This study explores the impacts of participatory design (PD) on data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a dearth of research in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The paper provides a research agenda for future researchers as well as discussing best practices for organizations seeking to optimise their data driven decision-making processes in a participatory manner. The research also discussed the ethical implications of data-driven decision-making. Ultimately, this research advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.

Most small and medium scale enterprises in Ghana neglect very critical performance indicators in their bossiness. As a result most SMEs do not survive till their fifth anniversary. The purpose of this study is to examine the relationship between prevention focus, promotion focus, resilience, and SME performance in Ghana. Furthermore, we investigated the moderation effect of entrepreneurial learning and Entrepreneurial orientation on the relationship between entrepreneurial resilience and SME performance in Ghana. Our results show that both promotion- and prevention-focused increase entrepreneurial resilience and SME performance significantly. This is an indication that there is a strong positive correlation between entrepreneurial resilience and success in both the individual and firm levels. This inference can therefore assist policy makers, politicians and various industry players in designing training programs on how to improve the resilience of SME owners in Ghana. Statistically our analysis shows that prevention focus (PreV) has a positive effect on entrepreneurial resilience (EntR) (β = 0.250; p<0.001), and promotion focus (PreM) has a positive effect on entrepreneurial resilience (EntR) (β = 0.268; p<0.001). Additionally, entrepreneurial resilience (EntR) has a positive effect on innovative performance (IP) (β = 0.547; p<0.001). Furthermore, the mediation results has shown that entrepreneurial resilience (EntR) mediates the relationship between prevention focus (PreV) and innovative performance (IP) (β = 0.136; p<0.001); as well as that of promotion focus (PreM) and firm performance (IP) (β = 0.146; p<0.001). Finally, the results with regards to moderation effects has shown that entrepreneurial orientation (EntO) partially moderates the relationship between entrepreneurial resilience (EntR) and innovative performance (IP) (β = 0.109; p<0.05), however, the result established that entrepreneurial learning (EntL) does not moderate the relationship between entrepreneurial resilience (EntR) and firm performance (IP) (β = -0.031; p<0.05). Using the Resource Based View theory, we concluded that, SME’s needs to adopted prevention (vigilant/avoidant strategies) by minimizing losses, and taking calculated risks with motivation from mentors to avoid loss as this would achieve resilience as well as promotion of strategies (eager/approach strategies) to achieve gains for their SMEs during economic disruption and economic downturn such as COVID-19. We concluded that SME’s could also achieve resilience when managers or owners of SMEs inspire the employees to work harder, and think outside the box to identify latent ideas and opportunities within their SME business environment.

The management of recycling and remanufacturing supply chains, which can help enterprises achieve low pollution, low energy consumption and sustainable development, has become a new strategy of modern enterprises. The factors of supply chain and government mechanisms will have an impact on enterprise decisions for recycling, remanufacturing and social welfare. In order to promote the sustainable operation of the supply chain, considering the coordination role of government mechanisms and supply chain, a recycling and remanufacturing supply chain model composed of a manufacturer, retailer and recycler is constructed. This paper discusses the pricing decision of new/remanufactured products, supply chain performance level, such as remanufacturing effort, publicity service efforts and profit, and social welfare in five models of three situations: centralized situation, including non-government mechanisms and non-supply chain coordination; manufacturer-led situation, including non-government mechanisms and non-supply chain coordination, government mechanisms and non-supply chain coordination, government mechanisms and supply chain coordination; government-led situation, including government mechanisms and non-supply chain coordination. It is found that under manufacturer-led situations, the government subsidy and bonus-penalty mechanisms can encourage manufacturer and retailer to actively participate in the recycling and remanufacturing activities. The supply chain coordination contract can further enhance the role of the consumer market and promote the implementation of government mechanisms. Manufacturer adopts a cost-sharing contract to encourage recyclers to carry out recycling activities. Under certain conditions, the contract can effectively improve the benefits and social welfare. The research conclusions have important theoretical and practical application value for the coordination and cooperation among enterprises in the supply chain and the formulation of government mechanisms.

Managing blood inventory is challenging due to the perishable and unstable nature of the product needed for transfusions in healthcare facilities. In this paper, we consider a periodic review blood inventory model with two priority demand classes, namely emergency and regular patients. We propose a dynamic programming model for determining the optimal ordering policy at the hospital given the uncertainty regarding received donated blood units. The optimal policy deals with placing orders for blood units that will expire within a fixed period. The objective is to minimize total expected costs within a planning horizon while maintaining a specified expected service level. Our model considers uncertain demands and donated blood units with discrete probability following known distributions. A tabu search algorithm is developed for large-scale problems. The performance of these ordering policies is compared against the optimal fixed order quantity and the order up-to-level policies using real-life data. The numerical results show the benefit of our model over the optimal fixed order quantity and the order up-to-level policies. We measure the total expected cost and the expected service level obtained from the optimal and near-optimal policies and provide a sensitivity analysis on parameters of interest.

