In this paper, we first introduce a generalized version of open shop scheduling (OSS), called generalized cyclic open shop scheduling (GCOSS) and then develop a hybrid method of metaheuristic to solve this problem. Open shop scheduling is concerned with processing n jobs on m machines, where each job has exactly m operations and operation i of each job has to be processed on machine i . However...

There are two ways in which the SAT problems can be framed: satisfiability problem and optimization problem. The optimization problem asks what is the largest number of clauses we can satisfy. The satisfiability problem is a special case of the optimization problem: it asks whether OPT (φ) = n, where n is the number of clauses in φ. It is obvious that MAX-3SAT is NP-hard since 3SAT itself is NP...

Large-scale projects often have several activities which are performed by subcontractors with limited multi-resources. Project scheduling with limited resources is one of the most famous problems in the research operations and optimization cases. The resource-constraint project scheduling problem (RCPSP) is a NP-hard problem in which the activities of a project must be scheduled to reduce the p...

This paper presents a novel, integer-linear programming (ILP) model for an identical parallel-machine scheduling problem with family setup times that minimizes the total weighted flow time (TWFT). Some researchers have addressed parallel-machine scheduling problems in the literature over the last three decades. However, the existing studies have been limited to the research of independent jobs,...

The buffer allocation problem is an NP-hard combinatorial optimization problem, and it is an important design problem in manufacturing systems. The research proposed in this paper concerns a product line consisting of n unreliable machines with n − 1 buffers and a preventive maintenance policy. The focus of the research is to obtain a better trade-off between the buffer level ...

We present an optimization problem that arises in the context of system-design and the reenement of high-level speciications. We present a graph-based formalization of the problem, thus deening a new optimization problem, which we call Minimum Configuration. We prove Minimum Configuration to be NP-hard and also hard to approximate , i.e. not to be in APX. These results are obtained by reduction...

Sparse signal recovery from a small number of random measurements is a well known NP-hard to solve combinatorial optimization problem, with important applications in signal and image processing. The standard approach to the sparse signal recovery problem is based on the basis pursuit method. This approach requires the solution of a large convex optimization problem, and therefore suffers from h...

in this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. machine as server and part as customer are assumed where servers should service to customers. since, the cell formation problem is np-hard, therefore, deterministic methods need a long time to solve this model. in this study...

When variables of constraints of an 0−1 Integer Linear Program (ILP) is replaced by a weaker constraint such that each variable belongs to the interval [0, 1] (i.e. decimal) instead of fixed values {0, 1} (integers); then this relaxation generates a new Linear Programming problem. For example, the constraint xi ∈ {0, 1} becomes 0 ≤ xi ≤ 1. This relaxation technique is useful to convert an NP-ha...