Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on bat... [more]

Today’s global business trends are causing a significant and complex data revolution in the healthcare industry, culminating in the use of artificial intelligence and predictive modeling to improve health outcomes and performance. The dataset, which was referred to is based on consumption data from 2015 to 2019, included approximately 500 goods. Based on a series of data pre-processing activities, the top ten (10) essential medicines most used were chosen, namely cotrimoxazole 480 mg, amoxicillin 250 mg, paracetamol 500 mg, oral rehydration salts (O.R.S) sachet 20.5 g, chlorpheniramine 4 mg, nevirapine 200 mg, aminophylline 100 mg, artemether 20 mg + lumefantrine (AL) 120 mg, Cromoglycate ophthalmic. Our study concentrated on the application of machine learning (ML) to forecast future trends in the demand for essential drugs in Rwanda. The following models were created and applied: linear regression, artificial neural network, and random forest. The random forest was able to predict 10... [more]

There have been many literature reviews carried out on Lean implementation in larger organisations with specific focus on the automobile industry. Lean implementation in the medical device industry has not been extensively investigated. Thus, this research endeavored to analyse the benefits of Lean, tools utilised and challenges and results of Lean implementation in Medtech companies. This article aims to bridge a gap in the literature by reviewing the literature that discusses Lean implementation in MedTech companies in Ireland with a perspective of identifying the benefits and challenges faced. The quantitative methodology allows us to review the comprehensive numbers and data which were collected from 20 Enterprise Ireland MedTech case studies. There are very few published case studies in the literature on Lean due to the highly regulated nature of MedTech sector and the vast array of medical devices, which are often under privacy and confidentiality constraints. The results showed... [more]

This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would interfere with operation, due to the occurrence of unspecified failure to a critical component. Over time, depreciation and degradation of components in a machine are unavoidable, as is shown in the present investigation through intelligent assessment of the lifespan of components. So far, no effective methodology has been implemented in a real-time maintenance environment. RLAs seem to be a more effec... [more]

The cloud energy storage system (CES) is a shared distributed energy storage resource. The random disordered charging and discharging of large-scale distributed energy storage equipment has a great impact on the power grid. This paper solves two problems. On one hand, to present detailed plans for designing an orderly controlled CES system in a realistic power system. On the other hand, Monte Carlo simulation (MCS) is used for analyzing the load curves of five types of distributed energy storage systems to manage and operate the CES system. A method of its planning and the principles of CES for applied in a power grid, are presented by analyzing the impact based on five load curves including the electric vehicle (EV), the ice storage system, the demand response, the heat storage system, and the decentralized electrochemical energy storage system. The MCS simulates the random charging and discharging of the system over a five-year planned scaling of distributed energy storage from 2021... [more]

The data based on location/activity sensing technology is exploding and integrating multi-source data provides us with a new perspective to observe tourist behavior. On the one hand, tourist preferences can be extracted from the attractions generated by clustering. On the other hand, potentially extracted tourist information can provide decision-making support for tourism management departments in tourism planning and resource development. Therefore, developing smart tourism services for tourists and promoting the realization of “smart scenic spots.” A field survey was conducted in Zhongshan Botanical Garden, China, from 3 February to 3 April 2019. This empirical study combines a handheld GPS tracking device and questionnaire survey using SEE to optimize k-means clustering algorithm and explores the spatial−temporal behavior patterns of tourists. The results showed that tourists in the botanical garden could be divided into three behavioral patterns. They are recreation and leisure, bi... [more]

Formulating a reasonable and feasible unit maintenance scheme is a promising way to eliminate potential risks and improve the reliability of power systems. However, the uncertainty and volatility of new energy outputs, such as wind power, increase the difficulty of scheme formulation. To overcome the complexity of uncertainty, a robust unit maintenance scheme considering the uncertainty of new energy output and electrolytic aluminum load is established in this paper. Considering the significant time-series characteristics of new energy, this paper first introduces the definition and mathematical model of information granulation (IG), through which the initial new energy output data can be transformed into fuzzy particles used for prediction and analysis. Moreover, a support-vector machine (SVM) regression prediction model is adopted, and a corresponding progressive search algorithm is designed to determine SVM parameters efficiently. Then, a robust unit maintenance model is established... [more]

With the extensive development of remanufacturing, remanufacturing trade, as an essential part of it, has also attracted much attention from researchers. Quite a large number of studies related to remanufacturing trade, such as pricing, sales, competition, channel expansion and service strategy, have been published in various journals. This paper aims to focus on the research status on remanufacturing trade through bibliometric analysis that can provide the primary research trends and the future research hotspots by analyzing the progress, parties and themes of the research. In this paper, the review and analysis are conducted on over 121 articles from 2000 until July 2021 with the help of VOS viewer (Leiden University, Leiden, The Netherlands) and Citespace (Drexel University, Philadelphia, PA, USA). The results of the analysis of research progress and research parties suggest that: (a) more and more researchers have started to pay attention to consumers during modeling; (b) sustainab... [more]

