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An efficient scheduling of dependent-tasks over edge-cloud is the key for achieving better utilization of computational resources and timely completion of interdependent tasks for scientific as well as defence-oriented applications. Many applications comprise several tasks that are dependent in nature and are required to be executed in a specific sequence within a minimum span of time. To handle the execution of such dependent-tasks on distributed computational resources is a challenging problem; as the number of tasks increase, the solution space comprising different task-resource mapping sequences also increase exponentially and it is very difficult to find the near optimal solutions in the search space. In this paper, we focus on two strategies for obtaining optimal solutions for scheduling the multiple dependent-tasks with the specified sequence in a parallel and distributed environment. In the first approach, a hybrid mechanism is proposed to efficiently search the scheduling solution space for multiple dependent tasks. The idea is to first find a schedule by heuristic algorithms and use these as initial solutions in the search space to obtain better solutions using unsupervised machine learning methods. In the second approach, each task-graph is partitioned into different clusters of sub-tasks, where each partitioned cluster is mapped onto the same resource. This strategy reduces the idle times on the resources, wherever possible, in comparison with the first approach. The innovative part of the proposed approaches is to schedule the multiple dependent-tasks in a parallel fashion rather than scheduling in sequential manner. The results show that the schedules obtained by our proposed approaches minimize the total execution time (TET) significantly as compared to other approaches considered in our research study. [ABSTRACT FROM AUTHOR]

Time-sensitive networks (TSNs) provide reliable, deterministic message delivery, ideal for networked control systems. Such networks are increasingly implemented in dynamic, resource-constrained avionics environments. This places pressure on the TSN scheduler to compute network flow schedules faster with slower processors, less memory, and limited power consumption. Avionics full-duplex switched Ethernet and IEEE 802.1 Time-Sensitive Networking are briefly compared and found to be largely similar in concept except for mechanisms enforcing determinism (BAG versus IEEE 802.1Qbv gate control) and the added requirement for time synchronization in time-sensitive networking. A new form of reliability known as polymorphic reconfiguration is introduced and contrasted with static, preconfigured redundancy. Scalability issues impacting TSN scheduling for polymorphic reconfiguration are derived. Preconfiguring and storing the TSN schedule for every possible network failure is infeasible in terms of memory storage. IEEE 802.1CB and other redundancy mechanisms exacerbate the problem by further increasing the number of flows that have to be reconfigured and scheduled. The computational complexity of TSN scheduling is reviewed and the notion of a processing-storage tradeoff via a schedule seed is introduced. The resource constrained scheduling challenge is addressed by analyzing tradeoffs in efficient scheduling approaches where qualitative performance metrics are inferred, high-level computational methods are compared, and suggestions for future approaches are outlined. This article contributes to time-sensitive networking scheduling capable of operating in a resource-constrained, dynamically changing network environment rather than in fixed avionics network environments to which they are limited today. Finally, the need for standards for TSN scheduling performance profiling and benchmarks as well as partial schedules and distributed TSN scheduling protocol standards is highlighted. [ABSTRACT FROM AUTHOR]

The continuing growth in space debris has posed a great threat to on-orbit operations. It is urgent to implement reliable and lasting monitoring of space debris. Safety and diversity in monitoring devices and business demands makes scheduling system resources increasingly complicated. This article proposes a novel adaptive multipopulation differential evolutionary algorithm based on a theoretical model specialized in the scheduling of space debris monitoring resources. Using Q-learning, the proposed algorithm adapts self-learning and dynamic adjustment properties in population proportion parameters. Experiments are performed with practical batch tasks and monitoring data to verify the effectiveness and reliable utility of the proposed algorithm to ensure the safety of on-orbit operation. [ABSTRACT FROM AUTHOR]

Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered - the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes. [ABSTRACT FROM AUTHOR]

We consider an optimal denial-of-service (DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However, due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning (DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process (MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm. [ABSTRACT FROM AUTHOR]

Coordinated multipoint (CoMP) has received considerable attention as a promising technology to improve the transmission rates and spectral efficiency of cell edge users for future networks. How to design coordinated scheduling through a cross-layer approach is challenging in CoMP systems. In this paper, a pilot-efficient scheduling policy is presented in CoMP systems with large-scale antennas. Our policy selects users to access the spectrum and allocates the power to users based on the queue state information (QSI) and channel state information (CSI). Based on the Lyapunov optimization, the cross-layer scheduling problem can be modeled as a combinatorial optimization problem, which is nontrivial. To solve this problem, we decouple the combinatorial optimization problem as a user selection problem and a power allocation problem. Then a low-complexity iteration algorithm is proposed to solve the combinatorial optimization problem. Simulation results demonstrate that our presented policy has better performance over traditional methods. Moreover, by comparing to the exhaustive search algorithm, the performance of our proposed policy is similar to that of an optimal policy. [ABSTRACT FROM AUTHOR]

