In recent years, the permutation flowshop scheduling problem (PFSP) with sequence-dependent setup times has been widely investigated in the literature, most focusing on the single-objective optimization problem. However, in a practical production environment, schedulers usually need to handle several conflicting objectives simultaneously, which makes the multiobjective PFSP with sequence-dependent setup times (MOPFSP-SDST) more difficult and time consuming to be solved. Therefore, this paper proposes a learning and swarm based multiobjective variable neighborhood search (LS-MOVNS) for this problem to minimize makespan and total flowtime. The main characteristic of the proposed LS-MOVNS is that it can achieve the balance between exploration and exploitation by integrating swarm-based search with VNS in the multiobjective environment through machine learning technique. For example, the learning-based selection of solutions for multiobjective local search and the adaptive determination of... [more]

The present study focuses on supply chain management to improve its sustainability from economic, environmental, and social perspectives. First, improving production process reliability and cost reduction are two main factors for enhancing economic sustainability. Hence, we introduced autonomated inspection and invested in ordering and setup costs. Second, reducing the carbon footprint in supply chains is the main pillar of their environmental stewardship, which is addressed by an eco-friendly and flexible production system in this study. Finally, an advanced single-setup-multi-delivery (SSMD) strategy is utilized to improve social aspects associated with human labor increase. For practicality, demand is considered as the selling price and is quality dependent. The sustainability enhancement is transformed as a term of profit; therefore, our model maximizes the total profit of the supply chain by optimizing a manufacturer’s and retailer’s decision variables. Numerical examples show tha... [more]

The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge computing environment, and a new fitness calculation method for evaluating the energy consumption of edge devices is designed. Combined with the workflow management system, a multi-resource computing offloading particle swarm optimization task scheduling algorithm for energy consumption optimization in mobile edge computing is proposed. The algorithm can fully reduce the energy consumption of mobile t... [more]

In order to promote the wind monitoring accuracy and provide a quantitative planning method for met mast layout in practical projects, this paper proposes a two-stage layout method for met mast based on discrete particle swarm optimization (DPSO) zoning and micro quantitative siting. Firstly, according to the wind turbines layout, rotational empirical orthogonal function and hierarchical clustering methods are used to preliminarily determine zoning number. Considering the geographical proximity of wind turbines and the correlation of wind speed, an optimal macro zoning model of wind farm based on improved DPSO is established. Then, combined with the grid screening method and optimal layout evaluation index, a micro quantitative siting method of met mast is proposed. Finally, the rationality and efficiency of macro zoning method based on improved DPSO, as well as the objectivity and standardization of micro quantitative siting, are verified by an actual wind farm.

Supply chain management (SCM), which generally refers to horizontal integration management, has steadily become the core competitiveness of company rivalry and an essential approach to developing national comprehensive and national strength since the end of the 20th century due to the numerous needs arising from a competitive international economy. Manufacturers develop a community of interest by forming long-term strategic partnerships with suppliers and vendors throughout the supply chain. This paper defines supply chain management by reviewing the existing literature and discusses the current state of supply chain management research, as well as prospective research directions. Specifically, we conducted a bibliometric analysis of the influential studies of SCM in terms of various aspects, such as research areas, journals, countries/regions, institutions, authors and corresponding authors, most cited publications, and author keywords, based on the 8998 reviews and articles collected... [more]

Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering both kinematic and dynamic constraints. Firstly, we offer a mathematical description of the energy consumption and space curve similarity operator. Secondly, we describe the mathematical models of the robot trajectory and path that were established in terms of their dynamics and kinematics. Then, we propose a motion planning method based on improved adaptive particle swarm optimization (PSO) to accelerate the convergence speed of the algorithm and ensure the accuracy of the model calculation. Finally, we outline the simulation test carried out to measure the inchworm-like robot’s creeping gait. The results show that the motion path obtained by using the planning method proposed in th... [more]

