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Highlights • We propose an economically efficient M -to- N electric vehicle charger. • The charger can charge multiple electric vehicles simultaneously with limited charging capacity. • A mixed-integer linear programming model for charging scheduling is developed. • An efficient relaxation-based heuristic algorithm is developed. Abstract This study investigates a real case of charging scheduling of an electric vehicle charger with multiple ports called M -to- N charger. The charger is designed for a multi-unit dwelling facility and can charge N electric vehicles simultaneously despite the supplied charging capacity being limited to only M electric vehicles. The electric vehicles arrive at the charger randomly and stay for their desired length of time, during which they must be charged as much as possible with minimum electric cost. The scheduling problem considers four objectives: maximizing the total charging amount, minimizing the total charging cost, minimizing the charging completion time, and maximizing the charging balance among the electric vehicles. A mixed-integer linear programming model and a relaxation-based heuristic algorithm are developed. Computational experiment results show that the proposed heuristic algorithm can generate schedules within 8 s for this case study by using an open-source linear programming solver. Compared with the mixed-integer programming algorithm, the proposed heuristic algorithm can provide solutions with less than 7% charging amount gap and 4% price gap. The proposed heuristic algorithm is successfully implemented in a real M -to- N charger. [ABSTRACT FROM AUTHOR]

Automated meeting scheduling is the task of reaching an agreement on a time slot to schedule a new meeting, taking into account the participants' preferences over various aspects of the problem. Such a negotiation is commonly performed in a non-automated manner, that is, the users decide whether they can reschedule existing individual activities and, in some cases, already scheduled meetings in order to accommodate the new meeting request in a particular time slot, by inspecting their schedules. In this work, we take advantage of SELFPLANNER, an automated system that employs greedy stochastic optimization algorithms to schedule individual activities under a rich model of preferences and constraints, and we extend that work to accommodate meetings. For each new meeting request, participants decide whether they can accommodate the meeting in a particular time slot by employing SELFPLANNER'S underlying algorithms to automatically reschedule existing individual activities. Time slots are prioritized in terms of the number of users that need to reschedule existing activities. An agreement is reached as soon as all agents can schedule the meeting at a particular time slot, without anyone of them experiencing an overall utility loss, that is, taking into account also the utility gain from the meeting. This dynamic multi-agent meeting scheduling approach has been tested on a variety of test problems with very promising results. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of job dispatching and scheduling, where each job consists of a set of tasks. Each task is processed by a set of machines simultaneously. We consider two important performance metrics, the average job completion time (JCT), and the number of deadline-aware jobs that meet their deadlines. The goal is to minimize the former and maximize the latter. We first propose OneJ to minimize the job completion time (JCT) when there is exactly one single job in the system. Then, we propose an online algorithm called MultiJ, taking OneJ as a subroutine, to minimize the average JCT, and prove it has a good competitive ratio. We then derive another online algorithm QuickJ to maximize the number of jobs that can meet their deadlines. We show that QuickJ is competitive via a worst case analysis. We also conjecture that the competitive ratio of QuickJ is likely to be the best one that any deterministic algorithm can achieve. We also shed light on several important merits of MultiJ and QuickJ, such as no severe coordination overhead, scalability, work conservation, and no job starvation. [ABSTRACT FROM AUTHOR]

Current precast production scheduling methodologies often have limited applicability in practice largely due to the limited consideration of human factors and interactions between the scheduling and workforce development. This study systematically integrates human factors in the form of worker competence into production scheduling. More specifically, the factors of worker learning rate, professional skills, experience in each type of operation, and operation specifications are all formulated to determine the duration of each process in precast production. A two-hierarchy scheduling optimization model (WCSO_PP) considering worker competence is proposed to synchronize the production scheduling with workforce development and to optimize the scheduling from both the short-term (time-oriented) and long-term (competence-oriented) views. Finally, the performance of the proposed model is validated using a real case study. The WCSO_PP model can achieve 74.09% and 73.01% cost savings in short-term and long-term scenarios, respectively. The developed model addresses the research gap in modeling the impact of human factors on the production, contributing to more reliable scheduling and enhancing the long-term human resource development. [ABSTRACT FROM AUTHOR]

Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency. [ABSTRACT FROM AUTHOR]

