The growing demands for affordable and applicable technologies for decentralized safe drinking water provision have instigated technical innovations in the water filtration industry. Adsorptive filtration appears to be the most affordable, resilient, and socially acceptable solution for households and small communities worldwide. However, water filtration devices have not yet been widely implemented due to lack of awareness for the efficiency of such systems using locally available materials. Water filtration has the potential to secure universal access to safe drinking water by 2030. This special issue has elucidated the applicability, benefits, constraints, effectiveness, and limitations of metallic iron as filter material for safe drinking water provision. Tools to make rainwater a primary water source are also presented together with ways to transform existing centralized water management systems into decentralized ones (sectorization). The knowledge is applicable to a wide variety... [more]

In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale mode... [more]

Dynamic lotsizing and scheduling on multiple lines to meet the customer due dates is significant in multi-line production environments. Therefore, this study investigates dynamic lotsizing and scheduling problems in multiple flexible machining lines considering mixed products. In addition, uncertainty in demand and machine failure is considered. A mathematical model is proposed for the considered problem with an aim to maximize the probability of completion of product models from different customer orders. A constructive heuristic method (CHLP) is proposed to solve the current problem. The proposed heuristic involves the steps to distribute different customer order demands among multiple lines and schedule them considering balancing of makespan between the lines. The performance of CHLP is measured with famous heuristics from the literature, based on the test problem instances. Results indicate that CHLP gives better results in terms of quality of results as compared to other famous li... [more]

The bullwhip effect results from inefficiencies in the supply chain; in perishable products, the inefficiencies are quality in the supply chain and product waste. We carried out a literature review to determine the causes of the bullwhip effect and the supply chain’s quality factors of this phenomenon’s perishable products. Update the demand, the level of deterioration of the product, and the number of intermediaries is the causes of the bullwhip effect most investigated. On the other hand, the product’s safety and the quality of the information are the quality factors of the chain of supplies of perishable products more researched. Future research should address the causes of human behavior that affect the bullwhip effect in the perishable goods supply chain.

Apparel manufacturing involves high water consumption and heavy water pollution in its supply chain, e.g., planting cotton, producing chemical fibers, and dyeing. This study employs a multi-regional input−output (MRIO) model to (1) assess the life cycle of blue and grey water (chemical oxygen demand (COD) specific) of China’s apparel manufacturing; (2) reveal the hidden linkage among sectors and regions in the whole supply chain; and (3) identify the key regions and upstream sectors with the most water consumption and heaviest water pollution. We found that the agricultural sector (i.e., planting fiber crops) is responsible for primary water consumption and water pollution. In addition, different provinces assume different production roles. Guangdong is a major output province in apparel manufacturing. However, its economic output is contributed to by other regions, such as blue water from Xinjiang and Jiangsu and grey water from Hebei and Shandong. Our research reveals the significanc... [more]

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

Compared with the ordinary merchant ship building, the concentralized distribution in cruise building is more complex. Plan change is a common phenomenon in cruise building, and it is easy to lead to mismatch between production and logistics, resulting in risks such as production schedule delay and inventory backlog. In order to reduce the adverse effects of plan change on the shipyard, it is necessary to conduct an in-depth study on the risks of a centralized distribution logistics plan. Based on the analysis of the composition of the centralized distribution logistics planning system, risk factors in different plan links are identified in this paper. A system dynamic model is constructed to simulate the propagation of five basic types of planning risk, including procurement plan, warehousing plan, pallet concentralization plan, distribution plan and production plan. In the case study of HVAC (heating, ventilation and air conditioning) materials, the values of risk factors are estimat... [more]

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In... [more]

This paper mainly presents a set of new Sapphire Backside Roughing technology. Presently, the associated Sapphire Backside Roughing technology is still concentrated on chemical etching, as its yield rate and efficiency are often limited by lattice structures, and the derived chemical waste fluid after etching is most likely to cause ecological contamination. In this research, refined abrasive jet processing technology is adopted, and in the meantime, the Taguchi experiment design method is taken for detailed experimental planning. Through processing parameter conditions and abrasive selection and development, proper surface roughing and processing uniformity are obtained so as to improve the various weak points of the abovementioned traditional etching effectively. It is discovered that abrasive blasting processing technology is, respectively, combined with wax-coated #1000 SiC particles and wax-coated #800 Zirconium particles to process the sapphire substrate with initial surface roug... [more]

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. The computational results showed that applying these schemes improves the relaxation tightness compared to only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It was also seen that there is a limit on the number of partitions that contribu... [more]

Orthognathic surgery and orthodontic treatment are required for patients with dentofacial deformities to obtain an ideal facial esthetic with good functioning. Recently, characterized by the surgery-first approach, an integrated orthodontic−surgical treatment has been introduced as an emerging solution to dentofacial deformity treatment. The surgery-first approach is regarded to have less treatment time and quicker enhancement of a facial profile than the conventional orthodontic−surgical treatment. Moreover, the recent advances in computing and imaging have allowed the adoption of 3-dimensional (3D) virtual planning protocols in orthognathic surgery as well as digital orthodontic treatment, which enables a paradigm shift when realizing virtual planning properly. These techniques then allow the surgeon and orthodontist to collaborate, plan, and simulate the dentofacial deformity treatment before performing the whole procedure. Along this line, in this research article, we present an in... [more]

