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The purpose of this paper and its companion (Part II: multi‐silo supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In tis part, the paper aims to devote attention to single silo and two‐silo supply chains. It also aims to discuss three models. The first model is based on the winebottling real‐world system and exposes complexities of a single operational component of the supply chain. The second model extends it to two components: production and distribution. The last system is a real‐world implementation of the two‐component supply chain.

Evolutionary approach is proposed for a single component problem. The two‐component experimental supply chain is addressed by the algorithm based on cooperative coevolution. The final problem of steel sheet production is tackled with the evolutionary algorithm.

The proposed systems produce solutions better than solutions proposed by human experts and in a much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The proposed systems are in the production use.

The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.

Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.

The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.

Cities evolved into quite complex urban systems. The rigid management process must reflect the complexity of the current political, social, and economic environment. With the vast city growth, citizens experience new difficulties – traffic congestion, pollution, immigration, overcrowding, and inadequate services.

In our research, we analyze problems and benefits that occur with the growing complexity and offer a new concept considering every city as a live and constantly developing complex adaptive system of many participants and actors that operate in an uncertain environment. These actors (residents, businesses, transport, energy, water supply providers, entertainment, and others) are the main elements of city life.

The new concept of “Smart City 5.0” is based on a previously developed model of Smart City 4.0 (compared with Industry 4.0) and implements the Urban Digital Ecosystem, where every element can be represented by a smart agent operating on its behalf. It is shown that smart services can interact vertically and horizontally in the proposed ecosystem, supporting competition and cooperation behavior based on specialized network protocols for balancing the conflicting interests of different city actors.

The chapter describes the design principles and the general architecture of the Urban Digital Ecosystem, including the basic agent of smart service, protocols of the agent’s negotiation, the architecture, and basic principles Smart City knowledge base.

The developed evolutionary methodology of implementation will ensure a minimum of disruptions to city services during its transformation into an urban ecosystem to harmoniously balance all spheres of life and the contradictory interests of different city actors.

The purpose of this paper is to tackle a satellite module layout design problem (SMLDP). As a complex engineering layout and combinatorial optimization problem, SMLDP cannot be solved effectively by traditional exact methods. Although evolutionary algorithms (EAs) have shown some promise of tackling SMLDP in previous work, the solution quality and computational efficiency still pose a challenge. This paper aims to address these two issues.

Scatter search (SS) and a cooperative co‐evolutionary architecture are integrated to form a new approach called a cooperative co‐evolutionary scatter search (CCSS). The cooperative co‐evolutionary architecture is characterized by the decomposition and cooperation for dealing with complex engineering problems. SS is a flexible meta‐heuristic method that can effectively solve the combinatorial optimization problems. Designing the elements of SS is context‐dependent. Considering the characteristics of SMLDP, our work focuses on two folds: the diversification method, and the reference set update method. The diversification method is built on the method of coordinate transformation and the controlled randomness. The reference set is updated by the static method on the basis of two dissimilarities. Two test problems for circles packing illustrated the capacity of SS. However, when solving SMLDP, SS shows some limitations in the computational time and quality. This study adopts divide‐conquer‐coordination strategy to decompose SMLDP into several layout sub‐problems. Then CCSS is applied to cooperatively solve these sub‐problems. The experimental results illustrate the capability of the proposed approach in tackling the complex problem with less computational effort.

Applying CCSS to SMLDP can obtain satisfying solutions in terms of quality and computational efficiency. This contrasts with the limiting experimental results of SMLDP with some approaches (including modified SS).

A new CCSS is proposed to provide an effective and efficient way of solving SMLDP. Some elements of SS are improved to address the layout problem. SMLDP is decomposed into several sub‐problems that can be solved cooperatively by CCSS after its characteristics are taken into consideration.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).

Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.

Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.

SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.

The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

To respond to customer needs and achieve customized manufacturing, the manufacturing industry, as represented by electronics assembly companies, has embarked on a path of business model transformation (customer to manufacturer [C2M]). The purpose of this paper is to examine the practical application of assembly line-Seru conversion in a Chinese electronics assembly company during the C2M transition.

To begin with, this paper proposed a production line improvement scheme suitable for the conversion of C2M manufacturing enterprise assembly line-Seru based on an analysis of the difficulties encountered in the existing production line of A company in China. Then, a mathematical model was presented for the minimum value of the makespan and the maximum workers’ expenditure between Serus. Finally, the SA-NSGA-II algorithm and the entropy-weight TOPSIS approach were used to determine the optimal scheme for Seru unit, batch, product type and worker distribution.

