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In both manual and automated warehouses, a combination of efficient zoning and picker routing plays an important role in improving travel time, congestion, and system throughput. This paper considers the order picker routing problem in a dynamic and synchronised zoning environment, where the items corresponding to each customer order are picked simultaneously in multiple zones, and zones may change between different orders. The objective is to minimise the maximum time of completing the picking activities in any zone. Using a min–max type of objective not only minimises the makespan of an order picking wave, but it also helps balance the workload of the order pickers more effectively. We present a mathematical model for the optimal solution of this problem, as well as a dynamic programming approach to find the optimal solution for the case where a zone is a set of adjacent aisles. Computational experiments on randomly generated instances show that the dynamic programming approach is able to find optimal solutions in negligible computational times. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the Internet of Things and 5G networks in smart cities, the inevitable generation of massive amounts of data, commonly known as big data, has introduced increased latency within the traditional cloud computing paradigm. In response to this challenge, Mobile Edge Computing (MEC) has emerged as a viable solution, offloading a portion of mobile device workloads to nearby edge servers equipped with ample computational resources. Despite significant research in MEC systems, optimizing the placement of edge servers in smart cities to enhance network performance has received little attention. In this article, we propose RESP, a novel Recursive clustering technique for Edge Server Placement in MEC environments. RESP operates based on the median of each cluster determined by the number of base transceiver stations, strategically placing edge servers to achieve workload balance and minimize network traffic between them. Our proposed clustering approach substantially improves load balancing compared to existing methods and demonstrates superior performance in handling traffic dynamics. Through experimental evaluation with real-world data from Shanghai Telecom's base station dataset, our approach outperforms several representative techniques in terms of workload balancing and network traffic optimization. By addressing the ESP problem and introducing an advanced recursive clustering technique, this work makes a substantial contribution to optimizing mobile edge computing networks in smart cities. The proposed algorithm outperforms alternative methodologies, demonstrating a 10% average improvement in optimizing network traffic. Moreover, it achieves a 53% more suitable result in terms of computational load. [ABSTRACT FROM AUTHOR]

Owing to the rapid growth of the Internet of Things (IoT) and mobile devices, big data has been generated, and a cloud computing system is required to manipulate it. The main issue in this system is the access delay (AD) when the IoT and mobile devices offload their workload on the servers and the workload balance among them. Mobile edge computing (MEC), which has a sufficient distribution of edge servers (ESs) and resources, has been developed to overcome this problem. The placement of ESs in MEC is an NP-hard problem and a multi-objective optimization (MOO) problem that aims to reduce the AD between mobile users and ES and balance the workload among these servers. In this paper, we propose a new hybrid natural-inspired algorithm by combining the manta ray foraging algorithm and the uniform crossover operator to find the optimal edge server placement (ESP) in MEC and minimize the AD between mobile users and the ES. Although the standard manta ray foraging optimization (MRFO) algorithm can balance diversification and intensification, it suffers from premature convergence. We invoke a uniform crossover operator in the standard MRFO algorithm to overcome this problem. In addition, the standard MRFO algorithm is designed to solve continuous optimization problems and is unsuitable for solving discrete problems. However, ESP is a discrete optimization problem. We modified the operators in the standard MRFO algorithm to solve the ESP problem, achieving an optimal solution in terms of minimizing the AD between BSs and ESs and balancing the workload among ESs. The proposed algorithm is then tested on the Shanghai Telecom dataset and compared with five other algorithms. The results show that the proposed algorithm achieves the least AD, and the workload balance reaches about 52% compared with the other algorithms. [ABSTRACT FROM AUTHOR]

The manufacturing industry has been pursuing an efficient and economical assembly system. By considering assembly line balancing (ALB) and part feeding (PF) as an integrated problem and programming them simultaneously opens additional opportunities to improve the performance of the entire assembly system. However, the integrated ALB and PF problem is a non-deterministic polynomial (NP) hard problem. This implies that exact solutions cannot be obtained in a reasonable computation time and its near-optimal solutions can only be realised by meta-heuristics. In this study, we propose a novel bi-level multi-objective genetic algorithm (NBMGA) to solve the integrated ALB and PF problem. First, a bi-level mathematical model is established to simultaneously minimise the number of stations and workload smoothness of ALB in the upper level as well as the number of supermarkets of PF in the lower level. Second, the NBMGA with two modified strategies, including extending fitness evaluation and adaptive termination condition, is designed for problem solving. Finally, a series of computational experiments are conducted to demonstrate the efficacy of the proposed algorithm. The computational results indicate that the proposed algorithm outperforms the bi-level nondominated sorting genetic algorithm (NSGA) II in terms of the approximation to the true frontier without sacrificing computational efficiency. [ABSTRACT FROM AUTHOR]