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Renewable energy-based multi-energy supply microgrids are expected to play an important role in smart cities. How to schedule such microgrids in grid-connected mode and dispatch power among sources inside the microgrids is a problem. Moreover, as electric vehicles are becoming more and more common, the charging of large numbers of vehicles is also a challenge for the utility grid. In this paper, we build a temporal-spatial electric vehicle charging demand model, which includes three parts: trip plans, duration of stay, and search for the shortest path based on the Dijkstra algorithm. Then, we build a grid-connected gas/electricity/heat microgrid and present a coordinated scheduling method for this microgrid. A day-ahead scheduling method is used to decide the role of the microgrid (i.e., operate as a load or as a generator from the point of view of the utility), a real-time rolling-horizon dispatching algorithm is used to respond to the forecasting errors and at the same time implement the real-time actual power exchange between the microgrid and the main grid. The problem is formulated as a mixed integer linear programming problem. The temporal-spatial electric vehicle charging demands model is simulated based on a 81-node transportation network, while the energy supply network is a combined IEEE-30, gas-20 and heat-14 network. The simulation results show the effectiveness of this coordinated scheduling method. [ABSTRACT FROM AUTHOR]

A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design the sales territory for a business sales plan. The business plan consist of assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization models. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers. [ABSTRACT FROM AUTHOR]

Advent of Internet of Things led to an exponential rise in battery-operated sensors transmitting small non-real time (NRT) data regularly. To this end, this work proposes a framework for centralized cognitive radio network (CRN) that facilitates better spectrum utilization and low-cost opportunistic NRT data transfer with high energy efficiency. The novelty of this framework is to incorporate Hidden Markov Model-based prediction within the traditional cognitive radio sensing-transmission cycle. To minimize the prediction time, we design a Hardware-based Hidden Markov Model engine (H2M2) to be used by the cognitive base station (CBS). CBS exploits the H2M2 engine over high primary user (PU) activity channels to minimize the collisions between PUs and NRT secondary users, thereby reducing the SU energy consumption. However, this is at the cost of reduced throughput. Taking this into account, we propose an Intersensing-Prediction Time Optimization algorithm that identifies the predictable PU activity channels and maximizes the throughput within a PU interference threshold. Furthermore, to minimize the total battery consumption of all the SUs within CRN, a Battery Consumption Minimizing Scheduler is designed at the CBS that efficiently allocates the predictable PU channels to the NRT SUs. By exploiting the unutilized high PU activity channels, the proposed Centralized Scheduling, Sensing and Prediction (CSSP) framework improves the spectral efficiency of the CRN. Exhaustive performance studies show that CSSP outperforms traditional nonpredictive sensing techniques in terms of energy efficiency and interferencemanagement. Finally, through a proof of concept, we validate the ability of CSSP framework in enabling NRT communication. [ABSTRACT FROM AUTHOR]

Plug-in electric vehicles are becoming one of indispensable prosumer electronics components for smart households and therefore, their cost efficient energy scheduling is one of the main challenging issues. In the current schemas, the charging and discharging interval of the vehicles are normally announced by the owners in advance leading to the suboptimal profit gain in some situations and hence consumers dissatisfaction. In this paper, we propose an efficient charging/discharging scheduling mechanism for electric vehicles in multiple homes common parking lot for smart households prosumers. The proposed mechanism takes into account the optimal interval allocation considering the instantaneous electricity load and the vehicles request pattern. Based on the data from the vehicles, a mixed optimization model is formulated by the central scheduler which aims to maximize the profit of consumers and is then solved using an effective algorithm. The optimization results are then sent to the system controller determining the interval and energy trading patterns between the power grid and the vehicles. The proposed algorithm has low complexity and ensures the energy satisfaction for all consumers. The performance of the scheduling schema is verified through multiple simulation scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint routing–scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpressure algorithms. Specifically, we propose two policies: first, the stochastic backpressure with energy harvesting, in which a node's routing–scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors’; and second, the stochastic soft backpressure with energy harvesting, an improved algorithm where the routing–scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones that work with an unlimited energy supply. [ABSTRACT FROM AUTHOR]

Information age is a recently introduced metric to represent the freshness of information in communication systems. We investigate age minimization in a wireless network and propose a novel approach of optimizing the scheduling strategy to deliver all messages as fresh as possible. Specifically, we consider a set of links that share a common channel. The transmitter at each link contains a given number of packets with time stamps from an information source that generated them. We address the link transmission scheduling problem with the objective of minimizing the overall age. This minimum age scheduling problem (MASP) is different from minimizing the time or the delay for delivering the packets in question. We model the MASP mathematically and prove it is NP-hard in general. We also identify tractable cases as well as optimality conditions. An integer linear programming formulation is provided for performance benchmarking. Moreover, a steepest age descent algorithm with better scalability is developed. Numerical study shows that, by employing the optimal schedule, the overall age is significantly reduced in comparison to other scheduling strategies. [ABSTRACT FROM AUTHOR]

Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10–20% over the original QPPTW. [ABSTRACT FROM AUTHOR]

• A novel methodology for scheduling of energy intensive industries under the uncertainty of spot electricity market. • Development of a scenario generator tool using ARIMA for electricity price forecasting. • Integration of deterministic optimization with multi scenario approach. • Risk metric analysis to facilitate decision making in choosing the best suited scenario. • Implementation of the methodology in a real-life cryogenic air separation plant. The planning of energy-intensive processes is intrinsically uncertain due to their dependence on the volatile energy market, with scheduling having a vast impact on the final production cost of these plants. Traditional stochastic methods are mathematically very complex, which translates into a significant computational effort that might prevent a timely response to varying electricity prices. To encounter this uncertainty, we develop a reliable hybrid simulation-optimization approach for optimizing the production plant scheduling, combining scenario analysis with risk analysis. The proposed methodology is demonstrated with real data from a cryogenic air separation plant in Tarragona (Spain). This approach also informs decision-makers about risk or expected shortfall associated with the implied scenario. The generic methodology used here can be easily adapted to schedule facilities in other energy-intensive sectors such as cement, metallurgy or pulp and paper. [Display omitted] [ABSTRACT FROM AUTHOR]

The industrial sector is the one that consumes the most energy in the world, whereas manufacturing activities play an important role in the energy consumption in the industry. The efficient scheduling/planning of production through intelligent operational methods using demand response (DR) can decrease the energy consumption and cost of a production process. However, the implementation of DR in industrial plants is more complex and challenging when compared with residential or commercial consumers. This is because the interruption of services can lead to the interruption of production and/or violation of operational constraints. In order to identify, classify, and summarize the DR applications in the industry, this work carries out an investigation in the form of a systematic mapping, comprising manufacturing and production processes that adopt this type of program. The literature review covers the period from 2013 to June 2022, with a focus on the implementation of DR in the manufacturing processes, as well as in the identification and classification of aspects related to the understanding and formulation of the problem and possible solution strategies. Unlike the existing literature that relates DR and industry, this study focuses specifically on the implementation of DR in manufacturing processes, identifying and classifying the main aspects related to problem modeling and solution strategies. After a rigorous analysis, a taxonomy is proposed based on 53 selected studies, emphasizing the deployment of DR in the industry. The taxonomy is summarized in terms of a graphical notation, highlighting the studies related to each category. Additionally, this work proposes a broader classification comprising loads and workstations in the industry. • Demand Response (DR) in industrial manufacturing processes is investigated. • 53 studies, that using DR, are analyzed in the period from 2013 to June 2022. • Characterization of an industrial application of DR in five practical aspects. • A new taxonomy is proposed for the implementation of industrial DR. • A broader classification of industrial loads/workstations in terms of five groups. [ABSTRACT FROM AUTHOR]

With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We show a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed $485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications. [ABSTRACT FROM AUTHOR]

The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated. Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT. [ABSTRACT FROM AUTHOR]

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance. [ABSTRACT FROM AUTHOR]

Hybrid flow shop scheduling problem (HFSP) has been extensively discussed and the main objectives are related to completion time. The reduction of energy consumption should be considered fully in HFSP in the era of green manufacturing. In this study, biobjective energy-efficient HFSP is considered, which is made up of three subproblems including scheduling, machine assignment, and speed selection. A three-string coding method is used to indicate solutions of three subproblems. A new teachers’ teaching-learning-based optimization (TTLBO) is proposed to minimize total energy consumption and total tardiness. Total tardiness is regarded as a key objective and a lexicographical method is adopted to compare solutions. TTLBO generates new solutions using a new optimization mechanism and is made up of the self-learning, interactive learning, and teaching of teachers. The learning phase of students are deleted from the algorithm. Multiple neighborhood searches are used to implement the self-learning of teachers and global search based on crossover is chosen to imitate other tivities of teachers. A number of experiments are conducted to test the impact of the new optimization meachanism on the performance of TTLBO and compare TTLBO with other algorithms from the literature. The computational results show that TTLBO is a competitive algorithm for the considered HFSP. [ABSTRACT FROM PUBLISHER]