Seru production and multiskilled workers are more suited to the C2M business model. The most effective strategy for worker allocation can reduce the number of employees and makespan in Serus. Additionally, the performance of the SA-NSGA-II algorithm and the method of selecting the optimal solution from the Pareto solution by the entropy-weighted TOPSIS method is also demonstrated.

Through a detailed study of how to transform the production line, other companies can apply the methods outlined in this article to shorten the delivery time, make full use of the abilities of workers and assign workers to specific positions, thereby reducing the number of workers, workers’ expenditure and improving the balance rate of production lines.

Given the scarcity of studies on the production method of C2M-type firms in the prior literature, this paper examined the assembly line-Seru conversion problem with the goal of minimizing the makespan and worker expenditure. To address the NSGA-II algorithm’s insufficient convergence, the simulated annealing process is incorporated into the method, which improves the optimization performance.

The purpose of this paper is to improve the hydraulic efficiency without changing the overall dimension. The blade profile optimization design of the aero-centrifugal pump based on the biharmonic equation surrogate model has been studied.

First of all, Bezier curves and linear function are used to control the annular angle distribution and the stacking angle of blade profile under the MATLAB platform. Grid independence analysis has been studied to find the finest mesh scheme. After the precision comparison of test data and computation fluid dynamics 15 sets of design parameters are carried out as the boundary condition of the biharmonic equation. The efficiency surrogate model of the biharmonic equation is constructed via iteratively solving of a discrete difference equation. The other two surrogate models of response surface model (RSM) and radial basis function neural network surrogate model (RBFNNSM) are compared with the biharmonic equation surrogate model by the standard of modified complex correlation coefficient R² and root mean square deviation (RSME). Finally, the artificial fish swarm algorithm has been used to find the global optimal design parameters with the objective function of highest efficiency.

The results show that the design parameters code conversion method can reduce the number of optimization parameters from five to three, makes the design space become a cube, and compared with RSM and RBFNNSM, the biharmonic equation surrogate model has higher precision with R² is 0.8958, RSME is 0.1382. The final optimum result of AFSA is at the point of [1 −1 −1]. The internal flow field analysis shows that after optimization the outlet relative velocity becomes more uniform and the wake effect has been significantly decreased. The hydraulic efficiency of the optimized pump is about 59.45 per cent increasing 5.4 per cent compared with a prototype pump.

This study developed a new method to optimize the design parameters of aero-centrifugal pump impeller based on biharmonic equation surrogate model, which had a good agreement with experimental values within just 15 sets of the original design. The optimization results shows that the method can improve the hydraulic efficiency significantly.

The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices.

DE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well‐known benchmarks and domain‐specific applications.

It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.

The increased speed of convergence may come at the price of a somewhat decreased robustness. However, such behavior is justified by the CPU time constraints under which the optimization has to be performed.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

This paper explorers the combined use of many of the most recent and successful algorithmic improvements to DE and applies them to both single and multiobjective problems.

This study aims to understand the coevolution and coexistence of cooperation and competition in the interorganizational collaboration of management consulting firms (MCFs) in India.

The narrative inquiry method was applied to understand the central phenomenon. The narrative inquiry method was found pertinent because the aim of the research was to inquire human-based phenomenon, especially life experiences, tensions, feeling, thought processes, emotions and personal puzzles. Narrations from 47 respondents from 32 MCFs from various consortiums were collected to make textual and phenomenal narrative inquiry. Finally, causal relationships were designed using the mapping method.

The study noticed coevolution and coexistence of cooperation and competition in the MCF consortiums. Cooperation was higher than the competition at the entry level, and the competition was higher than cooperation at the operational level of the consortium life cycle. Organizational side of coopetition was higher than human side of coopetition at the entry level, and human side of coopetition was higher than organizational side of coopetition at the operational level. A higher level of competition (than cooperation) pushes the consortium beyond the threshold level, creating a lesser value creation. Further higher level of competition (than cooperation) shoved the consortium beyond the injury limit, leading the consortium to collapse.

This study paid major attention on the human side and organizational side of coopetition from the life cycle perspective, but the findings and discussions concentrated more on entry level and operational level. The study, in fact, did not capture the status of coopetition at the termination phase of the consortium.

This study is one of the few studies that examined cooperation and competition as a single construct in interorganizational collaboration in the management consulting industry. This study is unique in two ways, one, examination from the human side of coopetition and organizational side of coopetition, and two, life cycle analysis of the consortium from the perspective of coopetition.