In real manufacturing environments, variations in production factors (i.e. processing time, demand, due-dates) are inevitable facts. All these dynamic changes, together with random disturbances (e.g. machine breakdowns) can seriously affect the system performance. In this paper we focus on load, processing time and due date variation and analyse their impacts on a scheduling system. Specifically, we investigate the impact of variation on dispatching policies in a job shop environment via simulation. The statistical analysis of the results leads to two major conclusions: first, the relative performance of rules is not threatened much by PV (processing time variation), LV (load variation) or DDV (due date variation) - a result that can be a consolation for practitioners in the field. Secondly, the performance of the rules deteriorates, in particular at high levels of PV, LV and DDV - a result that can provide new insights into the problem and produces useful information for researchers in their continuous effort to develop better dispatching rules
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 eectively for the MAD measure. #
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