In wafer fabrication, a robotic cluster tool is required to be closed down in order for engineers to perform its on-demand and preventive maintenance and switch between different wafer lots. They often deal with a close-down process subject to wafer residency time constraints, i.e., a wafer must exit from a processing chamber before its quality degradation within a certain time limit. To obtain higher yield, it is very important to optimize a close-down process for a cluster tool. Yet the existing literature pays no or little attention to this issue. By focusing on a time-constrained single-arm cluster tool, this paper intends: 1) to build its Petri net model to analyze its schedulability and 2) to develop computationally efficient algorithms to find an optimal and feasible schedule for its closing-down process under different workloads at its steps. Industrial examples are used to illustrate the application of the proposed method. [ABSTRACT FROM PUBLISHER]

We will discuss the joint optimization of the jobs sequence as well as inspection policy for a single system expected to process n jobs with different processing times. The system has a two-stage failures process, i.e., first a defect arises in the system, and if the defect is not detected, the system eventually fails. The interrupted job due to failure should be restarted after corrective replacement of the system. The possibility of inspecting the system before starting a job is considered to detect a potential defect. We develop two models to find the optimal policy based on either total expected makespan, or total expected cost. In the cost optimization model, we assume a common due date for all jobs and incur a penalty cost per unit time that the makespan exceeds the due date. We develop a recursive formula to obtain the expected makespan, and the number of failures and preventive replacements and present the application of our proposed models to a system, which is supposed to process four jobs. We compare the results of the direct calculation (recursive formula) with a Monte Carlo simulation model, and discuss how changing the models’ parameters can impact the optimal policy. [ABSTRACT FROM PUBLISHER]

Ammonia is a critical chemical to fertilize plants and feed the world. Recently, a first of its kind renewable ammonia plant has been built in Morris, MN, powered by wind energy. A broader deployment of such renewable plants will require careful selection of location and unit sizing to keep costs low and optimize the entire ammonia supply chain. In this paper, a 48-h receding horizon optimization problem is developed to optimally schedule unit set points for the system in order to minimize annual operating costs. This optimization formulation is then used to examine how the optimal operating cost depends on the key design parameters of location and unit size. Using the results obtained, simple correlations are developed which capture the dependence of operating costs on ratios of unit capacities, properly scaled to remove the dependence on location. [ABSTRACT FROM AUTHOR]

For coordinated multipoint transmission in heterogeneous ultra dense networks (HUDN), clustering and multicell resource scheduling should be carried out jointly. Based on the \boldsymbol\alpha -fairness function, the joint optimization problem is formulated that is NP-hard. A two-step joint clustering and scheduling (TS-JCS) scheme is proposed. Considering the extremely imbalanced cell loads in HUDN, the impact of cell loads on resource availability must be concerned, and a novel load aware clustering (LAC) is designed at the first step and solved by game theory. Based on the clustering results, a fair graph-coloring based inter-cell resource scheduling can be employed at the second step to maximize the resource utilization. Simulations are carried out to evaluate the performance of TS-JCS in a three-layer HUDN. It is shown that the utility of TS-JCS is close to the optimal value, demonstrating the high effectiveness of TS-JCS to solve the joint optimization problem. Compared with non-joint schemes, when fairness is considered in TS-JCS with \boldsymbol\alpha  = 1 and \boldsymbol\alpha  = 2, the average throughput can be improved by about 30% while keeping similar performance in cell-edge throughput. Finally, it is also shown that in HUDN with imbalanced load, the performance gain of TS-JCS with \boldsymbol\alpha  = 1 and \boldsymbol\alpha  = 2 over the baseline scheme is obvious compared to that in regular hexagonal networks with uniformly distributed loads, demonstrating the importance of the load aware scheme LAC for HUDN. [ABSTRACT FROM AUTHOR]

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using wireless power transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, whereas the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes’ energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semidecentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance. [ABSTRACT FROM AUTHOR]