An intensified competition forces warehouses to handle more orders in shorter time windows. This complicates the timely retrieval of these customer orders. Planning order picking operations, thereby aiming to increase efficiency, inevitably results in balancing concerns, such as imbalances among pick areas, pickers or time periods. Reducing workload imbalances, therefore anticipating on workload peaks, results in a more stable order picking process. However, there exist several measures that can be used to evaluate and correct existing imbalances. This study contributes to academic literature by analysing, explaining and evaluating the effectiveness of various workload balancing approaches (e.g. Rawlsian's approach, range, mean-based) in order picking operations, more specifically in the context of balancing workload over time in case of restricted time windows for retrieving customer orders. Results show that the effect of warehouse layout characteristics and customer order parameters on the effectiveness of balancing measures is very limited. However, the underlying managerial reason (e.g. workforce allocation, transportation schedule or human well-being) for solving the operational workload balancing problem does significantly impact the effectiveness and choice of an appropriate balancing measure. [ABSTRACT FROM AUTHOR]

Lean production has been industrialized in manufacturing industry in the Global North on account of delivering quality products. The industry in the Global South on the other hand is facing problems that could easily be minimized using the production principle. This paper is organized to adapt lean production for improving productivity of garment industry in Ethiopia. The target is to alleviate variety between company's productivity and customer demand. Shop floor visit, questionnaire survey, and focus group discussion-based data mining approaches have been used for this purpose. The qualitative part was used to map out baseline data of existing manufacturing process whereas the quantitative part was aimed to construct empirical findings obtained from qualitative organization. Aggregate results accordingly show that there are substantial lean wastes such as longer lead time, high WIP inventory, longer routes, and unutilized resources. It has been meanwhile renown that if appropriate lean production tools have framed and implemented, the impact of these wastes could meaningfully be minimized and a smooth process flow can be ensured. For instance, a lead time for a single round neck t-shirt in the case study has shown an improvement of 18.3% and a 3.9% improvement of resource utilization. [ABSTRACT FROM AUTHOR]

Assembly line balancing is an important and well recognised operations research problem. The current line balance may not stay optimal, even feasible, due to the disruptions in one or more workstations. In this study, after the disruption, we aim to rebalance the assembly line by considering the trade-off between workload balancing (fairness measure) and total replacement distance for the tasks assigned to the different workstations (stability measure). We try to generate all non-dominated objective function vectors for the defined fairness and stability measures. Two algorithms are developed: exact algorithm (classical approach) and tabu search algorithm. The results of the experiments have shown that the classical approach returns exact non-dominated objective vectors with up to 40 tasks and 7 workstations in one hour, and the tabu search algorithm returns approximate non-dominated objective vectors that are very close to their exact counterparts and can solve large sized instances with up to 94 tasks and 7 workstations in less than 10 s. [ABSTRACT FROM AUTHOR]

When designing a patrol route, it is often necessary to pay more attention to locations with high crime rates. In this paper, we study a patrol routing problem for a fleet of patrol cars patrolling a region with a high-crime neighborhood (HCN) consisting of multiple hot spots. Considering the disorder and chaos in the HCN, at least one patrol car is required in the HCN at any given time during the patrol. We call this routing problem the hot spot coverage patrol problem (HSCPP). In the HSCPP, the importance of a patrol location is quantified by a prize, and the prize is collected if a patrol car visits the location. Our objective is to maximize the sum of prizes collected by the patrol cars, obeying all operational requirements. We propose mathematical formulations and develop several solution approaches for the HSCPP. The global approach consists of finding the routing solution for all patrol cars with a single integer programming (IP) formulation. The partition approach involves first partitioning the region geographically and solving the routing problem in each subregion with two IP formulations. Next, we strengthen the partition approach by developing a column generation (CG) approach in which the initial columns of the CG approach are the solutions generated from the partition approach. We conduct a detailed computational case study using instances based on real crime data from Montgomery County, Maryland. To further understand the computational tractability of our solution approaches, we also perform a sensitivity analysis using synthetic instances under various scenarios. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0192) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0192). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This paper presents an equilibrium-based modeling framework for emergency response (ER) workload balancing for robust operations in metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal maximum workload for each ER station such that the weighted sum of the area-wide expected response time and its variation is minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station's workload increase on the robustness of the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively balanced among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service performance especially in situations with imbalanced workloads. [ABSTRACT FROM AUTHOR]

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times.

