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Simplifying the Complex: Modeling and Analysis for System Design and...
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Simplifying the Complex: Modeling and Analysis for System Design and Management

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems and Cybernetics".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 27827

Special Issue Editors

Dr. Mahmoud Efatmaneshnik

E-Mail Website
Guest Editor
Master of Systems Engineering Program, University of South Australia, Adelaide, SA 5000, Australia
Interests: systems engineering; capability management; logistics support; complexity science; decision making
Special Issues, Collections and Topics in MDPI journals
Dr. Shraga Shoval

E-Mail Website
Guest Editor
Department of Industrial Engineering & Management, Ariel University, Ariel 4076414, Israel
Interests: autonomous mobile systems; computer-integrated manufacturing; systems engineering
Dr. Larissa Statsenko

E-Mail Website
Guest Editor
UniSA STEM, University of South Australia, Adelaide, SA 5000, Australia
Interests: complex project management and supply chain management; organisational change; supply chain capability development; value chain integration

Special Issue Information

Dear Colleagues,

The complexity of modern-day systems is increasing at a rapid pace. Managing their overwhelming amount of complexity in a constructive and positive way is a necessity. Accordingly, developing tools, methods, and models of complexity is a moral imperative for the scientific community. This is, however, a very broad objective that finds its meaning in specific contexts. The context of this Special Issue is system design and management. Broadly, system design involves modeling, analysis, synthesis, and optimization. Complexity is present in each of these stages, which require specific methods for managing the objective and subjective complexities of these processes. Novel statistical–dynamical models of complexity can assist in simplifying subjective complexities and providing simple descriptions that can be used as heuristics to understand and manage systems, albeit in limited contexts and scopes.

This Special Issue invites scientific contributions proposing new innovative and original approaches for modeling complexity for a better and more simplified understanding of systems for the purpose of the design and management of such systems. The call especially targets advancements in research and practices in the field of complexity management, complexity modeling, and heuristics for the management of complexity, particularly in a design context. This Special Issue aims to provide an opportunity for academics and systems engineering practitioners to share their theoretical and practical knowledge as well as their findings in the field, with the aim of disseminating novel and state-of-the-art ideas. This Special Issue particularly looks forward to articles presenting, among other aspects:

  • Mathematical models of complexity, including network-oriented, dynamical, statistical, and mixed-model models.
  • Models of design processes with a particular emphasis on complexity management.
  • Complexity management tools and methodologies in design and systems engineering contexts.
  • Complex, interacting, and conflicting issues in design and management, such as the interaction of various system -ilities, such as affordability, flexibility, evolvability, resilience, etc.
  • Mathematical models of systems utility that tackle issues such as function–size trade-off.
  • Application of artificial intelligence in simplifying design and management complexity.
  • Decision simplification in the design and management of complex systems.

Dr. Mahmoud Efatmaneshnik
Dr. Shraga Shoval
Dr. Larissa Statsenko
Guest Editors

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. Systems 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 2400 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

  • complexity modeling
  • complexity management
  • systems analysis for complexity reduction
  • simplifying design processes
  • simplifying decision making

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Published Papers (12 papers)