In the 4G and forthcoming 5G, the throughput is largely improved. Let edge users be the users beyond a certain distance to the associated BS in their covering cell, where the receiving intended signal is relative small. Throughput of edge users is the key metric measuring system performance. Originally designed for improving the performance of edge users under different scenarios, many technologies of user scheduling and power allocation, such as coordinated multipoint and Nash noncooperative power game are proposed. However, the existing algorithms are generally heuristic leading to a large performance gap in different scenarios. In this paper, we formulate a joint optimization to integrate user scheduling and power allocation into a unified optimization framework. By proposing a novel power-fractionizing mechanism, binary optimization can be avoided and the scheduling can be incorporated into the power allocation process. Thereafter, the joint optimization problem is then transformed into a signomial programming (SP) problem, which is further approximated by iteratively solving a series of tractable geometric programming problems that can converge to at least a local optimum of the SP problem. Unlike the existing heuristic algorithms dedicated to specific scenarios, our algorithm is more robust and environment adaptive. Simulation results show that the proposed joint optimization outperforms the existing heuristic approaches by significantly improving the total edge user throughput with stable performance. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal scheduling problem in coordinated multipoint (CoMP) transmission-based cellular networks. We consider joint transmission and coordinated scheduling together in CoMP transmission-based cellular networks and develop an optimization framework to compute the optimal max-min throughput and the optimal scheduling of the transmissions to the users. The optimization problem is found to be a complex linear program with number of variables in [ABSTRACT FROM AUTHOR]

This paper reports the development of a two-level optimization methodology to help design a tri-generation system for a given district which satisfies the heating, cooling, and hot water demands and at the same time, minimize the annual total costs and CO 2 emissions. An optimization methodology is proposed and tested on a virtual district with eight buildings where three of them can host the district technologies including heat pump, gas engine, and lake cooling. Within the building, some backup technologies may be implemented including an air/water heat pump, a water/water heat pump, a boiler, and electric chillers. Analysis of the Pareto optimal frontier results in several distinct groups of configuration based on the selected district conversion technologies and their capacities. Solution to the sub-problems including design and operation of the district energy system is carried out by applying a Mixed Integer Programming (MIP) technique. Several different clusters are defined and studied regarding the cost and CO 2 emission. A reference configuration is defined for the purpose of comparison in which electricity is supplied by the grid, heating and hot water by a boiler, and cooling by an electric chiller. Compared to this configuration, the best solution with respect to CO 2 emissions causes 59% emission and 75% cost of the reference configuration. In this case, 53% of the total cost is associated with the initial investment cost while the rest 47% is associated with the operational cost. The optimal configuration with respect to the annual costs causes 86% more emission than the reference configuration and 38% less annual costs. In this case, 22% of the total cost is associated with initial investment cost while 78% of the total cost is associated with the operational cost. Implementation of a two-pipe system instead of a four-pipe system results in nearly 5% reduction in total annual cost. [ABSTRACT FROM AUTHOR]

This paper presents an agent cooperation mechanism for scheduling operations in a manufacturing network, while allowing manufacturers to absolutely control their scheduling activities. The study includes a thorough review of recent publications, a real-life industrial use case of a manufacturing network, an agent-based model of the network simulated with recursive porous agent simulation toolkit, the Muth and Thompson (MT10) scheduling data set, and the visualization of results in Microsoft Project. Results of a study of a four-layer cooperation mechanism showed that for the MT10 problem, manufacturer arrangement 0–5–7–2–3–8–1–9–6–4–0 was found to maximize the utilitarian social welfare of the manufacturing network. In terms of make-span, the network achieved a maximum of 1125 which was beyond the known optimal 930. Results suggest that manufacturers could express their scheduling goals and their preferences with whom they wanted to cooperate. These were measured by the time incentive and compatibility indicators. The latter could also be used to track the optimality loss in make-span optimization when implementing the decentralized scheduling approach in the context of manufacturing networks. [ABSTRACT FROM PUBLISHER]

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem. [ABSTRACT FROM AUTHOR]

