Supply networks (SN) must maintain operations and connectedness under disruptions to remain competitive; this is referred to as SN resilience. Building a resilient SN is an underlying challenge in supply chain management. In this paper, SN resilience is examined from the complex network topology perspective to understand how supply chain managers construct resilient networks. The proposed growth model considers enterprises leaving the network, which previous studies have ignored. Considering the heterogeneous roles of enterprises, new metrics based on a new proposed sub-network concept are presented to evaluate resilience. Using a computer simulation, the resilience of the SN generated by the model proposed in this paper is compared with that of other models, and the results indicate that (i) the proposed model can be tuned to generate a desired resilient network; (ii) the proposed metrics capture the resilience requirements of the SN very well; (iii) the more uniform the distribution of the enterprises, the more resilient the corresponding SN; and (iv) the higher the values of α and β, the lower the SN resilience, and β affects the resilience more than α does.

Local disruptions can be propagated from one firm to another in a supply network (SN) and eventually influence the whole SN. Therefore, numerous studies on SN resilience considering the ripple effect have been reported recently. However, previous studies paid less attention to this phenomenon from a network structure perspective: if a firm is facing the risk of failure, then its partners may help it to mitigate the risk of failure by collaboration during the process of disruption propagation. Specifically, how SN structures (e.g. characterised by different scaling exponents) and other parameters (e.g. redundancy) influence the effectiveness of collaboration on improving SN resilience considering the ripple effect is not clear. Accordingly, we propose a ripple effect with collaboration (REC) model to consider the aforementioned phenomenon. We also present three new SN resilience metrics to evaluate SN resilience. Then, using both generated (by a novel SN generating model) and real-life SNs, we simulate the SN resilience considering REC under random and targeted disruptions. Our results demonstrate that the effectiveness of collaboration can be affected by SN structures and other parameters, and collaboration can even negatively affect SN resilience in some cases. We also summarise managerial implications and give future research directions.

A genetic algorithm (GA) cannot always avoid premature convergence, and multi-population is usually used to overcome this limitation by dividing the population into several sub-populations (sub-population number) with the same number of individuals (sub-population size). In previous research, the questions of how a network structure composed of sub-populations affects the propagation rate of advantageous genes among sub-populations and how it affects the performance of GA have always been ignored. Therefore, we first propose a multi-population GA with an ER network (MPGA-ER). Then, by using the flexible job shop scheduling problem (FJSP) as an example and considering the total individual number (TIN), we study how the sub-population number and size and the propagation rate of advantageous genes affect the performance of MPGA-ER, wherein the performance is evaluated by the average optimal value and success rate based on TIN. The simulation results indicate the following regarding the performance of MPGA-ER: (i) performance shows considerable improvement compared with that of traditional GA; (ii) for an increase in the sub-population number for a certain TIN, the performance first increases slowly, and then decreases rapidly; (iii) for an increase in the sub-population size for a certain TIN, the performance of MPGA-ER first increases rapidly and then tends to remain stable; and (iv) with an increase in the propagation rate of advantageous genes, the performance first increases rapidly and then decreases slowly. Finally, we use a parameter-optimized MPGA-ER to solve for more FJSP instances and demonstrate its effectiveness by comparing it with that of other algorithms proposed in other studies.

In a complex environment, how to construct a resilient supply chain network (SCN) that can resist disruptions is a problem of great concern in supply chain management. In order to provide some insights for constructing a resilient SCN, this paper studies the influence of network structures on SCN resilience from the perspective of complex networks. Considering the exit and reselection of enterprises that have been ignored in previous studies, we propose a new SCN model in which nodes are connected with each other based on degree, fitness and distance. Subsequently, different disruption scenarios are simulated and the resilience of the SCN generated by the proposed model is compared with that of previous models. The simulation results show that the SCN generated by the proposed model is resilient to random disruptions, but vulnerable to targeted disruptions. In particular, the resilience of the SCN will be seriously affected when the strong-strong alliance is broken. Through the research on the influence of the parameters (e.g., $\alpha $ , $\beta $ and $f$ ) of the proposed model on SCN resilience, we find that $\alpha $ has a significant impact on SCN resilience. The larger the value of $\alpha $ is, the worse the resilience of the corresponding SCN is. The parameter $\beta $ has a slight effect on SCN resilience, and the more uniform the distribution of $f$ in the network is, the better the resilience of the corresponding SCN is. This study may contribute to the design and resilience optimization of SCNs.

There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1–S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP.