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This paper presents an optimal design method of number and placements of piezoelectric patch actuators in active vibration control of a plate. Eigenvalue distribution of energy correlative matrix of control input force is applied to determine optimal number of the required actuators. Genetic algorithms (GAs) using active vibration control effects, which are taken as the objective function, are adopted to search optimal placements of actuators. The results show that disturbance exerted on a plate is a key factor of determining optimal number and placements of actuators in active structural vibration control, and a global and efficient optimization solution of multiple actuator placements can be obtained using GAs.

The Indonesian Islamic banking industry is very promising, but there has been no analysis of the optimal and decreasing growth rate of the industry. Information regarding these is essential for policy makers, Islamic bankers and all related parties to guide the future development of the industry and sets up proper plans and strategies. The paper aims to explore the optimal and decreasing growth rates of the industry and in so doing contribute to the current literature on the Indonesian Islamic banking industry.

The paper first reports on the performance of the Indonesian Islamic banking industry, before explaining conditions where the Islamic banking industry is believed to be still immature. Third, in order to identify the optimal and decreasing growth rates, the paper estimates the future performance of the industry by using ARIMA models to identify periods where the growth rate is at optimal and decreasing points. Then, on the basis of a number of assumptions and statistical simulations, the analysis broadens to become qualitative in nature by determining the optimal numbers of Islamic banks to be established in the future.

The paper generates some important findings. First, the optimum growth rate of the market share (0.12 percent) is predicted to occur in December 2012; the market share in that month is estimated to be 5.75 percent. Second, although the market share keeps growing, the paper finds the rate of increase to be slow, and in October 2018, it becomes negative, at 0.004 percent. The estimated market share in that time is 11.63 percent. Finally, the optimal number of Islamic banks in December 2012 is shown to be 23 and in October 2018, 24.

Qualitative information on the months of the optimal and decreasing growth rates and quantitative information on the optimal number of Islamic banks to be established are significant information for policy makers, Islamic bankers and other related parties. The information is likely to be important in relation to their efforts to develop the Islamic banking industry, to anticipate decreasing growth in the industry and to establish new Islamic banks. More generally, the paper helps the related parties to direct and guide the future development of the industry.

To the best of the authors' knowledge, this is the first paper that attempts to establish optimal and decreasing growth rates in the Indonesian Islamic banking industry, or the optimal numbers of Islamic banks to be established in the future.

This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.

The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.

The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.

To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.

The restructuring of shipping and shipbuilding companies in the midst of rapidly shrinking global shipping demand has become a prominent issue in Korea. In shipping finance, loan syndication featuring many creditors surges as the preferred option. However, increasing the numbers of creditors in the syndicate results in two opposite effects. First is the beneficial effect from their enhanced monitoring power. On the other hand, there is the adverse effect resulting from increased difficulty in coordination when syndicate members increase, particularly in bankruptcy. Our aim of this paper is to analyze the role of finance in the shipping and shipbuilder markets, and determine the theoretical optimal number of creditors for the shipping finance syndicate based on Bolton and Scharfstein (1996). The two issues above result from moral hazard and non-verifiability: coordination among many creditors for collection of bonds in case of default, and the enhancement of monitoring private benefit exploitation by the ship-owner during default. Considering the two conflicting forces result from an increase in creditor membership, we draw conclusions on determining the optimal number of creditors by considering trade-offs between these two factors: More creditors are preferred when the monitoring effect dominates. Otherwise, less creditors are preferred.

The objective of this research study is to formulate and develop a novel optimization model that enables planners of modular construction to minimize the total transportation and storage costs of prefabricated modules in modular construction projects.

The model is developed by identifying relevant decision variables, formulating an objective function capable of minimizing the total transportation and storage costs and modelling relevant constraints. The model is implemented by providing all relevant planner-specified data and performing the model optimization computations using mixed-integer programming to generate the optimal solution.

A case study of hybrid modular construction of a healthcare facility is used to evaluate the model performance and demonstrate its capabilities in minimizing the total transportation and onsite storage costs of building prefabricated modules.

The model can be most effective in optimizing transportation for prefabricated modules with rectangular shapes and might be less effective for modules with irregular shapes. Further research is needed to consider the shape of onsite storage area and its module arrangement.

The developed model supports construction planners in improving the cost effectiveness of modular construction projects by optimizing the transportation of prefabricated modules from factories to construction sites.

The original contributions of this research is selecting an optimal module truck assignment from a feasible set of trucks, identifying an optimal delivery day of each module as well as its location and orientation on the assigned truck and complying with relevant constraints including the non-overlap of modules on each truck, shipment weight distribution and aerodynamic drag reduction.