With the stringent requirement of receiving video from the unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access-points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposing the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results show that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architecture is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

People have been migrating to various locations in search of better living. This is why they began to adopt and innovate multiple modes of transportation since the dawn of civilization. In the modern era, airline sector has transformed into a convenient mode of transportation due to time limitations of passengers. Given the dynamics of the airline sector and drastically changing global environment, SAUDIA airline realized the need of the time to revamp its operational capacities and strategies to deal with fierce competition. SAUDIA serves both domestic and international markets with huge customer facilitation; however, recently it has gone through severe competition and a subsequent loss of market share due to its slow response to the dynamics of the market. Therefore, understanding the constituent elements to revamp its network is crucial for SAUDIA to regain its market share so that it may attain its desired revenues. Keeping in view, this study is an endeavor to propose a new schedule based on a synergy of SWOT analysis, waves for the SAUDIA schedule, premium market analysis and nested logit model to overcome these problems. The proposed framework utilizes the shape of the waves under specific constraints to generate improved schedule by calculating market share and to decide based on nested logit regression model. The implication of this new schedule requires meeting several constraints such as the availability of aircraft within allocated time-windows, minimum connection time and minimum usage rate per aircraft per day. Findings of the study suggest that the proposed schedule is expected to increase total transit traffic by 48 percent with a 30 percent increase in the number of sources and destinations by SAUDIA, whereas it will enhance the overall fleet utilization by 12.5 percent. Additionally, it is anticipated that the proposed schedule would surge the revenues by 17 percent as compared to SAUDIA's regular weekly plan. [ABSTRACT FROM AUTHOR]

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer. [ABSTRACT FROM AUTHOR]

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission's quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit. [ABSTRACT FROM AUTHOR]

Multisensor scheduling, a multi-objective optimization problem, simultaneously aims to find the optimal matching between sensors and targets in a certain period, that is, to generate the optimal multisensor scheduling scheme. Several sensor scheduling literatures proposed multisensor scheduling schemes incorporating various algorithms, but the majority of them dealt with sensors to targets in two dimensions. First, the visual relationship between sensors and UAV cluster targets is investigated, and a three-dimensional correspondence between sensors, targets, and time is designed while dividing the scheduling time. Secondly, the detection advantage of sensors to UAV cluster, sensor load, and sensor handover numbers in the scheduling time interval are summed to create multisensor scheduling objective functions for UAV cluster. Furthermore, for the UAV cluster optimization problem, a hybrid optimization algorithm is proposed, which integrates the golden sine mechanism and adaptive weight factor in Harris hawks optimization algorithm. Finally, extensive experimental and statistical analyses show that the models and algorithms proposed in this paper outperform the existing ones. It has better convergence, does not need additional computational cost, and generates better multisensor scheduling schemes. [ABSTRACT FROM AUTHOR]

Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally scheduled on the physical cloud resources to obtain maximal performance. In the current paper, NP-Hard cloud task scheduling (CTS) problem for scheduling concurrent BoT applications is modeled as an optimization problem involving minimization of makespan and energy consumption. Whale optimization algorithm (WOA) has been found effective in solving a wide range of optimization problems. However, standard WOA has certain deficiencies such as inadequate exploration ability, slow convergence, high computation complexity, and insufficient exploration–exploitation phase trade-off. These deficiencies eventually result in unacceptable results when the original WOA is applied over task scheduling optimization problems. To address these limitations, a multi-objective scheduling algorithm called OWPSO is suggested, which incorporates opposition-based learning (OBL) and particle swarm optimization (PSO) mechanisms into the standard WOA method. Firstly, the OBL method is applied to produce an optimal initial population to enhance the exploration and convergence speed of the proposed OWPSO approach in the successive generations. Secondly, PSO and OBL methods are incorporated in the exploration phase of the standard WOA approach to enhance exploration ability further. Thirdly, a fitness-based switching mechanism is added to provide an adequate exploration–exploitation phase trade-off. Finally, a discrete resource allocation heuristic is incorporated in the OWPSO to provide an efficient resource allocation. Simulation experiments over the CloudSim simulator reveal that OWPSO approach results in makespan reduction in the range of 1.68−18.38% (for CEA-Curie workloads), 2.10−24.32% (for HPC2N workloads), and energy consumption reduction in the range of 0.93−14.70% (for CEA-Curie workloads), and 0.73−25.94% (for HPC2N workloads) over other well-known meta-heuristics. Statistical tests and box plots further revealed the robustness of proposed OWPSO algorithm. [ABSTRACT FROM AUTHOR]

