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.

The Roll-to-Roll (R2R) process refers to a high-efficiency, low-cost, continuous production method. The roll material used for processing is a flexible plastic or metal film. In many R2R processes, polarizing films are high-precision products with a high output value. In the production of conventional polarizers, product inspection will only be carried out after the production of the polarizing film is completed. The principal raw material of a polarizer sheet is a hydrophilic polymer, the properties of which may be influenced by water vapor, which degrades its quality. Whether or not the product is impacted can be ascertained by means of a quality inspection, but it must be performed after the process is finished. However, it is already too late when a defective product is detected: the production cost is increased, the schedule is influenced and the delivery date is delayed. The focus of this research was on environmental monitoring of the production drying process oven, but the comm... [more]

Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external organizations and systems. This study presents a narrative review explaining the existing knowledge on digital transformation in supply chain process management using text mining. It summarizes the existing literature to explain the current state of the art in supply chain digitalization. This comprehensive review identifies the most important topics and technologies and determines the future trends in this emerging field. We investigate the articles published in Web of Science and Scopus databases and use text mining techniques (clustering and topic modeling) on the article contents. Using VOS viewer, a bibliometric analysis of 395 articles with 12,700 references is analyzed. The contents of the articles are explored using text mining approa... [more]

The flexible job-shop scheduling problem with indirect energy and time-of-use (ToU) electricity pricing (FJSP-IT) is investigated. Considering the production cost, which includes the indirect energy cost, direct energy cost and time cost, the cost evaluation model under ToU pricing is built. To minimize the total production cost of the FJSP-IT, an approach based on a genetic algorithm and Petri nets (GAPN) is presented. Under this approach, indirect energy and direct energy are modeled with Petri net (PN) nodes, the operation time is evaluated through PN simulation, and resource allocation is fine-tuned through genetic operations. A group of heuristic operation time policies, especially the exhausting subsection policy and two mixed policies, are presented to adapt to the FJSP-IT with vague cost components. Experiments were performed on a data set generated from the banburying shop of a rubber tire plant, and the results show that the proposed GAPN approach has good convergence. Using... [more]

Second-generation biomass is an underexploited resource, which can lead to valuable products in a circular economy. Available locally as food waste, gardening and pruning waste or agricultural waste, second-generation biomass can be processed into high-valued products through a flexi-feed small-scale biorefinery. The flexi-feed and the use of local biomass ensure the continuous availability of feedstock at low logistic costs. However, the viability and sustainability of the biorefinery must be ensured by the design and optimal operation. While the design depends on the available feedstock and the desired products, the optimisation requires the availability of a mathematical model of the biorefinery. This paper details the design and modelling of a small-scale biorefinery in view of its optimisation at a later stage. The proposed biorefinery comprises the following processes: steam refining, anaerobic digestion, ammonia stripping and composting. The models’ integration and the overall b... [more]

This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the pro... [more]

Aiming at the problem of automatic path planning for the whole safflower bulbs during the operation of safflower picking robots, an improved ant colony algorithm (ACA) was proposed to plan the three-dimensional path of the safflower picking points. The shortest time and distance were taken as the overall goal of path planning to comprehensively improve the working efficiency of safflower picking robots. First, in order to shorten time, the angle induction factor was introduced to reduce the angle rotation of the end-effector. Second, in order to shorten the length of the picking path, the picking track was optimized. Finally, the design of the secondary path optimization reduced the number of picking points, which not only shortened the length of the picking path, but also shortened the picking time. The simulation results show that the path planned by the improved ACA was reduced by three picking points, shortening the total length by 74.32%, and reducing the picking time by 0.957 s.... [more]

Multi-equipment multi-process frequent scheduling under complex constraints is at the root of a large number of idle time fragments and transport waiting time in multi-equipment processes. To improve equipment utilization and reduce idle transportation time, a production process optimization scheduling algorithm with “minimum processing time and minimum transportation time” is proposed. Taking into account factors such as product priority, equipment priority, process priority, and overall task adjustment, the scheduling optimization is carried out through a hybrid algorithm combining a one-dimensional search algorithm and a dual NSGA-II algorithm. Compared with other algorithms, the scheduling algorithm proposed in this article not only shortens the minimum processing time but also strives to maximize the utilization rate of each piece of equipment, reducing the processing time of the enterprise by 8% or more, while also reducing the overall transportation time and indirectly reducing... [more]

In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show t... [more]