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times. [ABSTRACT FROM AUTHOR]

E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the "parts-to-picker" order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a "parts-to-picker" order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks' total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency. [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing is the specific application of Mobile Edge Computing in vehicular scenarios. In this work, to efficiently provide on-demand computational resource access for Mission Vehicles, Road Side Units (RSUs) and Cooperative Vehicles are both mounted with edge servers. To further improve the performance of this system, we firstly study the dynamic and cooperative task caching between RSUs. Under the control of the central controller, our caching scheme fully utilizes edge servers' storage resources. Then, we resort to Deep Deterministic Policy Gradient (DDPG) algorithm to adapt to the time-varying wireless environment. Moreover, we propose the DDPG-OCWB algorithm to improve the convergence performance of DDPG. Finally, numerical results verify the improved performance of our proposed algorithm. Based on a detailed comparison with existing research schemes, it is evident that our scheme dramatically reduces system delay while utilizing edge servers' energy efficiently. [ABSTRACT FROM AUTHOR]

Public institutions make expropriation to provide service access to individuals in the fulfillment of public services. Due to the wide range of service areas and needs, an average of 50000 cases is filed annually in our country regarding expropriation. The appointment of lawyers assigned to represent public institutions in lawsuits without calculating the files' workload causes an unbalanced workload distribution among lawyers. Although the activities that constitute the workload of lawyers seem to be participating in hearings and discoveries, preparation of the case file, preparation for hearings and discovery findings to courthouses on the relevant days, preparation of petitions during the trial examination of the files are also among the workloads. These workloads are not taken into account in file assignments and cause an unbalanced distribution of lawyers' workloads. In this study, the problem of assigning the case files related to expropriation procedures in a public institution to lawyers is addressed. In the institution, 724 case files of three different types are opened in 15 different regions within one month, and six lawyers work in the institution. A goal programming model has been proposed to balance lawyers' workloads by calculating the cases' workloads. Preparation, trial, discovery, file review, and travel times were taken into account to estimate the files' workload. The proposed model can also be used in other public institutions and for assigning different case files. [ABSTRACT FROM AUTHOR]

Generally, the computational grid consists of a large number of computing nodes, some of them are idle due to the uneven geographical distribution of computing requirements. This may cause workload unbalancing problems, which affect the performance of large-scale computational grids. In order to balance the computing requirements and computing nodes, we propose a job scheduling algorithm based on the workload prediction of computing nodes. We first analyze the causes of workload imbalance and the feasibility of reallocating computing resources. Secondly, we design an application and workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction model. To reduce the complexity of the AWAS algorithm, we propose a parallel job scheduling method based on computing node workload prediction. The experiments show that the AWAS algorithm can balance the workload among different computing nodes on the real-world dataset. In addition, we propose the parallelism of workload prediction model from the perspective of internal structure and data set to make AWAS apply to more computing nodes of the large-scale computing grids. Experimental results show that the combination of the two can achieve satisfactory acceleration efficiency. • In this paper, we propose an application and grid computing nodes workload aware scheduling algorithm (AWAS). • AWAS optimizes grid loads using greedy and heuristic methods. • This paper parallelizes the scheduling process of AWAS to improve its efficiency. • The experiment results show that the AWAS can achieve workload balancing in China National Grid. [ABSTRACT FROM AUTHOR]