As an emerging novel evolutionary technique, the immune algorithm has gained a lot of attention and wide applications in various fields of engineering and management. To our knowledge, this paper first considers the application of immune algorithm for the classic permutation flow shop scheduling problem. We present a hybrid heuristic combining immune algorithm and simulated annealing for the n-job, m-machine permutation flow shop scheduling problem to minimize makespan and total flow time. A heuristically directed population-based construction approach in the immune algorithm and a forward/backward shift neighborhood search heuristic in the simulated annealing of the proposed method is utilized to explore the search process for better schedules of jobs as well to speed up the convergence speed of the proposed algorithm. The proposed method is tested with Taillard's flow shop scheduling benchmark instances for different problems with job sizes varying from 20 to 500. The computational results demonstrate that the proposed heuristic is very competitive with the state-of-the-art procedures in terms of both solution quality and computational times. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the low-power scheduling problem with multiple threshold and/or supply voltage technologies in high-level synthesis. We propose a unified scheduling approach which is applicable to various optimization problems, including: 1) dynamic power and resource usage co-optimization; 2) leakage power optimization; and 3) dynamic power and leakage power co-optimization. To deal with different objectives with high flexibility, three problems are divided into two common subproblems including delay assignment and resource density variance minimization, then a vertex potential-based mobility allocation model is proposed to solve two subproblems simultaneously. Experimental results show that, for dynamic power and resource co-optimization, our scheduling approach produces optimum solutions for all six benchmarks with 15 groups of data; for leakage power optimization it also greatly excels the latest existing work, by 20% leakage power reduction and 52 times speedup. Besides, for dynamic and leakage power co-optimization, the Pareto solutions are studied. [ABSTRACT FROM PUBLISHER]

We tackle the problem of minimizing the maximum expected delivery time of all transmission pairs in 60 GHz mmWave networks. The network is dual-hop with multiple transmitter-receiver pairs, relays, and a centralized controller (termed PicoNet coordinator). We jointly optimize relay and link selection for the transmitter–receiver pairs to minimize delivery time, while the reflected non-line-of-sight transmission links are exploited to get around obstacles. To reduce computational complexity, we develop a decomposition principle to transform the joint-optimization problem into a link selection subproblem and a relay assignment subproblem when there are a sufficient amount of relays. A tight performance bound for the proposed algorithm is proved. We also develop a heuristic scheme to handle the case when there are no enough relays. The superior performance of our proposed algorithms is validated with extensive simulations and comparisons with benchmark schemes. [ABSTRACT FROM AUTHOR]

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines. [ABSTRACT FROM AUTHOR]

As demands for cloud-based data processing continue to grow, cloud providers seek effective techniques that deliver value to the businesses without violating Service Level Agreements (SLAs). Cloud right-sizing has emerged as a very promising technique for making cloud services more cost-effective. In this paper, we present CRED, a novel framework for cloud right-sizing with execution deadlines and data locality constraints. CRED jointly optimizes data placement and task scheduling in data centers with the aim of minimizing the number of nodes needed while meeting users’ SLA requirements. We formulate CRED as an integer optimization problem and present a heuristic algorithm with provable performance guarantees to solve the problem. Competitive ratios of the proposed algorithm are quantified in closed form for arbitrary task parameters and cloud configurations. We also extend our work to obtain a resilient solution, which allows successful recovery at run time from any single node failure and is guaranteed to meet both deadline and locality constraints. Simulation results using Google trace show that our proposed algorithm significantly outperforms existing heuristics such as first-fit by reducing the number of required active servers by up to 47 percent, and achieves near-optimal performance. We also show that our algorithm can significantly improve utilization of both computational resources and storage space by up to 28 and 15 percent, respectively. [ABSTRACT FROM AUTHOR]

The examination timetabling problem (ETTP) consists in the assignment of specific dates to the exams of a set of courses assuming that the course enrollments are known. This problem is also known as post-enrollment ETTP. In this paper, we describe and solve a variant of the ETTP which has two particularities: (1) it does not assume the course enrollments as known and uses the curriculum of the degree program to evaluate potential conflicts in the exam schedules, and (2) it considers the exams and classrooms of multiple degree programs simultaneously. We refer to this variant of the ETTP as curriculum-based examination timetabling problem (CB-ETTP), a problem faced by many universities worldwide, being the Universidad Diego Portales (UDP) in Santiago of Chile one of them. To the best of our knowledge, this problem has not been described as such in the ETTP literature. We propose an approach to solve the CB-ETTP consisting of four sequential stages. The first stage groups courses into clusters and generates classroom configurations called room patterns. The second stage assigns time slots and room patterns to course clusters. Then, the third stage assigns time slots and room patterns to individual courses. Finally, the fourth stage generates a definitive exam schedule assigning specific rooms to each course exam. We evaluate the performance of the proposed approach by applying it to real-world instances generated based on data provided by the Faculty of Engineering at the UDP. The results show a reduction in the number of conflicts and rescheduling with respect to the current exam scheduling practice used by this university. [ABSTRACT FROM AUTHOR]