Shipment consolidation is one of main initiatives to reduce CO2 emissions and transportation cost. It reduces the number of shipments per customer and reduces transportation costs by using larger shipments. This paper investigates the temporal consolidation process in a central consolidation center in a make-to-order supply chain. This research was motivated by a case study of a design furniture company that has many suppliers and customers in large parts of Europe. Simulation was used to check the effect of a new and a special time-based temporal consolidation on the response time in outbound logistics. A soft delivery deadline that is less than the average lead time was used because of the long lead time. Arena Software was used to model the supply chain in order to find the best circumstances to use consolidation. Results showed that temporal consolidation could be more effective when order preparation time is with larger variability. The useful waiting is more when there is at leas... [more]

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilizat... [more]

An adaptive control system for the set-point control and disturbance rejection of biotechnological-process parameters is presented. The gain scheduling of PID (PI) controller parameters is based on only controller input/output signals and does not require additional measurement of process variables for controller-parameter adaptation. Realization of the proposed system does not depend on the instrumentation-level of the bioreactor and is, therefore, attractive for practical application. A simple gain-scheduling algorithm is developed, using tendency models of the controlled process. Dissolved oxygen concentration was controlled using the developed control system. The biotechnological process was simulated in fed-batch operating mode, under extreme operating conditions (the oxygen uptake-rate’s rapidly and widely varying, feeding and aeration rate disturbances). In the simulation experiments, the gain-scheduled controller demonstrated robust behavior and outperformed the compared conven... [more]

The commission sales form is a very significant channel of sales today, which is especially true in the field of dietary supplements. In parallel, the prevailing digitalization trends have opened up further new opportunities for this form of distribution. The multi-level optimization process presented in the publication makes it possible to optimize the distribution logistics processes of companies producing food supplements at a high level by exploiting these new possibilities. The operation of the procedure is also illustrated through a practical example.

In digital transformation and development, supply chain integration has become a key strategy to improve supply chain synergy efficiency and enhance enterprise competitiveness. Based on the survey data of 185 manufacturing enterprises in Tianjin, the fuzzy set qualitative comparative analysis (fs QCA) method is used to explore the synergistic mechanism of government policy, supply chain partnership, information sharing, risk avoidance, and intelligence degree on supply chain integration and the interaction among them. The results show that: (1) a single factor does not constitute a necessary condition for promoting supply chain integration, but the formation and development of supply chain partnership plays a universal role in promoting supply chain integration; (2) the “multiple concurrent” of five factors constitute the diversified configuration of a driving supply chain integration path, that is, the driving supply chain integration path has the characteristic of “all roads lead to... [more]

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints,... [more]

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discr... [more]

The forecasting of univariate time series poses challenges in industrial applications if the seasonality varies. Typically, a non-varying seasonality of a time series is treated with a model based on Fourier theory or the aggregation of forecasts from multiple resolution levels. If the seasonality changes with time, various wavelet approaches for univariate forecasting are proposed with promising potential but without accessible software or a systematic evaluation of different wavelet models compared to state-of-the-art methods. In contrast, the advantage of the specific multiresolution forecasting proposed here is the convenience of a swiftly accessible implementation in R and Python combined with coefficient selection through evolutionary optimization which is evaluated in four different applications: scheduling of a call center, planning electricity demand, and predicting stocks and prices. The systematic benchmarking is based on out-of-sample forecasts resulting from multiple cross... [more]

Supply chains (SC) aim to provide products to the final customer at a certain service level. However, unforeseen events occur that impede supply chain objectives. SC Risk has been studied in the literature, providing frameworks and methodologies to manage SC failures. Nevertheless, more efforts are needed to prevent hazardous and disruptive risks and their consequences. These risks must be considered during the process of designing a supply chain. Some methodological contributions concerning risk in the supply chain network design (SCND) are conceptual frameworks for mitigating SC disruptions, which suggest strategies and measures for designing robust and resilient SCs. Although such contributions are valuable, they do not indicate how to cope with risk when designing a SC. The main objective of this research is to describe a methodology aimed at including risk considerations into the SCND. Our proposal aims to be, on the one hand, a comprehensive approach that includes a risk identifi... [more]

Range anxiety is a problem that restricts the development of pure electric vehicles. For this reason, much research starts from a shift schedule and strives to improve mileage. However, the proposed shift schedules have poor adaptive ability and are not suitable for dynamic conditions. In this paper, a shift schedule based on reinforcement learning (RL) is proposed, which uses Q-learning for optimization. However, the massive state variables and huge Q table in the state space put forward higher requirements on the computing power and storage space of the controller. Traditionally, the application of RL algorithms needs to rely on expensive GPU devices. To reduce high costs, we use an innovative treatment method, the optimal Latin hypercube design (Opt LHD), which is used for sampling, and state reduction is performed on the state space. Based on the above, the mileage is effectively improved by applying the shift schedule based on RL.

Solid oxide fuel cells are an emerging energy conversion technology suitable for high-temperature power generation with proper auxiliary heat. Combining SOFCs and polygeneration has produced practical applications for modern energy system designs. Even though many researchers have reviewed these systems’ technologies, opportunities and challenges, reviews regarding the optimal strategy for designing and operating the systems are limited. Polygeneration is more complicated than any other energy generation type due to its ability to generate many types of energy from various prime movers. Moreover, integration with other applications, such as vehicle charging and fueling stations, increases the complication in making the system optimally serve the loads. This study elaborates on the energy planning and guidelines for designing a polygeneration system, especially for residential applications. The review of polygeneration technologies also aligns with the current research trend of developi... [more]

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals w... [more]

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production co... [more]

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized... [more]