Minimizing Cumulative Batch Processing Time for an Industrial Oven Scheduling Problem

We introduce the Oven Scheduling Problem (OSP), a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative batch processing time across all ovens. This objective distinguishes the OSP from other batch processing problems which typically minimize objectives related to makespan, tardiness or lateness. We propose to solve this NP-hard scheduling problem via constraint programming (CP) and integer linear programming (ILP) and present corresponding CP- and ILP-models. For an experimental evaluation, we introduce a multi-parameter random instance generator to provide a diverse set of problem instances. Using state-of-the-art solvers, we evaluate the quality and compare the performance of our CP- and ILP-models, which could find optimal solutions for many instances. Furthermore, using our models we are able to provide upper bounds for the whole benchmark set including large-scale instances

Soft real-time scheduling on multiprocessors

The design of real-time systems is being impacted by two trends. First, tightly-coupled multiprocessor platforms are becoming quite common. This is evidenced by the availability of affordable symmetric shared-memory multiprocessors and the emergence of multicore architectures. Second, there is an increase in the number of real-time systems that require only soft real-time guarantees and have workloads that necessitate a multiprocessor. Examples of such systems include some tracking, signal-processing, and multimedia systems. Due to the above trends, cost-effective multiprocessor-based soft real-time system designs are of growing importance. Most prior research on real-time scheduling on multiprocessors has focused only on hard real-time systems. In a hard real-time system, no deadline may ever be missed. To meet such stringent timing requirements, all known theoretically optimal scheduling algorithms tend to preempt process threads and migrate them across processors frequently, and also impose certain other restrictions. Hence, the overheads of such algorithms can significantly reduce the amount of useful work that is accomplished and limit their practical implementation. On the other hand, non-optimal algorithms that are more practical suffer from the drawback that their validation tests require workload restrictions that can approach roughly 50% of the available processing capacity. Thus, for soft real-time systems, which can tolerate occasional or bounded deadline misses, and hence, allow for a tradeoff between timeliness and improved processor utilization, the existing scheduling algorithms or their validation tests can be overkill. The thesis of this dissertation is: Processor utilization can be improved on multiprocessors while providing non-trivial soft real-time guarantees for different soft real-time applications, whose preemption and migration overheads can span different ranges and whose tolerances to tardiness are different, by designing new algorithms, simplifying optimal algorithms, and developing new validation tests. The above thesis is established by developing validation tests that are sufficient to provide soft real-time guarantees under non-optimal (but more practical) algorithms, designing and analyzing a new restricted-migration scheduling algorithm, determining the guarantees on timeliness that can be provided when some limiting restrictions of known optimal algorithms are relaxed, and quantifying the benefits of the proposed mechanisms through simulations. First, we show that both preemptive and non-preemptive global earliest-deadline-first(EDF) scheduling can guarantee bounded tardiness (that is, lateness) to every recurrent real-time task system while requiring no restriction on the workload (except that it not exceed the available processing capacity). The tardiness bounds that we derive can be used to devise validation tests for soft real-time systems that are EDF-scheduled. Though overheads due to migrations and other factors are lower under EDF (than under known optimal algorithms), task migrations are still unrestricted. This may be unappealing for some applications, but if migrations are forbidden entirely, then bounded tardiness cannot always be guaranteed. Hence, we consider providing an acceptable middle path between unrestricted-migration and no-migration algorithms, and as a second result, present a new algorithm that restricts, but does not eliminate, migrations. We also determine bounds on tardiness that can be guaranteed under this algorithm. Finally, we consider a more efficient but non-optimal variant of an optimal class of algorithms called Pfair scheduling algorithms. We show that under this variant, called earliest- pseudo-deadline-first (EPDF) scheduling, significantly more liberal restrictions on workloads than previously known are sufficient for ensuring a specified tardiness bound. We also show that bounded tardiness can be guaranteed if some limiting restrictions of optimal Pfair algorithms are relaxed. The algorithms considered in this dissertation differ in the tardiness bounds guaranteed and overheads imposed. Simulation studies show that these algorithms can guarantee bounded tardiness for a significant percentage of task sets that are not schedulable in a hard real-time sense. Furthermore, for each algorithm, conditions exist in which it may be the preferred choice

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Scheduling strategies for the furniture industry

Technological developments and more demanding production standards have constantly been pushing the envelope, changing the perception of what is possible and desired in manufacturing processes. Such improvements are often made at marginal cost, yet have the potential to significantly benefit performance, enabling a strong competitive advantage. In this case study, a factory in the furniture industry is considered, where there are vast improvement opportunities and an increase in flexibility is needed. Furthermore, this problem can be best approximated by the flow shop model and the most critical characteristic is sequence-dependent setup times. To address this problem, an iterated greedy with local search meta-heuristic is implemented, which will be responsible for scheduling production orders in the way that best suits makespan and, consequently, productivity. Additionally, OptQuest, the optimiser functionally built into the Flexsim simulating software was also tested against the meta-heuristic and, still through simulation, a local rule was implemented, which allowed each workstation to define its own sequence of jobs, to minimise setup times. Lastly, the best performing of the previous methods was also compared to the original heuristic that had previously been specifically created for this problem. Through testing, it was found that the iterated greedy with local search meta-heuristic was able to generate solutions that had a much better makespan value than the ones produced by OptQuest, while the local rule was not able to provide significant improvement. Then, the meta-heuristic was compared to the original heuristic and, although the newly implemented algorithm did not consider all characteristics of the problem, productivity far outperformed that of the original technique