, but also on the MHS capacity (in terms of speed and number of MH devices). The extensive experiments show that when the MHS is not a bottleneck and/or the down times are long enough to compensate the cost of extra rerouting, rerouting all affected jobs to their alternative machines proves to be the best policy. However, when the MHS cannot handle the extra load due to rerouting or the down times are relatively short, then rerouting only the jobs that will arrive to the failed machine during repair performs the best.

Implementation of an auction-based distributed scheduling model for a dynamic job shop environment

This paper presents the details of distributed implementation of a new auction-based distributed scheduling mechanism for a job shop that can be used for a wide range of objectives. The problem is modelled similar to a combinatorial auction with math programming tools used for bid construction and evaluation. Each entity in the shop is represented by a process interacting with other processes over the network as and when needed. Preliminary results from implementing this model on a network of four computers for problems of varying size indicate that the computing benefits of distributed implementation can be realized despite network delays.

Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach

This paper is a report on a simulation study to investigate the performance of a number of scheduling rules on the basis of a rolling time horizon approach for a dynamic job shop environment. The performance measure considered is an economic objective which includes the main costs involved in a scheduling decision. The first purpose of the study was to find the best scheduling rule and the second to investigate the effects of the rescheduling interval on performance and examine whether there is a policy that can always improve performance. The simulation study, which is part of a larger project on practical workshop scheduling, has been carried out under widely varying conditions in terms of due date tightness, shop load level, and shop load balance level. The results show that a recently developed scheduling rule, SPT-C/R, is the most appropriate scheduling rule in minimizing overall cost and that the relationship between performance and rescheduling interval can be shown.

Scheduling for non regular performance measure under the due window approach

In the last two decades, Just-In-Time (JIT) production has proved to be an essential requirement of world class manufacturing. This has made schedulers most concerned about the realization of a JIT environment. The JIT concept requires not only a penalty for backorder and lateness but also for earliness. This can be translated into non-regular scheduling objectives. The most obvious objective can be to minimize the deviation of completion times. Concerning earliness/tardiness problems, researchers have usually considered systems where jobs incur no penalty for completion at a certain point of time (i.e. due date). In practice, however, job completions can also be accepted without penalty within an interval in time, which is known as the due window. This paper studies the scheduling problems in terms of the non-regular measure, mean absolute deviation (MAD), under the due window approach. The study is conducted in a dynamic job shop environment. Furthermore, we propose two new rules that perform quite effectively for the MAD measure.

Comparison of Order Review and Release techniques in a dynamic and uncertain job shop environment

This paper proposes a new methodological pattern to assess the effectiveness of Order Review and Release (ORR) techniques in a job shop environment. The standpoint for this new method lies in the following remarks: (i) comparisons among ORR models should be performed in dynamic and uncertain environments; (ii) ORR techniques robustness toward the shop uncertainty and perturbations should be considered; and (iii) ORR models should be compared by changing their features one at a time, instead of comparing completely different ORR techniques. Consistently, we present a comparison among three ORR models, previously developed in literature, aimed at investigating: (i) the impact of a dynamic and uncertain environment on the performances achieved; (ii) the robustness of these ORR models when facing some environmental perturbations, like the system workload, the mix imbalance, the machine unavailability and the processing time variability, that usually take place in real life job shops; and (iii) the overall effectiveness of the way workload is accounted for over time, since the models differentiate only by this item, while any other feature of the release mechanism is the same. Simulation results highlight that the performances of the ORR techniques tested depend on how perturbed the environment where they are implemented is. Moreover, the ORR techniques tested greatly differ in their robustness against environment perturbations.

A rolling horizon job shop rescheduling strategy in the dynamic environment

In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.