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Research

24 pages, 4940 KiB  
Article
Lifecycle Value Sustainment and Planning Mission Upgrades for Complex Systems: The Case of Warships
by Dylan Dwyer and Mahmoud Efatmaneshnik
Systems 2023, 11(4), 183; https://doi.org/10.3390/systems11040183 - 2 Apr 2023
Viewed by 1748
Abstract
Changeability analysis methods primarily assist with formulating a response to uncertain and new requirements from various system stakeholders and include asset management issues such as modelling lifecycle path dependency. Epoch-era networks proved to be an effective tool for managing the evolving requirements of [...] Read more.
Changeability analysis methods primarily assist with formulating a response to uncertain and new requirements from various system stakeholders and include asset management issues such as modelling lifecycle path dependency. Epoch-era networks proved to be an effective tool for managing the evolving requirements of a capability system, ensuring sustained value through life. Over the life of a system, stakeholders are faced with countless options to change their capability systems to sustain value, which is path dependent and can greatly impact the scope of decisions available later in life. This paper introduces and demonstrates the application of a revised epoch-era network approach to explore many potential lifecycle paths, along with utility vs. expense strategies, demonstrated through an example of a military frigate subject to evolving requirements. Results indicated the future limitations to sustaining value if the largest and most capable technology upgrades are selected too early in life. The two best lifecycle paths from different strategies were compared to understand the utility/expense trade-offs for the most optimal frigate upgrade trajectory. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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16 pages, 1662 KiB  
Article
Joint Optimization of Order Allocation and Rack Selection in the “Parts-to-Picker” Picking System Considering Multiple Stations Workload Balance
by Fang Wang, Yu Wang and Daofang Chang
Systems 2023, 11(4), 179; https://doi.org/10.3390/systems11040179 - 29 Mar 2023
Viewed by 2204
Abstract
E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately [...] Read more.
E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a “parts-to-picker” order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks’ total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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19 pages, 443 KiB  
Article
Two Due-Date Assignment Scheduling with Location-Dependent Weights and a Deteriorating Maintenance Activity
by Wei Wu, Dan-Yang Lv and Ji-Bo Wang
Systems 2023, 11(3), 150; https://doi.org/10.3390/systems11030150 - 15 Mar 2023
Cited by 9 | Viewed by 1388
Abstract
This paper investigates single-machine scheduling with a deteriorating maintenance activity, where the processing time of a job depends on whether it is handled before or after the maintenance activity. Under common and slack due date assignments, the aim is to find the optimal [...] Read more.
This paper investigates single-machine scheduling with a deteriorating maintenance activity, where the processing time of a job depends on whether it is handled before or after the maintenance activity. Under common and slack due date assignments, the aim is to find the optimal job schedule, position of the maintenance activity, and optimal value of the common due date (flow-allowance) so that the linear weighted sum of earliness, tardiness and common due date (flow-allowance) value is minimized, where the weights are location-dependent (position-dependent) weights. Through a series of optimal properties, a polynomial time algorithm is proposed and it is then proven that the problem is polynomially solvable. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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26 pages, 7722 KiB  
Article
A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards
by Aso M. Aladdin and Tarik A. Rashid
Systems 2023, 11(3), 144; https://doi.org/10.3390/systems11030144 - 9 Mar 2023
Cited by 4 | Viewed by 2522
Abstract
The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select [...] Read more.
The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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18 pages, 955 KiB  
Article
Probabilistic Modelling of System Capabilities in Operations
by Vesa Kuikka
Systems 2023, 11(3), 115; https://doi.org/10.3390/systems11030115 - 23 Feb 2023
Cited by 4 | Viewed by 1490
Abstract
We present a probabilistic method for simplifying the complexities in evaluating high-level capabilities. The method is demonstrated by using questionnaire data from a technology forecasting project. Our model defines capability as the probability of a successful operation. The model maps the actual observable [...] Read more.
We present a probabilistic method for simplifying the complexities in evaluating high-level capabilities. The method is demonstrated by using questionnaire data from a technology forecasting project. Our model defines capability as the probability of a successful operation. The model maps the actual observable capabilities to system capabilities. These theoretical quantities are used in calculating observable capability values for system combinations that are not directly evaluated as explanatory variables in the model. From the users’ point of view, the method is easy to use because the number of evaluated parameter values is minimal compared with many other methods. The model is most suited to applications where the resilience and effectiveness of systems are central factors in design and operations. Resilience is achieved by using alternative systems that produce similar capabilities. We study the limited use of alternative systems and their capabilities. We present experiments of model variations and discuss how to perform the model’s built-in consistency checks. The proposed method can be used in various applications such as comparing military system capabilities, technological investments, medical treatments and public education. Our method can add a novel view for understanding and identifying interrelations between systems’ operations. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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12 pages, 860 KiB  
Article
Simplifying the Complexity in the Problem of Choosing the Best Private-Sector Partner
by Peiyao Qiu, Ali Sorourkhah, Nasreen Kausar, Tonguc Cagin and Seyyed Ahmad Edalatpanah
Systems 2023, 11(2), 80; https://doi.org/10.3390/systems11020080 - 4 Feb 2023
Cited by 15 | Viewed by 2125
