Abstract Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states ( Predictive Monitoring ). Remaining Time Prediction of process instances is an important task in field (PPM). Existing approaches have two key limitations developing Models (RTM): (1) The features predictions lack context, and created mo...

In this paper we provide a novel approach to characterize the order process of an (s, S) and an (r, nQ) inventory policy. These inventory policies are commonly used when a fixed order cost is present. We provide two distinct perspectives. First, we analyze the time between subsequent replenishments, as well as the size of the replenishment orders, and characterize the distribution of both. Seco...

We consider a batch scheduling problem in which the processing time of a batch of jobs equals the maximum of the processing times of all jobs in the batch. This is the case, for example, for burn-in operations in semiconductor manufacturing, and other testing operations. Processing times are assumed to be random, and we consider minimizing the makespan and the flowtime. The problem is much more...

We consider the problem of partitioning interval graphs into cliques of bounded size. Each interval has a weight, and the cost of a clique is the maximum weight of any interval in the clique. This natural graph problem can be interpreted as a batch scheduling problem. Solving an open question from [7, 4, 5], we show NP-hardness, even if the bound on the clique sizes is constant. Moreover, we gi...

Some scheduling problems, including the Travelling Salesman Problem (TSP), have a “big valley” search landscape: the best solutions have common building blocks, so crossover works well. Unfortunately, in a realistic single-machine problem (TSP with batching, sequence-dependent setups, and multi-level precedence constraints) the best solutions have little in common. A mutation-only approach

In this work we introduce a Genetic Algorithm (GA) for the Order Batching Problem considering traversal and return routing policies. The proposed GA has been tested on randomly generated instances and compared with the wellknown savings algorithm. According to our extensive computational experiments we can say that the proposed GA yields promising solutions in acceptable computation times.

Aiming at the problem that accuracy and economy of traditional off-line batching method are not high, online system (BSMLIA) based on machine learning intelligent algorithms was put forward from three aspects: real-time, technical requirements economic benefits. The accurate solution on-line fast calculation sintering raw material ratio under influence multiple factors solved. Specifically, a B...

Latency or throughput is often critical performance metrics in stream processing. Applications’ can fluctuate depending on the input stream. This unpredictability due to variety data arrival frequency and size, complexity, other factors. Researchers are constantly investigating new ways mitigate impact of these variations with self-adaptive techniques involving elasticity micro-batching. Howeve...