Machine Allocation Problem Research Articles

The Hybrid Flow Shop Scheduling With Special Time Constraints

A constraint satisfaction optimization model is established for just-in-time HFS scheduling problem with limited waiting time. Considering the problem’s complicated characteristic of having binary variables, this paper decomposes it into a Multiple Capacitated Flow Shop Scheduling (MCFS) problem and a machine allocation problem. During the process of solving the MCFS problem, a local search is embedded into the procedure of constraint satisfaction optimization so as to improve the convergence of the algorithm. Data experiments show that both the model and algorithm are feasible and effective. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5093

Virtual Machine Allocation in Cloud Computing Environment

Virtual machine allocation problem is one of the challenges in cloud computing environments, especially for the private cloud design. In this environment, each virtual machine is mapped unto the physical host in accordance with the available resource on the host machine. Specifically, quantifying the performance of scheduling and allocation policy on a Cloud infrastructure for different application and service models under varying performance metrics and system requirement is an extremely challenging and difficult problem to resolve. In this paper, the authors present a Virtual Computing Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A rule based mapping algorithm for Virtual Machines (VMs) which is formulated based on the principles of set theoretic is also presented. The algorithmic design is projected towards being able to automatically adapt the mapping between VMs and physical hosts’ resources. The paper, similarly presents a theoretical study and derivations of some performance evaluation metrics for the chosen mapping policies, these includes determining the context switching, waiting time, turnaround time, and response time for the proposed mapping algorithm.

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method.

Metaheuristics for the Integrated Machine Allocation and Layout Problem

The Integrated Machine Allocation and Layout Problem (IMALP) is the problem of assigning a set of machines (including machine replicas) to locations while assigning product flows to machines such that Material Handling Cost is minimised. A new mathematical formulation, a Tabu Search (TS) heuristic, and a Memetic Algorithm (MA) are presented for the IMALP. The algorithms were evaluated using a set of test problems available in the literature. TS and the MA obtained equal or better solutions for the dataset than previous techniques presented in the literature. More specifically, TS obtained better solutions in 47% of the instances, and MA improved the best known solution in 52.4% of the cases. As a result, MA out-performed TS with respect to solution quality and computation time.

Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings

We present a comprehensive model for designing a cellular manufacturing system. The model bridges several known problems in that it integrates the cell formation problem, the machine allocation problem, and the part routing problem. Multiple process plans for each part and multiple routing alternatives for each of those process plans are considered. The part demands can be satisfied from internal production or through outsourcing. Machines have limited capacities. We propose a solution methodology based on a combination of a genetic algorithm and large-scale optimization techniques. A computational study is conducted to evaluate the viability of our approach for solving large scale problems. A limited computational experiment involving smaller problems, that are special cases of our model, is also conducted to compare our solution approach with existing models.

The integrated machine allocation and layout problem

Cellular manufacturing has received a considerable amount of attention in the research literature as an approach for improving the performance of manufacturing facilities. However, recent studies have shown that cellular layouts are not always superior to the traditional functional machine layout. We propose a model that does not require the machines to be placed in a functional layout or in a cellular arrangement, but allows the material flow requirements to dictate the machine placement. The model is formulated as an aggregation of the quadratic assignment problem and several network flow problems coupled with linear side constraints. A mixed integer program is presented to find the optimal solution for small problems, and heuristics are developed to solve larger problems. Computational results evaluating the quality of the solution methodologies are also presented.

Tool Machine Allocation Problem in a Cellular Manufacturing System

Abstract In this paper the tool allocation problem of a flexible manufacturing system is discussed. Two algorithms are developed and compared with one another in the presence of the “part move” strategy which moves the item to the workstation having the necessary tooling. The tools can not be shared between the machines. The aim is to find the appropriate cutting tool allocation which minimises the part flow between the CNC centers, i.e. the total distance travelled by the material handling system.

Improved Algorithms for Machine Allocation in Manufacturing Systems

In this paper we present two algorithms for a machine allocation problem occurring in manufacturing systems. For the two algorithms presented we prove worst-case performance ratios of 2 and 3/2, respectively. The machine allocation problem we consider is a general convex resource allocation problem, which makes the algorithms applicable to a variety of resource allocation problems. Numerical results are presented for two real-life manufacturing systems.

Machine allocation problems in manufacturing networks

In this paper we discuss two server (machine) allocation problems that occur in manufacturing networks. The manufacturing network is modelled as an open network of queues. The server allocation problems are solved by means of a marginal analysis scheme. We show that for the first problem our algorithm generates undominated allocations. Furthermore, the algorithm provides us with bounds to check how close the allocation generated is to the optimal allocation. In the second problem the algorithm presented generates optimal allocations within time bounded by a polynomial function in the size of the network.