20,396 research outputs found
Models and Strategies for Variants of the Job Shop Scheduling Problem
Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorably to more classical constraint programming methods relying on
propagation algorithms for global unary or cumulative resource constraints and
dedicated search heuristics. In particular, we described an approach that
combines restarting, with a generic adaptive heuristic and solution guided
branching on a simple model based on a decomposition of disjunctive
constraints. In this paper, we introduce an adaptation of this technique for an
important subclass of job shop scheduling problems (JSPs), where the objective
function involves minimization of earliness/tardiness costs. We further show
that our technique can be improved by adding domain specific information for
one variant of the JSP (involving time lag constraints). In particular we
introduce a dedicated greedy heuristic, and an improved model for the case
where the maximal time lag is 0 (also referred to as no-wait JSPs).Comment: Principles and Practice of Constraint Programming - CP 2011, Perugia
: Italy (2011
Min-Max Regret Scheduling To Minimize the Total Weight of Late Jobs With Interval Uncertainty
We study the single machine scheduling problem with the objective to minimize
the total weight of late jobs. It is assumed that the processing times of jobs
are not exactly known at the time when a complete schedule must be dispatched.
Instead, only interval bounds for these parameters are given. In contrast to
the stochastic optimization approach, we consider the problem of finding a
robust schedule, which minimizes the maximum regret of a solution. Heuristic
algorithm based on mixed-integer linear programming is presented and examined
through computational experiments
Parameterized complexity of machine scheduling: 15 open problems
Machine scheduling problems are a long-time key domain of algorithms and
complexity research. A novel approach to machine scheduling problems are
fixed-parameter algorithms. To stimulate this thriving research direction, we
propose 15 open questions in this area whose resolution we expect to lead to
the discovery of new approaches and techniques both in scheduling and
parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc
Preemptive Scheduling of Equal-Length Jobs to Maximize Weighted Throughput
We study the problem of computing a preemptive schedule of equal-length jobs
with given release times, deadlines and weights. Our goal is to maximize the
weighted throughput, which is the total weight of completed jobs. In Graham's
notation this problem is described as (1 | r_j;p_j=p;pmtn | sum w_j U_j). We
provide an O(n^4)-time algorithm for this problem, improving the previous bound
of O(n^{10}) by Baptiste.Comment: gained one author and lost one degree in the complexit
Single machine scheduling problems with uncertain parameters and the OWA criterion
In this paper a class of single machine scheduling problems is discussed. It
is assumed that job parameters, such as processing times, due dates, or weights
are uncertain and their values are specified in the form of a discrete scenario
set. The Ordered Weighted Averaging (OWA) aggregation operator is used to
choose an optimal schedule. The OWA operator generalizes traditional criteria
in decision making under uncertainty, such as the maximum, average, median or
Hurwicz criterion. It also allows us to extend the robust approach to
scheduling by taking into account various attitudes of decision makers towards
the risk. In this paper a general framework for solving single machine
scheduling problems with the OWA criterion is proposed and some positive and
negative computational results for two basic single machine scheduling problems
are provided
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