This paper addresses the permutation flowshop scheduling problem with peak power consumption constraints (PFSPP). The real-time power consumption of the PFSPP cannot exceed a given peak power at any time. First, a mathematical model is established to describe the concerned problem. The sequence of operations is taken as a solution and the characteristics of solutions are analyzed. Based on the problem characteristics, eight heuristics are proposed, including balanced machine-job decoding method, balanced machine-job insert method, balanced job-machine insert method, balanced machine-job group insert method, balanced job-machine group insert method, greedy algorithm, beam search algorithm, and improved beam search algorithm. Similarly, the canonical artificial bee colony algorithm and iterated local search algorithm are modified based on the problem characteristics to solve the PFSPP. A large number of experiments are carried out to evaluate the performance of new proposed heuristics and metaheuristics. The results and discussion show that the proposed heuristics and metaheuristics perform well in solving the PFSPP.

With the rapid development of the courier industry, customers are placing higher demands on the cost and delivery time of courier services. Therefore, this paper focuses on the bi-objective express transportation network design problem (BO-ETNDP) to minimize the operation cost and maximum arrival time. A multi-structure parallel design methodology (MS-PDM) is proposed to solve the BO-ETNDP. In this methodology, all topological structures commonly used in designing transportation networks are sorted out. For each topological structure, a novel bi-objective nonlinear mixed-integer optimization model for BO-ETNDP is developed considering the impact of the hub’s sorting efficiency on the operation cost and arrival time. To solve these models, a preference-based multi-objective algorithm (PB-MOA) is devised, which embeds the branch-and-cut algorithm and Pareto dominance theory in the framework of this ranking algorithm. In the case study, the applicability of the proposed methodology is verified in a real-world leading express company. The results show that our methodology can effectively avoid the limitation of solving the BO-ETNDP with a specific structure. Besides, the suitable topology for designing express transportation networks in different scenarios are explored through the sensitivity analysis.

Among the well-known scheduling problems, the customer order scheduling problem (COSP) has always been of great importance in manufacturing. To reflect the reality of COSPs as much as possible, this study considers that jobs from different orders are classified in various classes. This paper addresses a tri-criteria single-machine scheduling model with multiple job classes and customer orders on which the measurement minimizes a linear combination of the sum of the ranges of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NP-hard complexity of the problem, a lower bound and a property are developed and utilized in a branch-and-bound for solving an exact solution. Afterward, four heuristics with three local improved searching methods each and a water wave optimality algorithm with four variants of wavelengths are proposed. The tested outputs report the performances of the proposed methods.

With the growing environmental concerns and the rising number of electric vehicles, researchers and companies are paying more and more attention to green logistics. This paper studies the Electric Vehicle Routing Problem with time-dependent speeds and soft time windows. The purpose is to minimize the total distance travelled, while penalizing early or late arrivals at the customers’ locations. For this purpose, we formulated the Mixed Integer Linear Program (MILP) and developed a General Variable Neighborhood Search (GVNS) metaheuristic, an efficient way to tackle this problem. To prove the efficiency of our approach, we tested the GVNS against the Adaptive Large Neighborhood Search (ALNS) algorithm and our MILP model, using a set of available benchmark instances. After an extensive experimental evaluation, we concluded that GVNS can find better quality solutions than other methods considered in this research or the same quality solution in less time.

This paper recommends a new Many-Objective Teaching-Learning-Based Optimizer (MaOTLBO) to handle the Many-Objective Optimal Power Flow (MaO-OPF) problem of modern complex power systems while meeting different operating constraints. A reference point-based mechanism is utilized in the basic version of Teacher Learning-Based Optimizer (TLBO) to formulate the MaOTLBO algorithm and directly applied to DTLZ test benchmark functions with 5, 7, 10-objectives and IEEE-30 bus power system with six different objective functions, namely the minimization of the voltage magnitude deviation, total fuel cost, voltage stability indicator, total emission, active power loss, and reactive power loss. The results obtained from the MaOTLBO optimizer are compared with the well-known standard many-objective algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) and Non-Dominated Sorting Genetic Algorithm-version-III (NSGA-III) presented in the literature. The results show the ability of the proposed MaOTLBO to solve the MaO-OPF problem in terms of convergence, coverage, and well-Spread Pareto optimal solutions. The experimental outcomes indicate that the suggested MaOTLBO gives improved individual output and compromised solutions than MOEA/D-DRA and NSGA-III algorithms.

Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.