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

With increasing components’ variety in mixed-model assembly lines, industrials show interest in kitting operations using robots. This paper deals with a hybrid kitting system that consists of a robot and an operator working in series to deliver parts to a Just-In-Time mixed-model assembly line. We develop a mathematical model that optimally assigns stock keeping units to either the robot or the operator so that the cycle time of the overall system is optimised. To test the model, a case study from the automotive sector is presented and a sensitivity analysis is carried out on relevant system parameters. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. Compared to SNNs with simple fully connected structures, those extensive structures (e.g., standard convolutions, depthwise convolutions, and pointwise convolutions) can deal with more complicated tasks but lead to difficulties in hardware mapping. In this work, we propose a novel reconfigurable architecture, Cerebron, which can fully exploit the spatiotemporal sparsity in SNNs with maximized data reuse and propose optimization techniques to improve the efficiency and flexibility of the hardware. To achieve flexibility, the reconfigurable compute engine is compatible with a variety of spiking layers and supports inter-computing-unit (CU) and intra-CU reconfiguration. The compute engine can exploit data reuse and guarantee parallel data access when processing different convolutions to achieve memory efficiency. A two-step data sparsity exploitation method is introduced to leverage the sparsity of discrete spikes and reduce the computation time. Besides, an online channelwise workload scheduling strategy is designed to reduce the latency further. Cerebron is verified on image segmentation and classification tasks using a variety of state-of-the-art spiking network structures. Experimental results show that Cerebron has achieved at least $17.5\times $ prediction energy reduction and $20\times $ speedup compared with state-of-the-art field-programmable gate array (FPGA)-based accelerators. [ABSTRACT FROM AUTHOR]

Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In container terminals (CTs), the performance of quay cranes (QCs) is extremely impacted by their operators' productivity, which makes the QC worker assignment a relevant element in the QC scheduling. Nevertheless, in the literature, this problem was tackled without considering the objectives of balancing operators' workload and minimising the distance required to move between QCs, even though practitioners underline their importance in maintaining an efficient working environment. Accordingly, and based on a real-case study raised by a CT partner, this paper addresses a novel problem referred to as the travelling QC worker assignment and balancing problem. First, we propose a novel mathematical model with original constraints faced by the company to assign operators to QCs on a daily basis. Second, to find approximate solutions in a reasonable time, a constructive heuristic and an iterative local search (ILS) are proposed and tested on real datasets. We also propose a simulation-optimisation mechanism to evaluate the robustness of the solutions under real-time probabilistic perturbations. The proposed algorithms are integrated into a decision support tool, a network-linked application allowing users in a real CT to generate automatic assignments. The proposed simulation-optimisation procedure helps obtain balanced planning, avoid workload conflicts, and increase productivity. [ABSTRACT FROM AUTHOR]

In order to differentiate from competitors in terms of customer service, warehouses accept late orderswhile providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers' productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method. [ABSTRACT FROM AUTHOR]

Efficient routing and scheduling are critical for providing patients with quality health care in their homes, and palliative care involves specific requirements. This paper addresses the Palliative Home Health Care Routing and Scheduling Problem (PHHCRSP) through an integrated mathematical model. Key considerations include caregiver skills, care continuity, and a multi-period planning horizon. Special attention is also given to the case of clustered patients. Five formulations with different objective functions are analyzed and solutions reveal that optimizing only for minimum or maximum workload leads to non-efficient routing. The formulation that balances the total workload and the minimum workload emerges as the most effective, as it manages to obtain good routes while distributing the workload evenly among the caregivers. The results of the computational study on a set of well-known benchmark instances show that it is possible to obtain good patient care while keeping good working conditions for the caregivers. • We address the Palliative Home Health Care Routing and Scheduling Problem (PHHCRSP). • We develop and analyze five formulations with alternative objective functions. • We present valid constraints that improve the problem formulations. • The most effective formulation balances both the total and the minimum workload. • An extensive computational study is conducted on well-known benchmark instances. [ABSTRACT FROM AUTHOR]

Summary: We contribute to the optimization of the sparse matrix‐vector product by introducing a variant of the coordinate sparse matrix format that balances the workload distribution and compresses both the indexing arrays and the numerical information. Our approach is multi‐platform, in the sense that the realizations for (general‐purpose) multicore processors as well as graphics accelerators (GPUs) are built upon common principles, but differ in the implementation details, which are adapted to avoid thread divergence in the GPU case or maximize compression element‐wise (i.e., for each matrix entry) for multicore architectures. Our evaluation on the two last generations of NVIDIA GPUs as well as Intel and AMD processors demonstrate the benefits of the new kernels when compared with the optimized implementations of the sparse matrix‐vector product in NVIDIA's cuSPARSE and Intel's MKL, respectively. [ABSTRACT FROM AUTHOR]