• Demand responsive freight deliveries are optimized with TSP tour length approximations. • Headways and zone areas for flexible‐route delivery systems are jointly optimized and closed‐form solutions are obtained. • The problem is reformulated as a Lagrangian relaxation problem to constrain an integer fleetsize. • The single‐zone model presented in this paper can be the modular basis for a multi‐zone model with possible freight flows among all zones. • The sensitivities of results to important input factors are analyzed. Freight delivery planning is a critical area in transportation and logistics. This paper presents a planning-level model for jointly optimizing headways and zone sizes for flexible-route freight deliveries. The model analyzes deliveries for a one-to-many demand pattern from one terminal to random destinations in a zone, which are reached through approximated travelling salesman problem (TSP) tours. Then, the problem is reformulated as a Lagrangian relaxation problem to constrain the fleet size to an integer value. The proposed model and closed-form solutions provide direct insights into the relations affecting those decision variables. Results from the base case study and sensitivity analyses indicate how various factors affect optimal service frequencies, delivery zone sizes, and costs of freight deliveries. Before detailed vehicle routing problems (i.e., actual delivery scheduling and operations) are solved, the proposed model should be useful for planning freight delivery systems, regarding critical decisions about geographic coverage of services, delivery frequencies, vehicle capacities, and other system characteristics. The developed model can also be used for pickups with a many-to-one demand pattern and extended to serve many-to-many demand patterns in multiple delivery zones through transfers at a central terminal. [ABSTRACT FROM AUTHOR]

Magnetization transfer contrast MR fingerprinting (MTC‐MRF) is a novel quantitative imaging method that simultaneously quantifies free bulk water and semisolid macromolecule parameters using pseudo‐randomized scan parameters. To improve acquisition efficiency and reconstruction accuracy, the optimization of MRF sequence design has been of recent interest in the MRF field, but has been challenging due to the large number of degrees of freedom to be optimized in the sequence. Herein, we propose a framework for learning‐based optimization of the acquisition schedule (LOAS), which optimizes RF saturation‐encoded MRF acquisitions with a minimal number of scan parameters for tissue parameter determination. In a supervised learning framework, scan parameters were subsequently updated to minimize a predefined loss function that can directly represent tissue quantification errors. We evaluated the performance of the proposed approach with a numerical phantom and in in vivo experiments. For validation, MRF images were synthesized using the tissue parameters estimated from a fully connected neural network framework and compared with references. Our results showed that LOAS outperformed existing indirect optimization methods with regard to quantification accuracy and acquisition efficiency. The proposed LOAS method could be a powerful optimization tool in the design of MRF pulse sequences. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy. [ABSTRACT FROM AUTHOR]

The reconfigurable manufacturing system is an advanced field of research that has surpassed the efficiency of other manufacturing systems due to its high throughput, cost-effectiveness, and ability to accommodate product variety. An important problem addressed in the field of reconfigurable manufacturing systems is process planning which assigns configurations to different operations. Process planning has been the focus of research for almost two decades; however, a comprehensive review is lacking to highlight the possible streams of future contributions to this field of research. To this end, this study presents a systematic review of the process planning in a reconfigurable manufacturing system with a focus on optimization efforts. This review is organized in two interconnected phases, i.e., a theoretical phase and an implementation phase. The theoretical phase reviews the concerned literature regarding the levels of analysis, reconfigurable manufacturing system (RMS) characteristics, different research themes, and change agents. On the other hand, the implementation phase reviews the literature regarding the use of different objective functions, constraints, solution approaches, nature of problem/solution, and the use of industrial applications. Several future research streams are provided which can guide, in a broader sense, the advancement of process planning literature. As a demonstration, quality, supply chain, and human/operator issues are highlighted to advance the scope of applicability of the concerned literature. A thorough analysis of the operator aspects in scheduling literature is presented which can benefit practitioners working in a changeable/reconfigurable manufacturing environment. Moreover, practical process planning approaches are offered to analyze the cost, time, and quality aspects. Finally, the conclusion of the study is provided. [ABSTRACT FROM AUTHOR]