An integrated dynamic optimization strategy is presented for block copolymerization processes. First the reactor model, which has unstable modes and may lead to unbounded profiles under certain design and operating conditions, is derived. Second, optimal recipes for operating of the copolymerization process are determined. Finally, an optimization strategy for the integration of scheduling and dynamic process operation for general continuous/batch processes is considered. The resulting approach leads to significant improvements in productivity, while maintaining safe operation and satisfaction of product specifications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new Integer Linear Programming model, based on a time-space network that integrates the timetable generation problem and the vehicle scheduling problem with heterogeneous fleet. A difference of this approach consists in considering the demand for the timetable redefinition and the vehicle scheduling, factor rarely applied in optimization models of the transportation system. We applied real and large random instances. The results indicate that the model may contribute to optimizing the public transport planning leading to significant savings in terms of scheduled vehicles. Moreover, as the timetable changes are fairly short, it is slightly modified, minimally modifying the passengers routine, which enables the application of these approaches to real context. [ABSTRACT FROM AUTHOR]

In this paper we explore tramp ship routing and scheduling. Tramp ships operate much like taxies following the available demand. Tramp operators can determine some of their demand in advance by entering into long-term contracts and then try to maximise profits from optional voyages found in the spot market. Routing and scheduling a tramp fleet to best utilise fleet capacity according to current demand is therefore an ongoing and complicated problem. Here we add further complexity to the routing and scheduling problem by incorporating voyage separation requirements that enforce a minimum time spread between some voyages. The incorporation of these separation requirements helps balance the conflicting objectives of maximising profit for the tramp operator and minimising inventory costs for the charterer, since these costs increase if similar voyages are not performed with some separation in time. We have developed a new and exact branch-and-price procedure for this problem. We use a dynamic programming algorithm to generate columns and describe a time window branching scheme used to enforce the voyage separation requirements which we relax in the master problem. Computational results show that our algorithm in general finds optimal solutions very quickly and performs much faster compared to an earlier a priori path generation method. Finally, we compare our method to an earlier adaptive large neighbourhood search heuristic and find that on similar-sized instances our approach generally uses less time to find the optimal solution than the adaptive large neighbourhood search method uses to find a heuristic solution. [ABSTRACT FROM AUTHOR]

We consider a strategic game, where players submit jobs to a machine that executes all jobs in a way that minimizes energy while respecting the given deadlines. The energy consumption is then charged to the players in some way. Each player wants to minimize the sum of that charge and of their job's deadline multiplied by a priority weight. Two charging schemes are studied, the proportional cost share which does not always admit pure Nash equilibria, and the marginal cost share , which does always admit pure Nash equilibria, at the price of overcharging by a constant factor. [ABSTRACT FROM AUTHOR]

Continuous-time scheduling models for multiproduct pipelines typically view the contents of a pipeline as a set of batches. It facilitates tracking batch coordinates but prevents rigorously enforcing forbidden product sequences when dealing with intermediate dual-purpose nodes. Such models require the initial characterization of the pipeline and total number of batches as inputs, decisions that can be non-trivial. To overcome these limitations, this paper presents a new mixed-integer linear programming (MILP) formulation for straight pipelines with a single tuning parameter, the number of event points in the grid. It is derived from Generalized Disjunctive Programming (GDP) followed by a convex hull reformulation. Compared to a product-centric formulation based on the Resource-Task Network (RTN), it is much simpler, smaller in size and can be up to two orders of magnitude faster. Comparison to its batch-centric counterpart is not as favorable, the highlight being an 8% improvement in makespan for a benchmark problem. [ABSTRACT FROM AUTHOR]

The paper considers non-repairable 1-out-of- N heterogeneous warm standby computing systems with components exposed to internal failures and external shocks. To provide the data recovery in the case of operating component failure, the backup procedures are performed during the computational mission. The backups enable an activated standby component to take over the mission task from the point where the last backup has been completed without redoing the entire task from scratch. Both data backup and retrieval times depend on the amount of work performed. The system components are characterized by a different performance level, replacement time, time-to-internal failure distribution, and shocks survival probability. The shock processes also have different characteristics for different components. A numerical method is proposed to evaluate mission success probability for a given allowed mission time and expected mission completion time. The optimal backup scheduling problem is then formulated and solved for different optimization objectives and constraints. [ABSTRACT FROM AUTHOR]