In recent electronic industry, the assembly of electronic components on Printed Circuit Boards (PCB) is one of the most crucial tasks. Here, gantry type placement machines are popular because of the flexibility of their configurations. The Machine Configuration and (Work) Load Balancing (MCLB) problem determines the proper configurations of the machine modules in the assembly line and the allocation of the component placements among the modules minimizing the total production time. This is a difficult optimization problem, especially when the assembly line produces several batches of multiple PCB types using a common machine setup for all PCB types. In the present study, a graph model and a mathematical formulation are given for the multi-model problem (MCLB-M). A heuristic method is also presented, called Divide and Combine (DaC), which divides the multi-model problem into single product problems and solves them independently. The solutions are then combined to form a common sub-configuration of the modules. Finally, the global problem is solved by reducing its size using the fixed sub-configuration. The presented method is independent of the optimization algorithms used for the sub-problems; an integer programming model and a heuristic algorithm are used for the single problems, and an integer programming for the reduced global problem. The tests showed that the DaC method is robust and works well for problems of different size. [ABSTRACT FROM AUTHOR]

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature‐engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved. [ABSTRACT FROM AUTHOR]

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multisource IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising. [ABSTRACT FROM AUTHOR]

E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a “parts-to-picker” order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks’ total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency.

Increased customization has strengthened the importance of make-to-order companies. The advent of lean management and the introduction of smart and flexible technologies has enabled many of these companies to create flow shop routings. Order review and release (ORR) research, which originally focused on job shops, has started paying attention to flow shops. However, the results have not provided clarity on the best ORR method for flow shops. This study aims at developing such a method by applying a modular design approach. It identifies the relevant elements of ORR methods for flow shops, combines them into new methods and evaluates them in a simulation study. The simulation results demonstrate that performance in pure flow shops can be strongly improved by applying the right combination of workload measures, load balancing, and order dispatching. Specifically, the results show that (1) classical workload measures are still as effective as novel measures that have been suggested for flow shops, (2) balancing workloads explicitly through optimization at the order release stage strongly improves performance, and (3) shortest processing time dispatching is highly effective in flow shops as it avoids starvation of stations. In-depth analyses have been executed to unravel the reasons of performance improvements. As such, the article provides clarity on the improvement potential that is available for ORR in flow shops, while the new modular methods provide a first step in exploiting this potential. [ABSTRACT FROM AUTHOR]

In a steel industry, a slab yard plays a role of a buffer between continuous casting stage and rolling mill. An effective assignment and scheduling of cranes can guarantee the operation efficiency in the slab yard. This work studies a multicrane scheduling problem with noncrossing constraints of slabs. A mixed-integer programming model is used to formulate the problem that minimizes the whole traveling distance of all the cranes and ensures the workload balance among cranes. As it is an NP-hard problem, classical programming mathematical methods are difficult to get an optimal solution for large-size instances. Thus, we develop a branch and price algorithm to solve this problem. First, we formulate the model as a generalized set covering problem and a set partition problem. Then, we solve them and combine the solutions to obtain the solution of the original problem. Finally, we conduct computational experiments based on real data from an iron–steel plant. The comparisons of proposed methods with an exact solution method show its effectiveness. Note to Practitioners—This work deals with a multicrane scheduling problem. Aiming to minimize the total traveling distance of all the cranes, it establishes a mixed-integer programming model with a workload balance constraint on cranes. It presents a branch and price algorithm to solve the problem whose solution complexity grows exponentially with problem size. The integration of crane assignment and scheduling enables the better utilization of cranes and faster service in iron–steel enterprises and, hence, improving customer satisfaction. The experimental results reveal the effectiveness of the proposed approach. It can readily be put into use in the steel industry. [ABSTRACT FROM AUTHOR]

Today, business process management systems (BPMS) are rapidly expanding. Organizations and corporations need to leverage BPMS to manage their business processes and to increase economic productivity for competing in the international market. Therefore, researchers are to increase the features of BPMS. Workload balancing in BPMS is one of the challenges which have been studied by researchers. The purposes of workload balancing include increasing system stability, improving the resource efficiency and enhancing the quality of its products. In this paper, undesirable effects of the time delay of arrival rate in the tasks are presented. To overcome the undesirable effects, we define the workload pattern of resources and we propose a heuristic method to manage and regulate the processes in the BPMS as Process Concurrency. we leverage PSO algorithm to optimize process concurrency. Therefore, in addition to balancing the workload of resources, workload for each resource is maintained uniform through time. Creating uniformity in workload, as a result, increase the resource efficiency and improve the quality of products and services. To evaluate the proposed method, experimental results showed the optimal process Concurrency 29.8% increase balance and uniformity in the resource workload.