The urgent need for CO2 reduction is calling upon the industry to contribute. However, changes within local energy supply systems including efficiency enhancement are bound to several economical and technical constraints, which results in interfering trade-offs that make it difficult to find the optimal investment option for CO2 mitigation. In this article, a new optimization model is presented that allows to optimize the design and operation of a supply and heat recovery system and production scheduling simultaneously. The model was used for retrofitting of a small brewery’s local energy system to identify decarbonization measures for eight potential future scenarios with different technical, economical and ecological boundary conditions. The results show that the proposed cost-optimized changes to the current energy system only slightly reduce carbon emissions if decarbonization is not enforced since the optimal solutions prioritize integration of photo voltaic (PV) modules that main... [more]

To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is prop... [more]

An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These w... [more]

This article aims to target the key factors that could positively affect crowdfunding success for green products in order to promote crowdfunding efficiency and green supply chain management. Methods: Data were collected through expert interviews and questionnaires and then processed through analytic hierarchy process (AHP) analysis. Statistical tool: This study used Expert Choice as the software for AHP analysis. Sampling: There were 20 participants (20 effective) in pretesting and 30 (23 effective) in formal testing. Participants were followers of green products in Taiwan. Results: (1) Twenty-four factors were abstracted to form the final construct; (2) the 24 key factors could be divided into 2 hierarchies, with 5 primary factors and 19 secondary factors; (3) among the 5 primary factors, “green diversified context” was the most influential; (4) among the 19 secondary factors, “product material is safe and non-toxic” was the most important. Conclusions: Funders would be likely to fin... [more]

Al assisted alloying of carbon steel in Submerged Arc Welding (SAW) by Al-Cr-Ti-Cu unconstrained metal powders is applied. A base case without metal powder additions is compared to two metal powder addition schedules, Al-Cu-Ti and Al-Cu-Ti-Cr. Al powder is used as a deoxidiser element to control the oxygen partial pressure at the weld pool−molten flux interface to ensure that most of the Ti and Cr metal powder is transferred into the weld pool and that the weld metal ppm O is controlled within acceptable limits of 200 to 500 ppm O. The likely sequence of alloy melt formation is deduced from the relevant alloy phase diagrams. The effect of Fe addition into the initial Al-Cu-Ti and Al-Cu-Ti-Cr alloy melt is illustrated in thermochemical calculations. Increased metal deposition productivity with metal powder addition in SAW is confirmed. The metal deposition rates increased by 19% and 40% when Al-Cu-Ti and Al-Cu-Ti-Cr powders were applied at the same weld heat input used in the absence of... [more]

The primary goal of business organization is optimally maximizing their productivity and profit whilst reducing the cost resulting from lost sales and services given to their customers, which can be achieved by exceeding the balance between the demand and supply. Analyzing real-world situations, including integrated queuing-inventory systems, such as M/M/1-systems and M/M/1/∞-systems, can help business organizations reach this goal. This research analyzes integrated queuing-inventory systems with lost sales validated under a deterministic and uniformly distributed order size scheme under continuous review. The limited integrated inventory-queuing M/M/1/N-1-system was chosen as subject of our interest due to its closeness to reality. Thus, this system with exponentially distributed deteriorating products and random planning time with lost sales was simulated. This research aimed to analyze customers’ sanctification by studying the addition of the deterioration parameter γ to the model u... [more]

This paper focuses on the problem of event-triggered H∞ asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Both the probability information involved in the original system and the filter are assumed to be only partly available. Under this framework, via employing the Lyapunov−Krasovskii functional and matrix inequality transformation techniques, a sufficient condition is given and the filter is further devised to ensure that the resulting filtering error dynamic system is stochastically stable with a desired H∞ disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example.

