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Algorithms
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Optimization Algorithms for Decision Support Systems
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Optimization Algorithms for Decision Support Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 3903

Special Issue Editor

Dr. Mingchang Chih

E-Mail Website
Guest Editor
Department of Business Administration, National Chung Hsing University, Taichung City 402, Taiwan
Interests: quantitative decision analysis; decision support and business intelligence; optimization algorithms; application probability and statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A decision support system is an information system that supports business or organizational decision-making activities. Optimization techniques are well suited to dealing with emerging topics in decision support systems. In real-world applications, the formulation of decision-making problems and utilization of optimization techniques to support decisions are particularly important. This is because the usage of optimization techniques is very useful in improving the quality of decision making and analysis. Therefore, the journal of Algorithms invites excellent works on emerging topics in optimization algorithms with implementation in decision support systems. The areas of interest may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems.

Finally, I would like to thank Associate Professor Yen-Deng Huang for assisting me with this Special Issue.

Dr. Mingchang Chih
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical programming
  • soft computing algorithm
  • meta-heuristic algorithm
  • knowledge-based algorithm
  • machine learning algorithm
  • statistical methodologies
  • simulation optimization

Published Papers (2 papers)

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26 pages, 678 KiB  
Article
Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization
by Adekunle Rotimi Adekoya and Mardé Helbig
Algorithms 2023, 16(11), 504; https://doi.org/10.3390/a16110504 - 30 Oct 2023
Cited by 1 | Viewed by 1205
Abstract
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with [...] Read more.
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM. Full article
(This article belongs to the Special Issue Optimization Algorithms for Decision Support Systems)
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27 pages, 10487 KiB  
Article
A Multi-Objective Tri-Level Algorithm for Hub-and-Spoke Network in Short Sea Shipping Transportation
by Panagiotis Farmakis, Athanasios Chassiakos and Stylianos Karatzas
Algorithms 2023, 16(8), 379; https://doi.org/10.3390/a16080379 - 7 Aug 2023
Viewed by 1755
Abstract
Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers [...] Read more.
Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain. Full article
(This article belongs to the Special Issue Optimization Algorithms for Decision Support Systems)
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