The transformation to Supply Chain (SC) 4.0 promises new opportunities for companies to gain competitiveness. The Lean Value Stream Mapping (VSM) tool allows the supervision of all the processes of the entire SC, from which we can identify the different types of waste that hinder the competitiveness of the SC. Following the existing problems detected with the help of a diagnostic, we will propose a new process design by integrating 4.0 technologies to modernize the company. For our case study, we treat the multinational SC of Automotive Wiring Equipment Morocco, where we will focus on the downstream part of the SC composed of the warehouse and the different stages of road and sea transport until the final delivery in Austria. Then, we will consider the opportunities offered by each country in terms of logistics competitiveness. In this research work, we will show how Lean VSM4.0 will contribute to sustainable development by integrating the three pillars economic, environmental, and soc... [more]

While COVID-19 vaccines are generally available, not all people receive vaccines. To reach herd immunity, most of a population must be vaccinated. It is, thus, important to identify factors influencing people’s vaccination preferences, as knowledge of these preferences allows for governments and health programs to increase their vaccine coverage more effectively. Fortunately, vaccination data were collected by U.S. Census Bureau in partnership with the CDC via the Household Pulse Survey (HPS) for Americans. This study presents the first analysis of the 24 vaccination datasets collected by the HPS from January 2021 to May 2022 for 250 million respondents of different ages, genders, sexual orientations, races, education statuses, marital statuses, household sizes, household income levels, and resources used for spending needs, and with different reasons for not receiving or planning to receive a vaccine. Statistical analysis techniques, including an analysis of variance (ANOVA), Tukey mu... [more]

In process optimization, a process is adjusted so as to optimize a set of parameters while meeting constraints, with the objective to either minimize the total processing time or maximize the throughput. This article focused on the process optimization of a fully automated yogurt and flavor-filling machine developed based on the industrial revolution 4.0 concept. Mathematical models were developed for minimizing the total processing time or maximizing the throughput of an Industry 4.0-based yogurt filling system with two different machine settings called Case-I and Case-II. In Case-I, the yogurt and flavors are filled at two distinct points while Case-II considers the filling of yogurt and flavors at a single point. The models were tested with real data and the results revealed that Case-II is faster than Case-I in processing a set of customer orders. The results were used as inputs for the single-dimension rules to check which one results in more intended outputs. Additionally, differ... [more]

This study undertakes the line balancing problem while allocating reconfigurable machines to different workstations. A multi-objective model is used to analyze the position of workstations, assignment of configurations to workstations, and operation scheduling in a reconfigurable manufacturing environment. A model is presented that comprises the objectives of the Total Time (TT), the Line Efficiency Index (LEI), and the Customer Satisfaction Index (CSI). The objective is to minimize the completion time and maximize the efficiency of a production line. The proposed model combines the Simple Line Balancing Problems Type 2 and Type E in the form of SLBP-2E. The presented problems are addressed by using a heuristic solution approach due to non-polynomial hard formulation. The heuristic approach is designed to assess different solutions based on no repositioning, separate repositioning of workstations and configuration, and simultaneous repositioning of workstations and configurations. A de... [more]

To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created and... [more]

Considering the uniformity of cable tension variation, in this paper, the trajectory planning problem of the exit point for a continuously reconfigurable four-cable-driven two-degrees-of-freedom (DOF) parallel mechanism was studied. Furthermore, an improved quadratic programming model-based trajectory planning method is proposed, which greatly reduces the change in cable tension and can be used to solve the problem of excessive cable tension change when the existing mechanism moves on the moving platform. First, the structural characteristics of the parallel mechanism with a fixed exit point were analyzed, and the static model was established. Considering the cable length and tension constraints, the feasible workspace of the mechanism force was solved. Then, based on the dynamic modeling, an improved quadratic programming model was used to solve the cable tension values under the typical trajectory in the force-feasible workspace. Finally, considering the influence of structural param... [more]