We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems. Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution. The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task. The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration. The two mechanisms are instantiated via a new malleable thread-level implementation of the basic linear algebra subprograms, and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead. Concretely, our experimental results on an Intel-Xeon system with 12 cores show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.

This paper considers the problem of assigning nonpreemptive jobs on identical parallel machines to optimize workload balancing criteria. Since workload balancing is an important practical issue for services and production systems to ensure an efficient use of resources, different measures of performance have been considered in the scheduling literature to characterize this problem: maximum completion time, difference between maximum and minimum completion times and the Normalized Sum of Square for Workload Deviations. In this study, we propose a theoretical and computational analysis of these criteria. First, we prove that these criteria are equivalent in the case of identical jobs and in some particular cases. Then, we study the general version of the problem using jobs requiring different processing times and establish the theoretical relationship between the aforementioned criteria. Based on these theoretical developments, we propose new mathematical formulations to provide optimal solutions to some unsolved instances in order to enhance the latest benchmark presented in the literature. [ABSTRACT FROM AUTHOR]

Hazardous wastes' volume produced by human activities has increased in recent years. Consequently, associated risks involved in the treatment, recycling, disposing, and transportation of these hazardous materials have become more attractive for the researchers. In this study, we propose a new model for hazardous waste location routing problem. Appending the service time window and workload balance to the previous mathematical models can be taken into account as the major contributions of this study. Three objective functions including two systematic goals (cost and risk) and one social goal (workload balancing) have been considered for the model. Compatibility between wastes and a heterogeneous fleet of vehicles, which are rarely investigated in the literature, is discussed in this paper. Since the proposed model is classified as a multi-objective model, three multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II), and Strength Pareto Evolutionary Algorithm II (SPEA-II) are employed. As two other innovations, an adaptive penalty function is developed and the PESA-II is modified by removing replicated solutions from its archive and their obtained results are discussed. Finally, by experimenting a number of test problems in different sizes, it is demonstrated that proposed modified PESA-II and SPEA-II perform better than NSGA-II in most of comparison metrics including feasible answers exploration, CPU time, spacing metric, inverted generational distance, quality metric, etc., whereas, NSGA-II creates more spread Pareto frontiers which are suitable for decision-maker to choose, from among a range of different options. [ABSTRACT FROM AUTHOR]

The multigroup neutron transport equation is crucial for studying the motion of neutrons and their interaction with materials. Numerical simulation of the multigroup neutron transport equation is computationally challenging because the equation is defined on a high-dimensional phase space, the computational spatial domain is complex, and the materials are heterogeneous. A scalable parallel solver is required to address such a challenge. In this paper, we study a highly parallel Newton-Krylov-Schwarz (NKS) method consisting of a Newton-based eigenvalue solver, a Krylov subspace method, and a novel multilevel Schwarz preconditioner. The multilevel method is one of the most popular preconditioners for accelerating neutron transport calculations, but the construction of coarse spaces can be expensive and often unscalable when a large number of processors is used. We propose a novel matrix coarsening algorithm in which a multilevel hierarchy is constructed using a single-component matrix instead of the full matrix of the neutron transport equation. This new coarsening algorithm is referred to as "subspace-based coarsening." Above 8,000 processors, we show a 13x enhancement in multilevel preconditioner setup time when using the subspace-based coarsening method. A partition-based balancing strategy is studied to enhance the parallel efficiency of the NKS algorithm by equalizing the work for each processor. A hierarchical mesh partitioning algorithm is employed to generate a large number of submeshes while minimizing off-node communication. We demonstrate that the proposed algorithm is scalable with more than 10,000 processors for a realistic application on three-dimensional unstructured meshes with a few billion degrees of freedom. Neutron transport calculations using the improved NKS algorithm are twice as fast as those based on the unmodified NKS solver when over 8,000 processors are employed. [ABSTRACT FROM AUTHOR]