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Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes' residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

Performance and energy are now the most dominant objectives for optimization on modern parallel platforms composed of multicore CPU nodes. The existing intra-node and inter-node optimization methods employ a large set of decision variables but do not consider problem size as a decision variable and assume a linear relationship between performance and problem size and between energy consumption and problem size. We demonstrate using experiments of real-life data-parallel applications on modern multicore CPUs that these relationships have complex (non-linear and even non-convex) properties and, therefore, that the problem size has become an important decision variable that can no longer be ignored. This key finding motivates our work in this paper. In this paper, we first formulate the bi-objective optimization problem for performance and energy (BOPPE) for data-parallel applications on homogeneous clusters of modern multicore CPUs. It contains only one but heretofore unconsidered decision variable, the problem size. We then present an efficient and exact global optimization algorithm called ALEPH that solves the BOPPE. It takes as inputs, discrete functions of performance and dynamic energy consumption against problem size and outputs the globally Pareto-optimal set of solutions. The solutions are the workload distributions, which achieve inter-node optimization of data-parallel applications for performance and energy. While existing solvers for BOPPE give only one solution when the problem size and number of processors are fixed, our algorithm gives a diverse set of globally Pareto-optimal solutions. The algorithm has time complexity of $O(m^2 \times p^2)$ where $m$ is the number of points in the discrete speed/energy function and $p$ is the number of available processors. We experimentally study the efficiency and scalability of our algorithm for two data parallel applications, matrix multiplication and fast Fourier transform, on a modern multicore CPU and homogeneous clusters of such CPUs. Based on our experiments, we show that the average and maximum sizes of the globally Pareto-optimal sets determined by our algorithm are 15 and 34 and 7 and 20 for the two applications respectively. Comparing with load-balanced workload distribution solution, the average and maximum percentage improvements in performance and energy respectively demonstrated for the first application are (13%,97%) and (18%,71%). For the second application, these improvements are (40%,95%) and (22%,127%). Assuming 5 percent performance degradation from the optimal is acceptable, the average and maximum improvements in energy consumption demonstrated for the two applications respectively are 9 and 44 and 8 and 20 percent. Using the algorithm and its building blocks, we also present a study of interplay between performance and energy. We demonstrate how ALEPH can be combined with DVFS-based Multi-Objective Optimization (MOP) methods to give a better set of (globally Pareto-optimal) solutions. [ABSTRACT FROM AUTHOR]

A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is proposed for the mission planning of on-orbit servicing such as visual inspection, active debris removal and refueling through multiple servicing satellites (SSs). The scheduling has been done with the aim of minimization of fuel consumption and mission duration. So a multi-objective optimization problem is dealt with here which is solved by employing particle swarm optimization algorithm. Also, Taguchi technique is employed for robust design of effective parameters of optimization problem. The day that the SSs have to leave parking orbit, transfer duration from parking orbit to final orbit, transfer duration between one target to another, and time spent for the SS on each target are the decision parameters which are obtained from the optimization problem. The raised idea is that in addition to the aforementioned decision parameters, eccentricity and inclination related to the initial orbit and also phase difference between the SSs on the initial orbit are identified by means of optimization problem, so that the designer has not much role on determining them. Furthermore, it is considered that the SS and the target rendezvous at the servicing point and the SS does not perform any phasing maneuver to reach the target. It should be noted that Lambert theorem is used for determination of the transfer orbit. The results show that the proposed approach reduces the fuel consumption and the mission duration significantly in comparison with the conventional approaches. [ABSTRACT FROM AUTHOR]

Due to the increasingly severe resource scarcity and environmental pollution, appropriate recovery of end-of-life (EOL) products has gained significant importance in recent years. The disassembly lines are one of the vital steps during the entire recovery process. However, considering the disadvantages of manual disassembly lines such as high labor cost, low efficiency, and harm to workers' health, the transition to robotic disassembly lines is underway. Therefore, in this paper, a sustainable robotic disassembly line balancing problem (RDLBP) is proposed to improve the disassembly efficiency (cycle time) and environmental friendliness (total energy consumption) simultaneously. To narrow the gap between theory and practice, uncertain processing time, sequence-based tool changeover, and robots with different efficiencies and energy consumption rates are considered, which requires adding the steps of resequencing the tasks and allocating the most suitable robots within each workstation on the basis of assigning the tasks to workstations as evenly as possible. To solve this problem, the epsilon constraint algorithm is used to obtain the exact solutions for small scale cases and verify the validity of the proposed model. Simultaneously, due to the NP-hard nature, a modified bi-objective Harris Hawks optimization (MBOHHO) algorithm is developed to solve the line balancing problem for the first time, which combines opposition-based learning, differential evolution, and Gaussian mutation mechanisms, and modifies the renewal strategy of two vital parameters based on bioenergy consumption pattern, contributing to improve the diversity and help jump out of local optimum. Finally, computational experiments are performed to evaluate the performance of the MBOHHO algorithm by comparing it with four other outstanding optimization algorithms, and the results reveal its effectiveness and superiority in various metrics. [ABSTRACT FROM AUTHOR]

Motivated by the call of the International Maritime Organization to meet the emission targets of 2030, this study considers two important practical aspects of quay crane scheduling: efficiency and energy consumption. More precisely, we introduce the bi-objective quay crane scheduling problem where the objective is to minimize the vessel's completion time and the crane's energy consumption. This is done by formulating a bi-objective mixed-integer programming model. A branch-and-bound algorithm was developed as the exact solution approach to find the full set of Pareto-optimal solutions. We consider (i) various lower bounds for both objectives, (ii) specific upper bounds, (iii) additional branching criteria, and (iv) fathoming criteria to detect Pareto-optimal solutions. Numerical experiments on benchmark instances show that the branch-and-bound algorithm can efficiently solve small- and medium-sized problems. [ABSTRACT FROM AUTHOR]

Performance and energy are the two most important objectives for optimization on modern parallel platforms. In this article, we show that moving from single-objective optimization for performance or energy to their bi-objective optimization on heterogeneous processors results in a tremendous increase in the number of optimal solutions (workload distributions) even for the simple case of linear performance and energy profiles. We then study full performance and energy profiles of two real-life data-parallel applications and find that they exhibit shapes that are non-linear and complex enough to prevent good approximation of them as analytical functions for input to exact algorithms or optimization software for determining the Pareto front. We, therefore, propose a solution method solving the bi-objective optimization problem on heterogeneous processors. The method's novel component is an efficient and exact global optimization algorithm that takes as an input performance and energy profiles as arbitrary discrete functions of workload size, which accurately and realistically take into account resource contention and NUMA inherent in modern parallel platforms, and returns the Pareto-optimal solutions (generally speaking, load imbalanced). To construct the input discrete energy functions, the method employs a methodology that accurately models the energy consumption by a hybrid data-parallel application executing on a heterogeneous HPC platform containing different computing devices using system-level power measurements provided by power meters. We experimentally analyse the proposed solution method using three data-parallel applications, matrix multiplication, 2D fast Fourier transform (2D-FFT), and gene sequencing, on two connected heterogeneous servers consisting of multicore CPUs, GPUs, and Intel Xeon Phi. We show that it determines a superior Pareto front containing the best load balanced solutions and all the load imbalanced solutions that are ignored by load balancing methods. [ABSTRACT FROM AUTHOR]