Abstract
Governments frequently partner with the private sector to provide infrastructure and public services. These cooperations, known as public–private partnerships (PPPs), have often failed. Sometimes, due to the problem’s complexity, the public sector cannot choose the right partner for these projects, which is one [...] Read more.
Governments frequently partner with the private sector to provide infrastructure and public services. These cooperations, known as public–private partnerships (PPPs), have often failed. Sometimes, due to the problem’s complexity, the public sector cannot choose the right partner for these projects, which is one of the main reasons for failures. Complexity in such problems is associated with a large number of indicators, imprecise judgments of decision-makers or problem owners, and the unpredictability of the environment (under conditions of uncertainty). Therefore, presenting a simplified algorithm for this complicated process is the primary goal of the current research so that it can consider the problem’s various dimensions. While many researchers address the critical risk factors (CRFs) and others focus on key performance indicators (KPIs), this research has considered both CRFs and KPIs to choose the best private-sector partner. In addition, we used single-valued neutrosophic sets (SVNSs) to collect decision-makers’ views, which can handle ambiguous, incomplete, or imprecise information. Next, by defining the ideal alternative and using the similarity measure, we specified the ranks of the alternative. Additionally, to face the uncertain environment, we examined the performance of options in four future scenarios. The steps of the proposed algorithm are explained in the form of a numerical example. The results of this research showed that by employing a simple algorithm, even people who do not have significant operations research knowledge could choose the best option by paying attention to the dimensions of the problem complexity. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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19 pages, 1727 KiB  
Article
Socio-Technical and Political Complexities: Findings from Two Case Studies of Large IT Project-Based Organizations
by Navid Ahmadi Eftekhari, Saba Mani, Javad Bakhshi, Larissa Statsenko and Leila Moslemi Naeni
Systems 2022, 10(6), 244; https://doi.org/10.3390/systems10060244 - 3 Dec 2022
Cited by 3 | Viewed by 2602
Abstract
Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This [...] Read more.
Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This study aims to identify and prioritize factors contributing to complexity in IT projects by reporting two case studies conducted on large IT organizations. The literature on project complexity informed and guided this exploratory research. The data were collected through 21 semi-structured interviews and analyzed by applying open and axial coding content analysis. Underpinned by complexity theories, 19 factors contributing to the complexity of IT projects were identified, and their importance was highlighted using the Friedman test. The top five factors contributing to IT project complexity were identified as follows: the diversity of stakeholders; technological newness of the project; conflicting goals of stakeholders; variety of product sub-systems and components; and uncertainty of project objectives. This study’s findings contribute to the project management literature and inform practitioners about how to achieve more effective management of complex IT projects. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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14 pages, 615 KiB  
Article
Research on Innovation Capability of Regional Innovation System Based on Fuzzy-Set Qualitative Comparative Analysis: Evidence from China
by Zhenyu Huang
Systems 2022, 10(6), 220; https://doi.org/10.3390/systems10060220 - 16 Nov 2022
Cited by 4 | Viewed by 1709
Abstract
Building regional innovation systems (RISs) has become an important measure for China to implement an innovation-driven development strategy, but a moderately complex way to describe the characteristics of the RISs is needed for aiding the implementation of this strategy. Based on the subject-resource-environment [...] Read more.
Building regional innovation systems (RISs) has become an important measure for China to implement an innovation-driven development strategy, but a moderately complex way to describe the characteristics of the RISs is needed for aiding the implementation of this strategy. Based on the subject-resource-environment (SRE) framework with five secondary conditions, this study takes 31 regions in China as cases, and studies the innovation capability of the RISs by using the fuzzy-set qualitative comparative analysis (fsQCA) approach. The findings are as follows: (1) Different combinations of the five conditions have generated two high and one low innovation capability configurations of RISs. (2) The two configurations of high innovation capability are the independent-investment type and the independent-open type. (3) The configuration of low innovation capability is the core-resource-deficiency type. This study has simplified the complexity of the RISs to a certain extent and revealed that the matching effect among core conditions is the key to obtaining high innovation capability. The conclusions provide practical implications for the RISs in China to acquire appropriate innovation capabilities according to their resource endowment conditions. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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16 pages, 826 KiB  
Article
A Hybrid Hesitant Fuzzy Model for Healthcare Systems Ranking of European Countries
by Ahmet Aktas, Billur Ecer and Mehmet Kabak
Systems 2022, 10(6), 219; https://doi.org/10.3390/systems10060219 - 16 Nov 2022
Cited by 2 | Viewed by 1442
Abstract
Ranking several countries on a specific area may require the consideration of various factors simultaneously. To obtain a ranking of countries, the development of analytical approaches, which can aggregate opinions of a group of people on various criteria, is essential. The main aim [...] Read more.
