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As electric vehicles (EVs) are currently under-utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi-objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi-objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade-off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.

Uncertainty and non-deterministic nature of the real world makes planning and scheduling in cross-docks a very complicated task for decision makers. These constant changes that happen all the time, often, lead to an increase in costs and/or a decrease in efficiency. Most of the uncertainty in cross-docks is caused by un-known truck arrival times. In this study we address the problem of scheduling incoming and outgoing trucks at a cross-dock facility, when vehicle arrival times are unknown, through a cost-stable scheduling strategy. Two meta-heuristics, MODE and NSGA-II, are used for solving the designed sample problems and are compared with a random search based genetic algorithm existing in the literature. Finally, performance of each algorithm is measured and analyzed using four metrics: quality, spacing, diversification and mean ideal distance. The results indicate that the proposed model MODE algorithm performs better in comparison with the other two methods.

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

The thought to put forward a queuing model proposed in this work was its pertinence in everyday life wherever we can see the uses of computing and networking systems. Industrial software developers and system managers can consider the results of the model to evolve their system for better results. Here we present a novel queueing model having erratic server with delayed repair and balking. Two distinct breakdowns i.e. active and passive breakdown for the system are also considered with their respective amendments. This model is closely related with the smooth functioning of the system during some internal faults (virus attack, electricity failures etc.). The performance indicators which are utilized in enhancing the service standards are obtained using supplementary variable technique. Using ANFIS soft computing technique we have compared the analytical results with those of neuro fuzzy results. Furthermore single and bi-objective minimization problems are considered and minima is obtained using particle swarm optimization and multi objective genetic algorithm respectively. Also, the minimization problems are shown as a convex programming problem to ensure the global optimality of the result. The proposed approach makes it conceivable to accomplish a relevant harmony between operational expenses and administration quality.

In the modern market scenario governed by the Mass Customization paradigm, the so-called delayed product differentiation (DPD) rose as a production strategy best balancing traditional Make-to-Stock (MTS) and Make-to-Order (MTO), potentially reducing storage cost and customization time. In industry, DPD uses product platforms, defined as a set of components forming a common structure, from which a stream of derivative variants is produced. Early-stage platforms, made of few components, limit their storage cost, increasing the time to customize and turn them into final variants. The literature widely discusses the product platform design problem, asking to explore quantitatively the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing and applying a bi-objective optimization model able to assign the most suitable production strategy to each product variant among MTS, MTO, and DPD. In the case of DPD selection, the model designs the product platforms best balancing storage cost and customization time as the target metrics to optimize, subject to industrial constraints to produce and store them, matching each variant to the most suitable platform. A case study adapted from the electronic components sector exemplifies the use of the bi-objective model, supporting companies in managing high-variety mixes.

Scheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan . Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan , monetary cost, speed up , SLR , and efficiency metrics, respectively .

Research on multi-objective discrete optimization of Waste Electrical and Electronic Equipment (WEEE) remanufacturing remains under-studied in the literature. Remanufacturing laptops to extend their useful life is viewed as the best End-Of-Life (EOL) alternative considering environmental and social factors. This paper develops a model to decide the best EOL option, namely reuse, conditional repair, and disposal of quality-graded laptop parts, with economic and environmental objectives. The first objective is to maximize the profit of remanufactured laptops over a multi-period planning horizon. The second objective is to minimize the emissions associated with remanufacturing. A Multi-Objective Discrete Particle Swarm Optimization (MODPSO) algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are embedded as a decision support tool in Microsoft Excel with a user interface to yield Pareto optimal solutions, and the results are compared. The Taguchi approach is applied to find the optimum value of the control parameters of the proposed algorithms. The approach is tested with inputs from an authorized remanufacturer in Bangalore, India. The performance of the algorithms is further investigated using randomly generated test problems. The MODPSO algorithm provided better solutions for all problem instances based on the convergence and diversity metrics. The inclusion of conditional repair options and parts with low and medium-quality grades in remanufacturing leads to higher profit, albeit with more emissions. A variation in the quality grade assigned to the conditional repair option for the parts needed for higher profit margin laptops is observed. A sensitivity analysis is conducted to observe the impact of supply, demand, repair cost, shortage cost, and emissions on the two extreme Pareto solutions. The decision-making tool offers a continuum of trade-offs to help a remanufacturer choose the EOL options, depending on economic and environmental performance preferences.

In this study, an integrated bi-objective objective U-shaped assembly line balancing and parts feeding problem is explored by considering the heterogeneity inherent of workers. An optimization model is developed to formulate the addressed problem. Since the problem includes two different objectives, namely the minimizing the operational cost and maximum workload imbalance, the Pareto-optimal solutions are found by employing the second version of the augmented ε-constrained (AUGMECON2) method. To investigate the impact of qualification of workers on the system performance, a set of scenarios is constructed based on the worker skill levels. Each scenario is determined based on the nature of the worker pool in which workers are assigned to the stations. The optimization model and implemented method are validated through data taken from water-meter and elevator producers. The computational results reveal that the scenarios have a great impact on system performance. In particular, it is revealed that as the skill levels of workers increases, the quality of the Pareto-optimal solutions increase by up to 30% in terms of the comparison metrics. Therefore, an order release mechanism and worker training activities are suggested to be performed to enhance system performance.

As an emerging computer technology with numerous bits, bit-wise allocation, and extensive parallelism, the ternary optical computer (TOC) will play an important role in platforms such as cloud computing and big data. Previous studies on TOC in handling computational request tasks have mainly focused on performance enhancement while ignoring the impact of performance enhancement on power consumption. The main objective of this study is to investigate the optimization trade-off between performance and energy consumption in TOC systems. To this end, the service model of the TOC is constructed by introducing the M/M/1 and M/M/c models in queuing theory, combined with the framework of the tandem queueing system, and the optimization problem is studied by adjusting the processor partitioning strategy and the number of small TOC (STOC) in the service process. The results show that the value of increasing active STOCs is prominent when system performance significantly depends on response time. However, marginal gains decrease as the number of STOCs grows, accompanied by rising energy costs. Based on these findings, this paper constructs a bi-objective optimization model using response time and energy consumption. It proposes an optimization strategy to achieve bi-objective optimization of performance and energy consumption for TOC by identifying the optimal partitioning strategy and the number of active small optical processors for different load conditions.

Current static evacuation path planning methods are unable to meet the demands of real-time changes in fire scenarios. Therefore, a bi-objective dynamic planning (BODP) method for fire evacuation paths is proposed not only to minimize evacuation time but also reduce the thermal radiation dose received by personnel during the evacuation process. The BODP method includes both an equivalent rule proposed for the first time based on the linear weighting method, and an improved Dijkstra algorithm designed in this paper. Because of them, the BODP method can normalize evacuation time and thermal radiation dose to the same degree and perform multi-sink dynamic planning on a hexagonal grid map, in order to find the optimal evacuation path with the lowest equivalent cost in the event of a tank farm fire. This method accounts for changing fire scenarios and avoids potentially high-risk paths. Finally, a case study of a chemical plant tank farm is conducted to demonstrate the effectiveness of the method. The results indicate that the BODP method is better suited for evacuation path planning in complex and dynamic fire scenarios. The BODP method resolves the issue found in static path planning: the first-degree burn probabilities derived from estimated thermal radiation doses are 34% lower than those derived from actual thermal radiation doses, potentially resulting in unnecessary casualties. Additionally, the BODP method is also applicable to scenarios involving domino effects triggered by fire incidents. This method provides scientific and reliable technical support for emergency evacuation in tank farm fires, greatly contributing to the safety and protection of personnel.