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The purpose of this paper is to seek an efficient method to tackle the energy provision problem for smart grid with sparse constraints and distributed energy and storage devices.

A complex smart grid is first studied, in which sparse constraints and the complex make-up of different energy consumption due to the integration of distributed energy and storage devices and the emergence of multisellers are discussed. Then, a real-time pricing scheme is formulated to tackle the demand response based on sparse bilevel programming. And then, a bilevel genetic algorithm (BGA) is further designed. Finally, simulations are conducted to evaluate the performance of the proposed approach.

The considered situation is widespread in practice, and meanwhile, the other cases including traditional model without the sparse constraints can be seen as its extensions. The BGA based on sparse bilevel programming has advantages of “no need of convexity of the model.” Moreover, it is feasible without the need to disclose the private information to others; therefore, privacies are protected and system scalability is kept. Simulation results validate the proposed approach has good performance in maximizing social welfare and balancing system energy distribution.

In this paper, the authors consider the sparse constraints due to the fact that each user can only choose limited utility companies per time slot. In reality, there exist some other sparse cases, which deserve further study in the future.

To the best of the authors’ knowledge, this is one of the very first studies to address pricing problems for the smart grid with consideration of sparse constraints and integration of distributed energy and storage devices.

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a placement machine with multi‐slot feeders. We formulate the problem as a binary linear programming model, and propose a heuristic procedure to find the solution that consists of a board‐assembly sequence, an associated component loading and unloading strategy and a feeder‐assignment plan within reasonable computational effort. Computational results from solving the simulated problem instances by using the heuristic method and the mathematical model are provided and compared. The proposed heuristic procedure can be incorporated into the PCB scheduling optimization software to decrease cycle times and increase overall assembly throughput in a high‐mix, low‐volume PCB manufacturing environment.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.

To provide some heat transfer and friction factor results for fin‐and‐tube heat transfer surfaces which may be used in air conditioning industry.

Numerical simulation approach was adopted to compare the plain plate fin and three types of radial slotted fin surfaces.

It is found that at the same frontal velocity (1.0‐3 m/s) the plain plate fin has the lowest heat transfer rate with the smallest pressure drop. The full slotted fin surface has the highest heat transfer rate with the largest pressure drop penalty. The partially slotted fin (where the strips are mainly located in the rear part of the fin) and the back slotted fin are some what in between. Under the identical pumping power constraint, the partially slotted fin surface behaves the best.

The results are only valid the two‐row fin surface.

The results are very useful for the design of two‐row tube fin surfaces with high efficiency.

This paper provides original information of slotted fin surface with radial strips from the field synergy principle.

The purpose of this paper is to investigate the optimization for a placement machine in printed circuit board (PCB) assembly when family setup strategy is adopted.

A complete mathematical model is developed for the integrated problem to optimize feeder arrangement and component placement sequences so as to minimize the makespan for a set of PCB batches. Owing to the complexity of the problem, a specific genetic algorithm (GA) is proposed.

The established model is able to find the minimal makespan for a set of PCB batches through determining the feeder arrangement and placement sequences. However, exact solutions to the problem are not practical due to the complexity. Experimental tests show that the proposed GA can solve the problem both effectively and efficiently.

When a placement machine is set up for production of a set of PCB batches, the feeder arrangement of the machine together with the component placement sequencing for each PCB type should be solved simultaneously so as to minimize the overall makespan.

The paper investigates the optimization for PCB assembly with family setup strategy, which is adopted by many PCB manufacturers for reducing both setup costs and human errors.

The paper investigates the feeder arrangement and placement sequencing problems when family setup strategy is adopted, which has not been studied in the literature.

This chapter studies the consequences of firm delayering on wages and the wage distribution inside firms. I consider a market-based tournament model with asymmetric information to endogenize firms’ delayering decisions. My model predicts that when the CEO becomes more productive, firms grow in size. When the CEO becomes sufficiently productive, firms delayer. After delayering, wages at all levels rise and the wage gap between the CEO and the laborers widens. These predictions capture the dynamic process of firms’ structure and size changes and match a set of empirical findings in recent studies that are not well explained by existing theories.

In the modern era of e-grocery, attended home delivery (AHD) has been identified as a crucial part of the last-mile delivery problem. This paper aims to deal with a real-life last-mile-delivery problem in the context of the Indian public distribution system (PDS). The authors identified two different environments for the said AHD problem and proposed two different approaches to address the issue under these problem settings.

In this study, the authors first consider the problem in a static environment and propose an iterated local search (ILS) integrated with an adaptive large neighborhood search (ALNS) meta-heuristic algorithm to obtain a routing solution. Thereafter, they extend the study in a dynamic environment where new delivery requests occur dynamically and propose a heuristic algorithm to solve the problem. For the dynamic case, multiple scenarios for the occurrence of delivery requests are considered to determine decisions regarding the opportunity to include a new request into the current solution.

By computational experiments, the authors show that the proposed approach performs significantly well for large size problem instances. They demonstrate the differences and advantages of the dynamic problem setting through a set of different scenarios. Also, they present a comparative analysis to show the benefits of adopting the algorithm in dynamic routing scenarios.

Future research may extend the scope of this study by incorporating stochastic delivery failure probabilities and customer behavior affecting the delivery response. Also, the present study does not take inventory policies at the depot into consideration. It will be of interest to see how the system performs under the uncertainty of supply from the depot. Despite the limitations, the authors believe that this study provides food for thought and encouragements for practitioners.

This study shows the benefits of adopting an AHD problem in a dynamic setting in terms of customer service as compared to a traditional static environment. The authors clearly demonstrate the differences and advantages of the dynamic problem setting through a set of different scenario analysis.

This paper investigates a real-life AHD problem faced by the Department of Food, Supply and Consumer Affairs, India. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The study is unique and highly relevant for real-world applications and can help build a more robust AHD system. Also, the proposed solution approaches to aid the problem in both static and dynamic routing scenarios will be of particular interest to practitioners.

An approach to optimize the design of three‐phase induction motors for a wide speed range drive is considered. Two operating points in the speed range are taken into consideration. The problem is handled as a constrained optimization problem. An accurate model for the motor in terms of its dimensions has been developed which predicts the motor performance based on about 60 parameters of motor geometry.