Ranking several countries on a specific area may require the consideration of various factors simultaneously. To obtain a ranking of countries, the development of analytical approaches, which can aggregate opinions of a group of people on various criteria, is essential. The main aim of this study was to propose such a ranking approach for European countries in terms of healthcare services. To this end, a hybrid group decision-making model based on Hesitant Fuzzy Linguistic Terms Set (HFLTS) and Hesitant Fuzzy Technique of Order Preference by Similarity to Ideal Solution (HF-TOPSIS) is presented in this study. Importance degree of indicators were determined by the HFLTS-based group decision-making approach, and then HF-TOPSIS was used to obtain the rank of countries. According to the results obtained by the proposed model, Austria, Sweden and Finland are the best European countries in terms of healthcare services. Moreover, two comparative analyses, one for the utilization of different hesitant fuzzy distance measures in HF-TOPSIS and one for the ranking of countries obtained by utilizing TOPSIS, return some variations in country rankings. While Austria remained the best country for all distance measures in the hesitant fuzzy environment, Luxemburg was found to be the best for the deterministic case of TOPSIS. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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21 pages, 5286 KiB  
Article
Developing a Stochastic Two-Tier Architecture for Modelling Last-Mile Delivery and Implementing in Discrete-Event Simulation
by Zichong Lyu, Dirk Pons, Jiasen Chen and Yilei Zhang
Systems 2022, 10(6), 214; https://doi.org/10.3390/systems10060214 - 10 Nov 2022
Cited by 4 | Viewed by 2525
Abstract
Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. [...] Read more.
Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. This research aims to develop last-mile delivery architecture in DES and evaluate the credibility of the model. A two-tier architecture was proposed and integrated with a DES model to simulate freight operations. The geographic foundation of the model was determined using Geographic Information Systems (GIS), including identifying customer locations, finding cluster centres, and implementing Travelling Salesman Problem (TSP) simulation. This complex model was simplified to the two-tier architecture with stochastic distances, which is more amenable to DES models. The model was validated with truck GPS data. The originality of the work is the development of a novel and simple methodology for developing a logistics model for highly variable last-mile delivery. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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17 pages, 4310 KiB  
Article
Application of Sine Cosine Egret Swarm Optimization Algorithm in Gas Turbine Cooling System
by Tianyi Li, Yanmei Liu and Zhen Chen
Systems 2022, 10(6), 201; https://doi.org/10.3390/systems10060201 - 30 Oct 2022
Cited by 11 | Viewed by 1916
Abstract
Gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear, and uncertain MIMO system. In order to solve the control problem of pressure, flow, and temperature of the system, an intelligent approach is necessary and more appropriate. The current system control mainly [...] Read more.
Gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear, and uncertain MIMO system. In order to solve the control problem of pressure, flow, and temperature of the system, an intelligent approach is necessary and more appropriate. The current system control mainly depends on the experience of the staff, which exists problems such as high labor intensity, low work efficiency and low control accuracy. Lack of accurate models make parameters tune difficultly, and ordinary control methods are difficult to control complex gas turbine cooling system. In this paper, the system transfer function model is built based on the field data obtained under different working conditions and system identification method. The diagonal matrix decoupling method is used to weaken the correlation between variables and achieve independent control among variables. When optimizing the parameters of the controller, Sine Cosine Egret Swarm Optimization Algorithm is proposed. Egret Swarm Optimization Algorithm is composed of Sit-And-Wait strategy, random walk, and encirclement strategy. The sit-and-wait strategy is prone to premature convergence, which makes the optimized parameters unsuitable for gas turbine cooling system. Sine Cosine Algorithm is introduced to randomly use the sine-cosine function for the pseudo-gradient of the weights of the observation equation, thus expanding the search range of the population. Friedman tests prove that the deviation of SE-ESOA is within the allowable range. The results show that the result of Sine Cosine Egret Swarm Optimization Algorithm is more stable and accurate, and it is more suitable for gas turbine cooling system, which solve the pressure, flow, and temperature control problems of complex systems. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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19 pages, 3217 KiB  
Article
Project Manager Competencies for Dealing with Socio-Technical Complexity: A Grounded Theory Construction
by Navid Ahmadi Eftekhari, Saba Mani, Javad Bakhshi and Sahar Mani
Systems 2022, 10(5), 161; https://doi.org/10.3390/systems10050161 - 20 Sep 2022
Cited by 14 | Viewed by 4418
Abstract
An ongoing question is what constitutes the characteristics of a project manager. This is the subject of many studies. The characteristics, skills, abilities and knowledge of project managers—essential factors in a project’s success—describe their level of competency. This study aims to assess the [...] Read more.
An ongoing question is what constitutes the characteristics of a project manager. This is the subject of many studies. The characteristics, skills, abilities and knowledge of project managers—essential factors in a project’s success—describe their level of competency. This study aims to assess the relationship between project manager competencies and project complexity in the information technology (IT) sector. In total, 21 semi-structured interviews were conducted with senior practitioners associated with complex IT projects in the private and public sectors. All transcripts were analysed through grounded theory and content analysis, with experts approving the results. Our study identified 41 competencies within project complexity, with these grouped under the following 10 dimensions: project management (PM) knowledge; management skills; interpersonal skills and attributes; professionalism; expertise; emotional skills; contextual skills; influencing skills; team working; and cognitive skills. According to this research, leadership is the core competency of a project manager, while project management knowledge is the most essential of these competency dimensions. This study’s findings can assist both academics and practitioners in simplifying the complexity of projects and helping to achieve a project’s objectives. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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