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time. Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows. [ABSTRACT FROM AUTHOR]

5G NR is designed to operate over a broad range of frequency bands and support new applications with ultra-low latency requirements. To support its extremely diverse operating conditions, multiple OFDM numerologies have been defined in the 5G standards. Under these numerologies, it is necessary to perform scheduling with a time resolution of $\sim 100 \mathrm {\mu s}$. This requirement poses a new challenge beyond existing LTE and cannot be satisfied by any existing LTE schedulers. In this paper, we present the design of GPF+, which is a GPU-based proportional fair (PF) scheduler with timing performance under $100 \mathrm {\mu s}$. GPF+ is an improvement over our GPF in Huang et al. (2018). The key ideas include decomposing the original scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF+ on an off-the-shelf NVIDIA Tesla V100 GPU, we show that GPF+ is able to achieve near-optimal PF performance with timing performance under $100 \mathrm {\mu s}$. GPF+ represents the fastest GPU-based PF scheduler that can meet the new real-time requirement in 5G NR. [ABSTRACT FROM AUTHOR]

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution. [ABSTRACT FROM AUTHOR]

In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. [ABSTRACT FROM AUTHOR]

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures. [ABSTRACT FROM AUTHOR]

Copyright of Zeszyty Naukowe SGGW: Ekonomika i Organizacja Logistyki is the property of Warsaw University of Life Sciences (SGGW), Faculty of Economic Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a novel optimization problem for simultaneous capacity planning and scheduling in reservation scheduling environments is proposed. The problem is important for seasonal reservation systems such as hotel or seat reservations of travel agencies, or operation and treatment reservations in health tourism. The objective of the problem is to maximize the net profit gained from the processed reservations. To the best of our knowledge, the problem was not previously studied. An integer programming model is developed for exact solutions and extensive computational experimentation reveals model performance under different scenarios. The results are analyzed, and managerial implications are discussed.

Optimal production scheduling in off-site construction is hindered by the lack of comprehensive methods that simultaneously addresses workforce allocation, realistic labor productivity, workstation idle time (WIT), work-in-progress (WIP), and project completion time (PCT). To overcome these limitations, this paper introduces a hybrid planning and scheduling method that integrates the linear scheduling method reflecting workforce allocation, Monte Carlo simulation for estimating realistic labor productivity, and a multi-objective optimization model to minimize WIT, WIP, and PCT concurrently. The proposed method is validated using the Taillard instance benchmark dataset, and a panelized production line located in Edmonton, Canada. As a result, the proposed method is effective in generating realistic and optimal production schedules, achieving reductions of 5.2 % in PCT, 41.45 % in WIT, and 56.42 % in WIP. These findings are crucial for industry professionals aiming to improve efficiency in the production line and provide a foundation for further research in off-site construction scheduling. • A hybrid scheduling method is proposed for off-site construction (OSC) manufacturing. • Advanced linear scheduling method is proposed to create production schedule in OSC. • A multi-objective optimization model is proposed using NSGA-II. • Probabilistic process time is estimated using Monte Carlo simulation. • Proposed method can be used to generate an effective and optimum production schedule in OSC. [ABSTRACT FROM AUTHOR]

This study investigates the economic and resilience co-optimization of a decentralized hybrid energy system (HES) within scenarios involving limited energy sources and a hybrid energy storage solution. The HES is comprised of a building-integrated Photovoltaic (PV) system incorporating an adiabatic compressed air energy storage (A-CAES) and batteries, with the main grid, serving as a backup. A two-stage sizing-scheduling model is proposed to optimize the configuration, minimize lifetime costs, and enhance both long and short-term resiliency while achieving an optimal schedule. The charging/discharging transition time of A-CAES is also adopted in the operational model. The results indicate a substantial annual resiliency improvement of approximately 41.1 %, with optimal sizing and energy storage integration. Additionally, they highlight the cost-effectiveness of the PV/A-CAES system while emphasizing the higher self-sufficiency achieved by the PV/A-CAES/battery system, attaining an electrical load management ratio of 47.3 % and a PV self-consumption rate of 96 %, a 6 % improvement over HESs with individual A-CAES. Furthermore, it is presented that under an optimal operational condition, even with the highest PV power availability during grid interruptions, HES relying solely on individual A-CAES could meet 94 % of the load demand. Integrating a fast-response battery enhances this resiliency to 100 %, effectively reducing PV-power curtailment, particularly during grid interruptions and A-CAES transitions. • The resilience of PV-based HES under limited grid dependency is studied. • A sizing-scheduling approach to economic and resilience co-optimization is developed. • Adopting energy storage units results in a 41 % annual improvement in resiliency. • Ideally, PV/A-CAES can fulfill 94 % of the daily load demand during a grid failure. • Hybrid storage systems reduce daily operational costs and enhance daily resilience. [ABSTRACT FROM AUTHOR]

