A learning stage of scheduling tends to produce knowledge about a benchmark of priority dispatching rules which allows a scheduler to improve the solution quality for a set of similar job-shop problems. Once trained on the sample job-shop problems (usually with small sizes), the adaptive algorithm solves a similar job-shop problem (with a moderate size or a large size) better than heuristics us...

An interesting research area is to solve scheduling problems using network flow models and algorithms. Most of the existing papers in this research area deal with scheduling problems with parallel machines (identical and / or uniform machines). This paper considers the specialized case and develops network flow models and algorithms to solve open shop and flow and job shop problems.

The just-in-time production philosophy has led to a growing interest in scheduling problems considering both earliness and tardiness penalties. Most publications considering these so-called non-regular objective functions are devoted to single machine problems. In the case of multi-machine problems, there are some papers dealing with parallel machine problems. However, for multi-operation sched...

The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous eeorts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderate...

The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous eeorts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderate...

In this work we show that certain classical preemptive shop scheduling problems with integral data satisfy the following integer preemption property: there exists an optimal preemptive schedule where all interruptions and all starting and completion times occur at integral dates. We also give new upper bounds on the minimal number of interruptions for various shop scheduling problems.

Lot sizing and scheduling in flow shop has been considered in this paper. Our study includes a multi-level and multi-period capacitated lot sizing and scheduling problem (CLSP) with sequence-dependent setups, setups carry over in flow shop. In manufacturing environments backlogging is unavoidable. If the production capacity is infinite, any demand can be satisfied

Shop scheduling problems are known to be notoriously intractable, both in theory and practice. In this paper we give a randomized approximation algorithm for flow shop scheduling where the number of machines is part of the input problem. Our algorithm has a multiplicative factor of 2(1 + δ) and an additive term of O(m ln(m+ n)pmax)/δ ).