Most studies on distributed flexible job shop scheduling problem (DFJSP) assume that both processing time and transmission time are crisp values. However, due to the complexity of the factory processing environment, the processing information is uncertain. Therefore, we consider the uncertainty of processing environment, and for the first time propose a multiobjective distributed fuzzy flexible job shop scheduling problem with transfer (MO-DFFJSPT). To solve the MO-DFFJSPT, a hybrid decomposition variable neighborhood memetic algorithm (HDVMA) is proposed with the objectives of minimizing the makespan, maximum factory load, and total workload. In the proposed HDVMA, the well-designed encoding/decoding method and four initialization rules are used to generate the initial population, and several effective evolutionary operators are designed to update populations. Additionally, a weight vector is introduced to design high quality individual selection rules and acceptance criteria. Then, t... [more]

Oil and gas (O&G) supply chain management (SCM) is complex because it deals with different geographic locations to manage demand and supply, transportation, inventory, and distribution. Blockchain technology has created an interesting research gap in the SCM domain, and this study is designed to describe the relevancy of blockchain technology for O&G SCM. SCM is based on agile and lean supply chains (SCs). Agile SC focuses on increasing flexibility and responsiveness to gain competitive advantages, and lean SC is based on eliminating waste and processes to improve firm performance. This study is an initial effort to propose a framework that suggests the implication of blockchain for O&G by providing an overview of O&G SCM. Data were collected from SC managers of O&G companies, and we analyzed the impact of agile and lean SCs on firm performance. The results indicate that agile SC is highly important for O&G industries in comparison to lean SC. This study proposes the key requirements o... [more]

In the literature, several approaches have been proposed to integrate and optimize product supply and construction processes associated with demand management. However, in Industry 4.0, there needs to be more studies related to applying techniques that directly affect the programming and reprogramming process that integrates the industries at the operational level. This document proposes a flow-shop scheduling procedure to address the problem of planning the repair of medical equipment in public hospitals whose main objective is to eliminate downtime and minimize total production time. The research stems from the practical problem of responding to clinical users who make use of critical equipment, such as mechanical respirators, due to COVID-19, and the limited quantity of this equipment, which makes it necessary to have repair planning processes that seek to keep the equipment in operation for the most extended amount of time. The novelty of this study is that it was applied to a crit... [more]

It is well known that with the development of economic globalization and increasing competition in the market, enterprises are facing a huge challenge in the unrelated parallel machine scheduling problem with setup time (UPMST). Determining the processing order of all jobs and assigning machines to production scheduling has become more complex and has research implications. Moreover, a reasonable production scheduling scheme can not only complete the production plan efficiently but also contribute to reducing carbon emissions. In this paper, a mathematical model with the goal of the shortest completion time is studied for the UPMST problem. An improved line-up competition algorithm (ILCA) is proposed to solve this model, and the search accuracy and rate of the algorithm are improved by the newly proposed heuristic workpiece allocation rules and variation strategies. From the perspective of evaluation purposes, the effectiveness and stability of the method are significantly superior to... [more]

The original schedule may not be optimal or feasible due to delivery delay caused by disruption. To solve the vehicle rescheduling problem with delivery delay based on loss aversion in prospect theory and customer heterogeneity, a mathematical model is established to minimize the sum of distance cost and penalty cost. Next, an improved compressed annealing algorithm with heterogeneous pressure is proposed to solve the model. Finally, numerical experiments are executed on the basis of 30 classic Solomon benchmarks to test the performance of the proposed solution approach. Sensitivity tests are carried out for the customer waiting sensitivity parameter, the length of delay time, and the time when the delivery delay occurs. The computational results show that, compared to the traditional rescheduling method, the higher the degree of customer heterogeneity, the longer the length of delay time, and, the earlier the distribution delay occurs, the stronger the validity and practicability of t... [more]

The rapid development of science and technology, and the restructuring of the mining extraction industry, bring about profound changes in the structure and complexity of technological equipment used in mining. In this paper, the Reliability Centered Maintenance (RCM) method has been applied to analyze the components of the KSW-460NE shearer machine, which fails quite frequently. The cutter drums do not match from a constructive point of view, and the concrete operation conditions, alongside the picks (being in direct contact with coal and hard inclusions) and guides are submitted to intense abrasion wear, showing a great number of failures. The data collected following the machine’s exploitation allowed parameter determination characterizing the reliability of the components mentioned, the manner of failure, and the effects. Using calculation methods, it has been possible to facilitate the interpretation of the result in view of establishing measures required to improve maintenance of... [more]