The scheduling of university exams is a complex task that involves various constraints such as administrative limits, pedagogical needs, student volume, and different courses. The emergence of Covid-19 and future pandemics has added new constraints related to infection prevention and contact tracing. To address these challenges, this study proposes a multi-objective mathematical model that considers university resources, reduced classroom occupancy, and minimized student interaction. The model aims to minimize violations of pandemic-related constraints and categorize exams by difficulty. To facilitate scheduling for entire faculties or universities, a Genetic Algorithm based web-based decision support system is developed. With these tools, the study successfully created an optimal schedule for eight departments simultaneously. • This paper proposes a new MIP model for university exam scheduling problem. • The proposed soft constraints consider pandemic and pedagogical conditions. • The proposed formulation can solve small sized problems using commercial software. • We propose a Genetic Algorithm based DSS to solve faculty sized problem. • The proposed heuristic obtains optimal and close-to-optimal solutions. [ABSTRACT FROM AUTHOR]

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms. • Multi-objective CSO. • Fault tolerance. • Load balancing. • Task scheduling. • High resource utilization. [ABSTRACT FROM AUTHOR]

The multiproduct pipeline network with multi-source and multi-terminal encompasses multi-point injection and multi-point distribution. Upon reorganization of batches at transfer depots, the intricacies of precise batch tracking become increasingly complex, thereby rendering the optimization of multiproduct pipeline network scheduling particularly arduous. In this paper, we introduce a dynamic index batch numbering method. By delineating the boundaries of intermediate injection points, the issue is bifurcated into two sub-problems: the optimization of batch planning for multiproduct pipeline with single-source and multi-terminal and the optimization of resource allocation at intermediate oil depots. The proposed algorithm is validated on two genuine multiproduct pipeline networks in China, demonstrating its capability to generate feasible solutions within a computational constraint of 18,000 CPU seconds. The findings reveal that this methodology holistically considers the entire pipeline network system, encompassing upstream oil intake, downstream oil supply, and the tank capacities of transfer depots, thereby establishing a coherent link between pipeline scheduling and depot planning. This paper may serve as a reference for the automated formulation of multiproduct pipeline network scheduling strategies. • A novel schedule method for multiproduct pipeline network is proposed. • Propose dynamic index batch numbering method for precise batch tracking. • The effectiveness of the proposed method is validated through solid examples. [ABSTRACT FROM AUTHOR]

The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost scheduling, while the third layer, demand response which is a significant expansion of this domain. The implementation of the proposed architecture in MATLAB/Simulink, with test results. demonstrating the significance of the proposed solution

Motivated by the behavioral phenomena that occur while human operators are carrying out tasks, we study multitasking scheduling problems with a rate-modifying activity. In the problems, the processing of a selected task suffers from interruptions by other tasks that are available but unfinished, and the human operators regularly engage rest breaks during work shifts allowing them to recover or mitigate some of the negative effects of fatigue. The objectives are to respectively minimize: makespan, total completion time, maximum lateness, and due-date assignment related cost by determining when to schedule the rate modifying activity and the optimal task sequence in the presence of multitasking. Scheduling models and algorithms are proposed to solve the problems. The numerical examples are presented to illustrate the theorems and algorithms. [ABSTRACT FROM PUBLISHER]

The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized.

NASA’s Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its’ services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN’s limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm’s performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation.

Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.

Charging coordination is employed to efficiently serve electric vehicle (EV) charging requests without overloading the distribution network. Parameters such as parking duration, battery state-of-charge (SoC), and charging amount are provided by EVs to the charging coordination center to schedule their charging requests efficiently. The existing literature assumes that the customers always provide correct information. Unfortunately, customers may provide false information to gain higher charging priority. Assessing the impact of cheating behavior represents a significant and open problem. Herein paper, the impact of providing false information (e.g., parking duration) on the efficiency of the charging coordination mechanism is investigated. The charging coordination strategy is formulated as a linear optimization problem. Two different objectives are used to assess the impact of the objective function on the amount of performance degradation. Our investigations reveal that the degradation of the efficiency of the charging coordination mechanism depends on the percentage of cheating customers and cheating duration versus the typical parking duration. In addition, the impact of cheating behavior increases with the number of deployed chargers. Thus, the severity of the cheating impact will increase in the future as more fast chargers are allocated in charging networks.