Flow Shop Scheduling Problems circumscribes an important class of sequencing problems in the field of production planning. The problem considered here is to find a permutation of jobs to be sequentially processed on a number of machines under the restriction that the processing of each job has to be continuous with respect to the objective of minimizing the completion time of all jobs, known in literature as makespan or Cmax. This problem is as NP-hard, it is typical of combinatorial optimization and can be found in manufacturing environments, where there are conventional machines-tools and different types of pieces which share the same route. The following research presents a Reinforcement Learning algorithm known as Q-Learning to solve problems of the Flow Shop category. This algorithm is based on learning an action-value function that gives the expected utility of taking a given action in a given state where an agent is associated to each of the resources. To validate the quality of the solutions, test cases of the specialized literature are used and the results obtained are compared with the reported optimal results. [ABSTRACT FROM AUTHOR]

With increasing concerns about worldwide environmental conditions and rapid development of renewable energy technologies, microgrids have been regarded as a promising solution to reduce the burden of infrastructure-based power systems. However, due to the intrinsically intermittent features of existing renewable energy, along with random residential behavior patterns, unpredictable plugged-in or unplugged actions of electric vehicles (EVs) and the time-varying price of electricity, it is challenging for microgrid operators to efficiently perform load scheduling and energy management. In this paper, we propose an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids. We formulate this problem into a stochastic optimization problem with the objective of minimizing the time-average cost of a microgrid, including the purchase cost of electricity from the main grid, the cost of charging and discharging batteries, renewable harvesting costs, and life-cycle greenhouse-gas emission costs. To solve this problem, the key idea is to exploit the dynamics of the price of electricity to conduct battery charging and discharging operations, renewable energy harvesting, and schedule EV loads properly. Our method is based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information. The theoretical analysis of our algorithm confirms that the proposed strategy can achieve optimality with explicit bound. By conducting extensive real-data driven simulations, we demonstrate that our proposed algorithm can achieve much lower cost and be more eco-friendly than other alternative solutions. [ABSTRACT FROM PUBLISHER]

Dark silicon refers to the phenomenon that a fraction of a many-core chip has to become “dark” or “dim” in order to guarantee the system to be kept in a safe temperature range and allowable power budget. Techniques have been developed to selectively activate non-adjacent cores on many-core chip to avoid temperature hotspot, while resulting unexpected increase of communication overhead due to the longer average distance between active cores, and in turn affecting application performance and energy efficiency, when Network-on-Chip (NoC) is used as a scalable communication subsystem. To address the brand-new challenges brought by dark silicon, in this paper, we present FoToNoC, a Folded Torus-like NoC, coupled with a hierarchical management strategy for heterogeneous many-core systems. On top of it, objectives of maximizing application performance, energy efficiency and chip reliability are isolated and well achieved by hardware-software co-design in several different phases, including application mapping and scheduling, cluster management and DVFS control. Evaluations on PARSEC benchmark applications demonstrate the significance of the entire strategy. Compared with state-of-the-art approaches, the proposed FoToNoC organization can achieve on average 35.4 and 35.2 percent on communication efficiency and application performance improvement, respectively, when maintaining the safe chip temperature. The hierarchical cluster-based management strategy can further reduce an average 34.6 percent of the total energy consumption with a notable reduction on the chip peak temperature. The significant achievements on system energy efficiency and the reduction on chip temperature of H.264 decoder and DSP-stone benchmarks additionally verify the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

Fog–Cloud computing environments have become attractive focus areas for scheduling workflow automation of different processes in different domains. The planning of workflow containing tasks having different characteristics, in Fog-Cloud computing, can affect the gain in terms of resources. The main objective of this article is to efficiently schedule a workflow in such heterogeneous environments under conflicting constraints between the user and the resource provider. In this paper, we propose a new approach to designing an environment that combines partitioning, sequencing, and scheduling algorithms while guaranteeing a multi-objective optimization of users' and providers' conflicting requirements. Our approach also integrates a distributed resolution with Multi-Agent System (MAS), in order to reduce the complexity of the problem, and a fuzzy inference system to deal with the uncertainty problem. To study and confirm the effectiveness of our approach, we conducted an experimental study on different data collections. We compared our approach with the main related approaches that we studied. The analysis of the obtained results clearly shows the limits of these studied approaches and confirms the performance of our approach, as well as its effectiveness in planning workflows on Fog–Cloud computing while taking into account the utilization of resources. In terms of Makespan, our solution recorded a reduction of 48% compared to the Energy Makespan Multi-Objective Optimization (EM-MOO) approach, and 41% compared to the Multi-Agent System based Genetic Algorithm (MAS-GA), while respecting the time constraint. [ABSTRACT FROM AUTHOR]