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

Computerized layout (or “topology”) optimization was pioneered almost four decades back. However, despite dramatic increases in available computer power and the application of increasingly efficient optimization algorithms, even now only relatively modest sized problems can be tackled using the traditional “ground structure” approach. This is because of the need, in general, for the latter to contain every conceivable member connecting together the nodes in a problem. A simple, but effective solution method capable of tackling problems with large numbers of potential members (e.g. >100,000,000) is presented. Though the method draws on the linear programming technique of “column generation”, since layout optimization specific heuristics are employed it is presented as an iterative “member adding” method. The method requires a ground structure with minimal connectivity to be used in the first iteration; members are then added as required in subsequent iterations until the (provably) optimal solution is found.

The purpose of this paper is to investigate the influence of the crowdfunding platforms on reward-based crowdfunding projects. This study offers guidance for the creator on how to choose among platforms and how to make optimal product and pricing decisions.

Usually, crowdfunding platforms are able to help creators to lower unit costs and charge platform fees in return. In this paper, the reduction of the unit cost and the platform fee are selected for determining the competitive strength (CS) of a platform. Then, the CS affecting the creators and the backers of the projects is analyzed.

In the basic model, when the product quality level is exogenous, the optimal price increases in the product quality level and decreases in the difficulty level of the project, while the corresponding expected profit is a unimodal function of the product quality level and the difficulty level. In the endogenous case, the optimal price is exactly twice the unit cost. With the influence of platforms, platforms with higher CS tend to help the creator to lower the prices and to achieve higher profitability. Moreover, platforms with higher CS usually help the creator to offer higher quality products and to charge higher prices.

The opportunity cost is zero in this paper. In reality, backers arrive at the project in different order. Usually, earlier backers bare more opportunity cost and risk.

To the best of authors’ knowledge, this paper is the first one to offer suggestions for creators on how to choose among crowdfunding platforms. The study provides theoretical guidance on product and pricing decisions on an analytical side.

Embracing a large set of innovation objectives and collaborating with diverse partners have been promoted as a means to improve innovation performance. However, empirical evidence on the relationships between breadth of objectives, breadth of cooperation and innovation performance is limited, particularly in the context of emerging economies. A larger number of objectives and cooperation partners inevitably increases the complexity in organizational alignment, and cooperation eventually leads to diminishing returns. This study adds to the debate on the costs and benefits of cooperation for innovation. Understanding the optimal levels of the breadth of objectives and cooperation supports managerial decision-making and productivity in the practice of cooperation for innovation.

Operationalizing breadth of innovation objectives and cooperation via the Turkish Community Innovation Survey data, self-reports reflecting 5,863 firm-level responses between 2006 and 2008 are analysed using tobit and probit models. The maximum likelihood estimator is used to find the optimal levels for breadth of objectives and cooperation.

Firms with greater breadth of innovation objectives experience higher innovation performance; those with greater breadth of cooperation also experience higher innovation performance, but our results indicate the existence of optimal levels of breadth for both innovation objectives and cooperation.

The authors extend the logic that there is no safety in numbers in cooperation for innovation. If the aim is to enhance innovation performance, managers and policymakers need to pay attention to the number of innovation objectives and the amount of cooperation pursued by firms. However, innovation success may be closely associated with a firm's dynamic capabilities and ability to mobilize its resources. Drawing on organizational learning theories, future research could explore why a lower than maximum level of cooperation may be more conducive to reaching levels of enhanced innovation performance and whether this level is influenced by cognitive processes.

The authors draw attention to the ideal number of innovation objectives and number of cooperating partners required to enhance innovation performance, thus contributing to the debate on the complex relationships between innovation, performance and cooperation in the unique setting of a large developing economy.

The objective is to study the optimal damper placement of a given number of passive viscoelastic dampers, when the structure (nuclear plant) is subjected to motions from earthquake, in order to reduce the acceleration responses at a particular location, for example, a vibration‐sensitive room (nuclear reactor).

The IDESIGN software including genetic algorithms for the optimisation task has been interfaced with a finite element program, ABAQUS, to create a structural optimisation tool. The tool has been tested on a 3D building structure.

Optimisation results for different constraints on number of dampers shows that eight optimally located dampers results in an overall maximum reduction of 59 per cent of the cost function for the uncontrolled structure. It is also found that six optimally placed dampers are more effective than the fully damped case with 12 dampers.

More powerful computers are needed in order to reduce the computational time for many engineering models. Especially parallel processing would make more efficient use of the genetic algorithm. It is also necessary to perform stochastic finite element analysis to investigate the impact on the cost function due to different earthquakes.

The study of vibration reduction by an optimal number and location of viscoelastic dampers indicate that structures subjected to dynamic forces can be designed both safely and economically.

The tool developed is quite general and is believed to be applicable to many types of structural optimisation analyses.