As renewable energy penetration increases, the lack of flexibility in a multi-renewable power system can seriously affect its own economics and reliability. To address this issue, three objectives are considered in this study: power fluctuations on tie-line, operating cost, and curtailment rate of renewable energy. Presented also is an optimal day-ahead scheduling model based on the MREPS for distributed generations with flexibility constraints. The multi-objective particle swarm optimization (MOPSO) algorithm can be applied to obtain a set of Pareto non-dominated solutions for the day-ahead scheduling strategy with the proposed model. By using fuzzy comprehensive evaluation, the optimal compromise solution is determined in the set. The presented method sacrifices a small amount of economy and power fluctuation, but it can reduce the deviation between forecast and realized power fluctuations on the tie-line, while improving the utilization of renewable energy.

Reducing direct carbon emissions in the transport sector is crucial for carbon neutrality. It is a considerable challenge to achieve substantial CO2 emissions reductions while satisfying rapidly growing traffic demands. Previous studies cannot be applied directly in long-term planning for the transport sector with rapid demand growth. To bridge this gap, a multi-regional model is proposed in this paper to quantify the optimal decarbonisation path for the transport sector in order to save costs. Considering modal shift and infrastructure construction, this model regards the transport sector as a whole and China is taken as a case study. The results show that electricity and hydrogen will be the major fuels of the transport sector in the future, accounting for 45 percent and 25 percent of fuel demands in 2060. This means that the electricity used by the transport sector accounts for 10 percent of the electricity consumed by the whole of society. The results reflect that freight transport... [more]

In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are s... [more]

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy... [more]

The review assesses the Hoshin Kanri process from the point of view of theory and practice implementations in different organizations. There are several adaptations of Hoshin Kanri and a wide range of tools used in each organization. This review aims to determine which and how companies have implemented the afore-mentioned methodology into their strategic management. The PRISMA statement was the framework for the present research. The structure for this study was obtained through a review of articles from two of the most important databases (Scopus and Web of Science). The review focuses on three parts: the theoretical basis of the methodology, a bibliometric overview of the selected articles, and practical insight into the implementation of Hoshin Kanri within the case-study organizations. Since the study’s purpose is to determine not only the companies where Hoshin Kanri was implemented but also the reasons and results of those implementations, therefore, 26 journal articles covering... [more]

In this paper, a manufacturing process for a single-server permutation Flow Shop Scheduling Problem with sequence dependant, disjoint setups and makespan minimization is considered. The full problem is divided into two levels, and the lower level, aimed at finding an optimal order of setups for a given fixed order of jobs, is tackled. The mathematical model of the problem is presented along with a solution representation. Several problem properties pertaining to the problem solution space are formulated. The connection between the number of feasible solutions and the Catalan numbers is demonstrated and a Dynamic Programming-based algorithm for counting feasible solution is proposed. An elimination property is proved, which allows one to disregard up 99.99% of the solution space for instances with 10 jobs and 4 machines. A refinement procedure allowing us to improve the solution in the time required to evaluate it is shown. To illustrate how the properties can be used, two solving metho... [more]

Under the dual−carbon goal, the research on energy conservation and emission reduction of new energy vehicles has once again become a current hotspot, and plug−in hybrid electric vehicles (PHEVs) are the first to bear the brunt. In order to improve the fuel economy of PHEV, an adaptive energy management strategy is designed on the basis of the intelligent prediction of driving cycles. Firstly, according to the vehicle dynamics model, the optimal control objective function of PHEV is established, and the relationship between vehicle fuel consumption and driving cycle is analyzed. Secondly, the initial weights and threshold of the backpropagation (BP) neural network are optimized using the particle swarm optimization (PSO) algorithm, and a PSO−BP neural network vehicle velocity prediction controller is established. Thirdly, combined with the approximate equivalent consumption minimization strategy (ECMS) algorithm to calculate the optimal initial equivalent factor in the prediction time... [more]

The Aircraft Arrival/Departure Problem (AADSP) is the core problem in current runway system, even has become the bottleneck to prevent the improvement of the airport efficiency. This paper studies the single runway AADSP. A Mixed Integer Programming (MIP) model is constructed and an algorithm named Ant Colony based on Rank 2 Matrix Approximation (RMA-AC) method is proposed. Numerical results validate that the new algorithm, as well as the new model, exhibits better performance than CPLEX and the traditional two-phase algorithm. The runway efficiency enhanced by RMA-AC, within 20 s computation, is about 2−5% even for the 800 aircraft sequences. It is a promising method to improve the efficiency of the future aircraft scheduling system.

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.