Object-Oriented Class Library for Resource Allocation Problems

2015 ◽  
Vol 799-800 ◽  
pp. 1149-1153 ◽  
Author(s):  
Vladislav A. Chekanin ◽  
Alexander V. Chekanin
Keyword(s):  
Resource Allocation ◽  
Optimization Problems ◽  
Object Oriented ◽  
Numerical Control ◽  
Large Set ◽  
Scheduling Problems ◽  
Packing Problems ◽  
Class Library ◽  
Applied Software ◽  
Allocation Problems

The object-oriented class library designed for solving various optimization problems of resource allocation, including problems of cutting materials and any dimensional packing problems, is described in this paper. The class library enables obtaining of suboptimal solutions of NP-completed resource allocation problems using standard evolutionary and modified heuristic optimization algorithms. The developed class library can be used in creation of an applied software for a wide class of optimization problems, including problems of resource allocation in storage systems and logistics, problems of cutting materials on machine tools with numerical control, scheduling problems and a large set of other practical problems.

scholarly journals ADAPTIVE HARMONY SEARCH FOR OPTIMIZING CONSTRAINED RESOURCE ALLOCATION PROBLEM

2018 ◽  
pp. 260-269
Author(s):  
Amol C. Adamuthe ◽  
Tushar R. Nitave
Keyword(s):  
Resource Allocation ◽  
Optimization Problems ◽  
Harmony Search ◽  
Population Diversity ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Multi Objective ◽  
Wide Range ◽  
Allocation Problems ◽  
Population Based Algorithm

Resource Allocation problem is finding the optimal assignment of finite available resources to tasks or users. Resource allocation problems refer to a wide range of applications such as production, supply chain management, transportation, ICT technologies, etc. Resource allocation problems are NP-hard in nature where the objective is to find the optimal allocations satisfying given constraints. Harmony search (HS) algorithm is a meta-heuristic population based algorithm found good for solving different optimization problems. This paper presents adaptive harmony search (AHS) for solving one-dimensional bin packing problem (BPP) and multi-objective virtual machine placement problem (VMP). The proposed real coded solution representation supports partial constraint satisfaction. Adaptive pitch adjustment rate (PAR) based on population diversity improves the performance of harmony search algorithm. Results show that proposed HS gives optimal solution for 50 BPP instances with 100 % success rate. The performance reduced for large instances of BPP. The proposed weighted AHS for multi objective VMP problem gives better results than genetic algorithm.

scholarly journals A Constraint-Based Declarative Programming Framework for Scheduling and Resource Allocation Problems

2019 ◽  
Vol 06 (01) ◽  
pp. 69-90
Author(s):  
Jarosław Wikarek ◽  
Paweł Sitek
Keyword(s):  
Resource Allocation ◽  
Human Resources Management ◽  
Decision Makers ◽  
Declarative Programming ◽  
Scheduling Problems ◽  
Production Logistics ◽  
Programming Framework ◽  
Allocation Problems ◽  
Execution Mode ◽  
Resource Constrained Scheduling Problem

Scheduling and resource allocation problems are widespread in many areas of today’s technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, project and human resources management, services, etc. The literature (problem classification, scheduling and resource allocation models, solutions) is vast and exhaustive. In practice, however, classical scheduling problems with fixed structures and standard constraints (precedence, disjoint, etc.) are rare. Practical scheduling problems include also logical and nonlinear constraints, and they use nonstandard criteria of schedule evaluations. Indeed, in many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as general and/or specific questions. Thus, there is a need to develop a programming framework that will facilitate the modeling and solving of a variety of diverse scheduling problems. The framework should be able to (a) model any types of constraints, (b) ask questions/criteria relating to the schedule execution mode and (c) be highly effective in finding solutions (schedule development). This paper proposes such a constraint-based declarative programming framework for modeling and solving scheduling problems which satisfies the assumptions above. It was built with the Constraint Logic Programming (CLP) environment and supported with Mathematical Programming (MP). The functionality and effectiveness of this framework are presented with the use of an illustrative example for the resource-constrained scheduling problem with additional resources.

Energy Landscapes of Atomic Clusters as Black Box Optimization Benchmarks

2012 ◽  
Vol 20 (4) ◽  
pp. 543-573 ◽  
Author(s):  
C. L. Müller ◽  
I. F. Sbalzarini
Keyword(s):  
Optimization Problems ◽  
Geometry Optimization ◽  
Black Box ◽  
Atomic Clusters ◽  
Pairwise Distance ◽  
Large Set ◽  
Packing Problems ◽  
Problem Class ◽  
Cluster Optimization ◽  
Problem Instances

We present the energy minimization of atomic clusters as a promising problem class for continuous black box optimization benchmarks. Finding the arrangement of atoms that minimizes a given potential energy is a specific instance of the more general class of geometry optimization or packing problems, which are generally NP-complete. Atomic clusters are a well-studied subject in physics and chemistry. From the large set of available cluster optimization problems, we propose two specific instances: Cohn-Kumar clusters and Lennard-Jones clusters. The potential energies of these clusters are governed by distance-dependent pairwise interaction potentials. The resulting collection of landscapes is composed of smooth and rugged single-funnel topologies, as well as tunable double-funnel topologies. In addition, all problems possess a feature that is not covered by the synthetic functions in current black box optimization test suites: isospectral symmetry. This property implies that any atomic arrangement is uniquely defined by the pairwise distance spectrum, rather than the absolute atomic positions. We hence suggest that the presented problem instances should be included in black box optimization benchmark suites.

scholarly journals Empowering the configuration-IP: new PTAS results for scheduling with setup times

2021 ◽  
Author(s):  
Klaus Jansen ◽  
Kim-Manuel Klein ◽  
Marten Maack ◽  
Malin Rau
Keyword(s):  
Approximation Scheme ◽  
Constant Factor ◽  
Setup Times ◽  
Linear Programs ◽  
Scheduling Problems ◽  
Design Of Algorithms ◽  
Packing Problems ◽  
Integer Linear Programs ◽  
One Machine ◽  
Target Locations

AbstractInteger linear programs of configurations, or configuration IPs, are a classical tool in the design of algorithms for scheduling and packing problems where a set of items has to be placed in multiple target locations. Herein, a configuration describes a possible placement on one of the target locations, and the IP is used to choose suitable configurations covering the items. We give an augmented IP formulation, which we call the module configuration IP. It can be described within the framework of n-fold integer programming and, therefore, be solved efficiently. As an application, we consider scheduling problems with setup times in which a set of jobs has to be scheduled on a set of identical machines with the objective of minimizing the makespan. For instance, we investigate the case that jobs can be split and scheduled on multiple machines. However, before a part of a job can be processed, an uninterrupted setup depending on the job has to be paid. For both of the variants that jobs can be executed in parallel or not, we obtain an efficient polynomial time approximation scheme (EPTAS) of running time $$f(1/\varepsilon )\cdot \mathrm {poly}(|I|)$$ f ( 1 / ε ) · poly ( | I | ) . Previously, only constant factor approximations of 5/3 and $$4/3 + \varepsilon $$ 4 / 3 + ε , respectively, were known. Furthermore, we present an EPTAS for a problem where classes of (non-splittable) jobs are given, and a setup has to be paid for each class of jobs being executed on one machine.

Using AHP for resource allocation problems

1995 ◽  
Vol 80 (2) ◽  
pp. 410-417 ◽  
Author(s):  
R. Ramanathan ◽  
L.S. Ganesh
Keyword(s):  
Resource Allocation ◽  
Allocation Problems
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