The last-mile logistics is regarded as one of the least efficient, most expensive, and polluting part of the entire supply chain and has a significant impact and consequences on sustainable delivery operations. The leading business model in e-commerce called Attended Home Delivery is the most expensive and demanding when a short delivery window is mutually agreed upon with the customer, decreasing possible optimizing flexibility. On the other hand, last-mile logistics is changing as decisions should be made in real time. This paper is focused on the proposed solution of sustainability opportunities in Attended Home Delivery, where we use a new approach to achieve more sustainable deliveries with machine learning forecasts based on real-time data, different dynamic route planning algorithms, tracking logistics events, fleet capacities and other relevant data. The developed model proposes to influence customers to choose a more sustainable delivery time window with important sustainabili... [more]

The modeling of the paint atomization process is a barrier in computational fluid dynamics numerical simulation for the whole process of air spraying, and seriously restricts robot intelligent spray gun trajectory planning and the improvement of coating quality. Consequently, a multi-scale paint atomization model based on the hybrid Euler−Lagrange method was established in this paper, which included a large liquid micelle motion model, a particle motion model, and a turbulence flow model. The Euler method was adopted to capture the gas−liquid interface in the atomization flow field to describe the deformation and motion of large liquid micelles. The identification and transformation mechanisms of large liquid micelles and small particles were constructed by the particle motion model, and the motion of small droplets generated by paint atomization was tracked by the Lagrange method. The turbulence motion of the fluid in the process of paint atomization was described by a two-equation tu... [more]

In order to describe the impact that the appropriation of additive manufacturing (AM) has on the supply chain (SC), a validated system dynamics model representing vectorially multiple products and multiple demands in different periods was used as a basis to apply to a case study of medical implant manufacturing, configuring three chain scenarios: 1. traditional supply chain with subtractive manufacturing, 2. centralized supply chain with additive manufacturing, and 3. decentralized supply chain with additive manufacturing. It was possible to notice that the production time is longer in additive manufacturing compared to traditional manufacturing and the cycle time and total demand closure were lower in traditional manufacturing. In addition, it was observed that the AM performance is significantly better in conditions of lower demand, which can be attributed to the characteristics of customization and small batches that this type of production approach implies.

To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum completion time and reduce the comprehensive impact of resources and the environment, and an improved moth-flame optimization algorithm was developed. A coding scheme based on the number of reentry layers, stations, and machines was designed, and a hybrid population initialization strategy was developed, according to a situation wherein the same types of nonequivalent parallel machines were used. Two different update strategies were designed for updating the coding methods of processes and machines. The population evolution strategy was adopted to improve the local search ability of the proposed algorithm and the quality of the solution. Through simulation experiments based on different datasets, the effectiveness of the proposed algorithm wa... [more]

In order to realize the recyclability of innovative product resources, we explored the optimal dynamic path of each decision variable in the remanufacturing supply chain and analyzed the impact of each decision variable on supply chain performance. Based on the Bass innovation diffusion model, we established a remanufacturing supply chain model in which a single manufacturer leads and a single retailer follows, and the retailer is responsible for recycling. The optimal wholesale price, retail price, and recovery effort path were obtained through optimal control theory. We also discussed the influence of different innovation coefficients and imitation coefficients on the overall long-term profit of each member in the supply chain, and at the same time, found the optimal market share of the product. The research results show that the larger the market innovation coefficient and the imitation coefficient are, the larger the overall long-term profit of the manufacturer and the greater the... [more]

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees we... [more]

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems of jobs and machines in the work cells, and of jobs between work cells, based on the process plan flexibility. Second, in order to make real-time scheduling decisions, a framework of multi-task multi-agent reinforcement learning based on centralized training and decentralized execution is proposed. Each agent interacts with the environment and completes three decision-making tasks: job sequencing, machine selection, and process planning. In the process of centralized training, the value network is used to evaluate and optimize the policy network to achieve multi-agent cooperation, and the attention mechanism is introduced into the policy network to realize information sharing among multiple tasks.... [more]