The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the sc... [more]

Suitable trajectories with minimum execution time are essential for an industrial robot to enhance productivity in pick and place operations. A novel point-to-point trajectory planning algorithm (PTPA) is proposed to improve the motion efficiency of industrial robots. The jerk profile for a trajectory model is determined by five intervals and the jerk constraint. According to the kinematic constraints and two shape coefficients, a velocity threshold and three displacement thresholds are calculated for an individual joint to transfer the proposed jerk motion profile into four specific profiles. The optimal trajectory model of the joint is developed for the minimum-time and jerk-continuous trajectory via the performance evaluation with the input displacement and three displacement thresholds. Moreover, time-based motion synchronization for all joints is taken into account in PTPA to decrease unnecessary burdens on the actuators. The simulations illustrate that the execution time by PTPA... [more]

Six sigma is understood as a technique for the continuous improvement in process quality; however, it has been rarely scientifically analysed in small- and medium-sized enterprises (SMEs). SMEs representthe vast majority of enterprises throughout economies and contribute to automotive supply chains in various tier ranks. As SMEs are known to lack resources and skills while focusing on short-term benefits rather than on long-term gradual improvements, the aim of of this paper is to analyse the perception of six sigma process capabilities in automotive supply chains assuming differences in company size, supply chain rank and six sigma duration. This was tested with Fisher’s exact test. Companies with less than 1000 employees, subsuppliers and companies with a six sigma implementation in the last 3 years struggled to meet six sigma principles, suggesting that mainly small companies inhibit a risk for the supply chain. These findings contribute to the existing theoretical body of knowledge... [more]

This article describes a methodical framework that combines two specific methods of Lean management, namely the ABC method and the MRP planning method. The article further argues that combining the ABC inventory method with subsequent MRP planning is beneficial if the combination is implemented in practice. To demonstrate the benefits, the framework is tested using a case study company. The presented case-study problem is to reduce the number of changeover downtimes in the environment of an engineering production company. The researched company deals with the problem of setting up production lines in a way to minimize the number of downtimes within one work shift. Within the solution, four possible variants of the production plan are presented. By combining the ABC and MRP methods, up to four changeovers can be saved, which in financial terms represents a saving of about EUR 450,000 per year.

We live in the big data era, in which a large amount of information is continuously created, registering all kinds of events, such as those generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes [...]

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and ach... [more]

Vaccines are biological products containing a weakened, inactivated part of bacteria or viruses that are not harmful to the human body. Vaccine manufacturers and distributors should always store vaccines at the right temperature. To do this task, manufacturers and distributors need to manage cold supply chains to the required standards. Cold chain management helps manufacturers control and keep vaccines at the right temperature while ensuring quality and extending their expiration date. That will help businesses in the medical industry reduce economic losses, avoid waste, and bring more significant benefits to patients. The selection and evaluation process for logistics suppliers, especially those who deal with low-temperature storage, considers many factors to reduce the potential waste of products from poor storage strategies. The author introduces an integrated approach to solve such a fuzzy multiple criteria decision-making (MCDM) problem based on the Fuzzy Analytical Hierarchy Pro... [more]

In recent decades, manufacturers’ intense competitiveness to suit consumer expectations has compelled them to abandon the conventional workflow in favour of a more flexible one. This new trend increased the importance of master production schedule and make-to-order (MTO) strategy concepts. The former improves overall planning and controls complexity. The latter enables the production businesses to reinforce their flexibility and produce customized products. In a production setting, fluctuating resource capacity restricts production line performance, and ignoring this fact renders planning inapplicable. The current research work addresses the MPS problem in the context of the MTO production environment. The objective is to resolve Rough-Cut Capacity Planning by considering resource capacity fluctuation to schedule the customer’s order with the minimum cost imposed by the company and customer side. Consequently, this study is an initial attempt to propose a mathematical programming appro... [more]

Supply chain management begins with supplier evaluation and selection. The supplier selection deals with various criteria with different contexts which makes it a complex multi-criteria decision-making (MCDM) method. In this paper, a novel MCDM method, called the alternative ranking process by alternatives’ stability scores (ARPASS), is proposed to solve supplier selection problems. ARPASS considers each alternative as a system that is constructed on integrated components. To perform properly, a system requires high integrity and stability. ARPASS utilizes the stability of alternatives as an effective element for ranking the alternatives. The ARPASS is developed in two forms, ARPASS and ARPASS*. The new method utilizes standard deviations and Shannon’s entropy to compute the alternatives’ stabilities. In this paper, in addition to the new MCDM methods, a new method called the grey equilibrium product (GEP) is introduced to convert grey linguistic variables into crisp values, using deci... [more]

Biomass has gained global attention as one of the most important renewable energy resources that reduces greenhouse gas emissions. Various research works have been dedicated to biomass supply chain in the past decade as to continuously support the deployment of biomass resources for regional applications. In this work, a novel graphical method based on process integration is proposed for targeting the amount of biomass resources needed for a power generation problem. Apart from having a good visualized interface, the graphical method provides good insights to stakeholders on the macro-level planning of biomass allocation. Two examples are solved to demonstrate the newly proposed methods.