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle. • Presents a solution framework to large-scale electric bus scheduling problem. • Introduces next day operability for continuous electric bus operations. • Bus range is a key determinant for diesel to electric bus replacement ratio. • 150-mi electric range leads to nearly 60% increase in fleet size. • Higher charging speed considerably reduces fleet size. [ABSTRACT FROM AUTHOR]

• The issue featured with concurrent job operations - mobile robots scheduling problems in complex FMS environment. • A logical and practical approach for the selection of GA parameters based on fuzzy rules to improve the solution to the complex combinatorial NP-hard scheduling problem. • Adaptively adjustment of genetic operator, i.e., crossover and mutation for better exploration and exploitation activities in searching process. Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality. [ABSTRACT FROM AUTHOR]

in recent years, large-scale electric vehicles connected to the power grid has a significant impact on demand-side resources. In addition, the vehicle-to-grid technology of electric vehicles introduces more uncertainties, which brings great challenges to the optimal scheduling of integrated energy systems. To solve this problem, this paper fully considers the uncertainty and fluctuation of wind power. Firstly, the objective function of joint configuration is constructed to maximize the utilization of wind power and minimize the planned generation cost of the system. Secondly, the linearized power flow equations are used to represent the relations among the state variables, and the second-order cone relaxation method is applied to deal with the branch power flow constraints, which is transformed into a solvable mixed-integer second-order cone programming model. Finally, the validity of the model is verified by an example. The system achieves obvious peak-shaving and valley-filling effect and improves the economic benefit of the system. • The load characteristics and energy storage characteristics of the electric vehicle fully absorb the wind energy. • The proposed second-order cone optimal dispatching strategy realizes the supplementary supply of grid and renewable energy. • The optimal dispatching strategy provides theoretical support for the efficient operation of grid. [ABSTRACT FROM AUTHOR]

The coordination among drones and ground vehicles for last-mile delivery has gained significant interest in recent years. In this paper, we study multiple drone delivery scheduling problem (MDSP) [1] for last-mile delivery, where we have a set of drones with an identical battery budget and a set of delivery locations; along with profit for delivery, cost, and delivery time intervals. The objective of the MDSP is to find conflict-free schedules for each drone such that the total profit gained is maximum subject to the battery constraint of the drones. This paper proposes an optimal pseudo-polynomial algorithm and a fully polynomial time approximation scheme (FPTAS) for the single drone delivery scheduling problem (SDSP). Also, we propose two approximation algorithms of factor 1 3 for the MDSP with some constraints on the number of drones. We formulate the problem for the heterogeneous case, where the drones have non-identical battery capacities, and propose a 1 6 ρ -approximation algorithm, where ρ is the ratio between the minimum and maximum battery capacity of the drones. • An optimal pseudo-polynomial algorithm and FPTAS for the single drone delivery scheduling problem is proposed. • Two approximation algorithms for the multiple drone delivery scheduling problem are proposed. • An approximation algorithm for the multiple heterogeneous drone delivery scheduling problem is proposed. • We analyzed the approximation factors of all the aforementioned proposed algorithms and time complexities. [ABSTRACT FROM AUTHOR]

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Optimal energy management trends are indispensable in improving the power grid’s reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model’s applicability to EM problems.

This work addresses integration of batch scheduling with water allocation, recycle and reuse opportunities for freshwater minimization in batch plants via sequential and simultaneous methodologies. The presented scheduling model is based on state task network representation and unit-specific event based continuous time formulation. In the production scheduling model, a three-index finish time variable has been considered for handling multiple states having different processing time durations for the same task in a processing unit. The scheduling model introduces constraints to handle storage violations for production and consumption of the same state in the same unit. In the water network model for freshwater minimization, a regeneration unit along with a central water storage tank has been included to exploit the possibility of water reuse in the washing units. Four case studies are solved with single and multiple contaminants to evaluate the performance of the proposed model, which gives better savings in terms of freshwater consumption and thus also minimizes the effluent generation. Additionally, a preliminary analysis for two-objective optimization is presented where revenue is maximized, and the total water cost is minimized simultaneously using the weighted-sum method.