A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1483-1499 ◽  
Author(s):  
Christopher Garcia
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Greedy Heuristic ◽  
Planning Problem ◽  
Large Set ◽  
Effective Solution ◽  
Heavy Industry ◽  
Content Type ◽  
Wide Range

Purpose – The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem. Design/methodology/approach – The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required. Findings – The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions. Practical implications – The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement. Originality/value – This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

scholarly journals Learning Sparse Log-Ratios for High-Throughput Sequencing Data

2021 ◽  
Author(s):  
Elliott Gordon-Rodriguez ◽  
Thomas P Quinn ◽  
John P Cunningham
Keyword(s):  
Combinatorial Optimization ◽  
High Throughput ◽  
Optimization Problem ◽  
High Throughput Sequencing ◽  
Compositional Data ◽  
Learning Algorithm ◽  
Combinatorial Optimization Problem ◽  
Supplementary Information ◽  
Wide Range ◽  
Log Ratio

Abstract Motivation The automatic discovery of sparse biomarkers that are associated with an outcome of interest is a central goal of bioinformatics. In the context of high-throughput sequencing (HTS) data, and compositional data (CoDa) more generally, an important class of biomarkers are the log-ratios between the input variables. However, identifying predictive log-ratio biomarkers from HTS data is a combinatorial optimization problem, which is computationally challenging. Existing methods are slow to run and scale poorly with the dimension of the input, which has limited their application to low- and moderate-dimensional metagenomic datasets. Results Building on recent advances from the field of deep learning, we present CoDaCoRe, a novel learning algorithm that identifies sparse, interpretable, and predictive log-ratio biomarkers. Our algorithm exploits a continuous relaxation to approximate the underlying combinatorial optimization problem. This relaxation can then be optimized efficiently using the modern ML toolbox, in particular, gradient descent. As a result, CoDaCoRe runs several orders of magnitude faster than competing methods, all while achieving state-of-the-art performance in terms of predictive accuracy and sparsity. We verify the outperformance of CoDaCoRe across a wide range of microbiome, metabolite, and microRNA benchmark datasets, as well as a particularly high-dimensional dataset that is outright computationally intractable for existing sparse log-ratio selection methods. Availability The CoDaCoRe package is available at https://github.com/egr95/R-codacore. Code and instructions for reproducing our results is available at https://github.com/cunningham-lab/codacore. Supplementary information Supplementary data are available at Bioinformatics online.

Inverse version of the kth maximization combinatorial optimization problem

2018 ◽  
Vol 54(5) ◽  
pp. 72
Author(s):  
Quoc, H.D. ◽  
Kien, N.T. ◽  
Thuy, T.T.C. ◽  
Hai, L.H. ◽  
Thanh, V.N.
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem

scholarly journals Supplier-initiating risk management behaviour and supply-side resilience: the effects of interpersonal relationships and dependence asymmetry in buyer-supplier relationships

2020 ◽  
Vol 40 (7/8) ◽  
pp. 971-995
Author(s):  
Yiyi Fan ◽  
Mark Stevenson ◽  
Fang Li
Keyword(s):  
Risk Management ◽  
Interpersonal Relationships ◽  
Supply Side ◽  
Large Set ◽  
Supplier Relationships ◽  
Content Type ◽  
Management Behaviour ◽  
Wide Range ◽  
Dependence Asymmetry ◽  
The Relationship

PurposeThe aim of the study is to explore how two dimensions of interpersonal relationships (i.e. size and range of relationships) affect supplier-initiating risk management behaviours (SIRMB) and supply-side resilience. Further, the study aims to explore the moderating role of dependence asymmetry.Design/methodology/approachNine hypotheses are tested based on a moderated mediation analysis of survey data from 247 manufacturing firms in China. The data are validated using a subset of 57 attentive secondary respondents and archival data.FindingsSIRMB positively relates to supply-side resilience. Further, SIRMB mediates the positive relationship between range and supply-side resilience, and this relationship is stronger at lower levels of dependence asymmetry. Yet, although dependence asymmetry positively moderates the relationship between range and SIRMB, it negatively moderates the relationship between size and SIRMB. We did not, however, find evidence that size has a conditional indirect effect on supply-side resilience through SIRMB.Practical implicationsManagers in buying firms can incentivise SIRMB to enhance supply-side resilience by developing a diverse rather than a large set of interpersonal relationships with a supplier. This might include allocating particular employees with a wide range of contacts within a supplier to that relationship, while it may be necessary to adopt different networking strategies for different supplier relationships. Firms in a highly asymmetrical relationship may seek to raise supplier expectations about the necessity to initiate risk management behaviour or look to change the dynamic of the relationship by managing contracts for fairness.Originality/valueNew knowledge on SIRMB as a mediating variable underpinning the relationship between interpersonal relationships and supply-side resilience is provided; and empirical evidence on the opposing moderation effect of dependence asymmetry is presented.

scholarly journals ANALYSIS AND SYNTHESIS OF ENHANCED ANT COLONY OPTIMIZATION WITH THE TRADITIONAL ANT COLONY OPTIMIZATION TO SOLVE TRAVELLING SALES PERSON PROBLEM

2011 ◽  
Vol 1 (1) ◽  
pp. 88-92
Author(s):  
Pallavi Arora ◽  
Harjeet Kaur ◽  
Prateek Agrawal
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Traditional Approach ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Analysis And Synthesis ◽  
Heuristic Technique ◽  
Travelling Salesperson Problem

Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.

Ant Colony Optimization and Multiple Knapsack Problem

2007 ◽  
pp. 498-509 ◽  
Author(s):  
S. Fidanova
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Multiple Knapsack Problem ◽  
Constraint Problem ◽  
Multiple Knapsack ◽  
Ant Colony Optimization Algorithms

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.

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