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This paper aims to propose a combined methodology to help decision makers in evaluating and selecting the most effective part feeding system.

As a first step of the methodology, a hierarchical clustering analysis is applied to design a kitting or hybrid feeding system. Second, activity-based costing methodology is applied to determine which system is better according to their costs. Besides, sensitivity analysis is implemented to observe the behavior of the system in case of the takt time changes.

Using kitting systems purely can lead to problems because of the big and expensive parts in the mixed-model assembly systems. Therefore, the hybrid feeding policy can provide better solutions for such systems.

A case study is conducted in a company and the most produced product of the company is considered to design the part feeding system. Results indicated that transportation cost has a large proportion on the total cost and the hybrid feeding policy may be a good solution to reduce this cost.

The paper includes implications for the design of hybrid feeding systems in lean-based assembly lines. The proposed methodology may be a practical tool for decision makers to design and decide on the part feeding policy.

Kitting design has not been studied yet to the best of the authors’ knowledge. Besides, there is no certain decision methodology indicating which system is better. In this study, different methods are combined as a new methodology with the purpose of industrial decision-making.

The purpose of this paper is to present a literature review on parts feeding policies and to provide the components of parts feeding systems via a classification structure.

This paper determines the scope and components of parts feeding systems via a classification structure under three main components such as the storage of parts, transport of parts and feeding policy. Afterward, it is focused on parts feeding policies and the related papers are reviewed and analyzed according to their feeding policy types, objectives, solution methodologies and the application types.

A classification structure showing the components and scope of parts feeding systems is provided. Parts feeding policies are handled in detail and feeding policy types, objectives, solution methodologies and application types in the existing studies are presented in this paper. However, the paper highlights the open research areas and advances for academics and presents applied solution methodologies and case studies for practitioners.

This paper reveals the scope of parts feeding systems by presenting a classification structure including three main components and related subcomponents and provides a comprehensive literature review on parts feeding policies.

Assembly systems require uninterrupted components' availability to feed workstations. This paper aims to propose a methodology to help managers in evaluating and selecting the most suitable policy for materials delivery to the shop floor. The analysis focuses on three basic policies, namely kitting, just in time kanban‐based continuous supply and line storage, even including class‐based hybrid policies.

Descriptive models are developed to design components' delivery systems and to compute their performances. Empirical criteria are utilized to associate specific policies to components classes in order to implement customized hybrid line feeding policies. A case study is then included to exemplify the method application and to show its capabilities as a decision making tool.

Hybrid feeding policies may be preferable to a single feeding policy common to all components. This is shown in a representative case study. However, in general there is a priori superior method and only a comparison of alternative feeding policies based on objective performance measures can determine the best approach in specific industrial applications.

The methodology is aimed at preliminary sizing and selection of alternative line feeding systems in deterministic environments. It is not intended for detailed performance analysis of assembly systems.

Production managers are given quantitative decision tools to properly select the components' delivery method at an early decision stage. This allows trade‐offs between alternatives to be explored in order to deploy customized feeding policies differentiated on components basis to better fit specific company requirements.

The paper extends previous descriptive models for line feeding systems and includes the possibility of hybrid policies.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

The purpose of this paper is to develop an optimization model allowing the choice of parts feeding policy to assembly lines in order to minimize total cost.

An integer linear programming mathematical model is developed to assign the optimal material feeding policy to each part type. The model allows choice between kitting, line stocking and just in time delivery policies.

The choice of assembly lines feeding policy is not trivial and requires a thorough economic comparison of alternatives. It is found that a proper mix of parts feeding policies may be better that adopting a single material delivery policy for all parts.

The model is aimed at single-model assembly lines operating in a deterministic environment, but can be extended to the multi-model line case. While relevant quantitative cost drivers are included, some context-related qualitative factors are not included yet. The model assumes that information about product structure and part requirements are known and that a preliminary design of the assembly system has been carried out.

Production managers are given a quantitative-decision tool to determine the optimal mix of material supply policies at an early decision stage.

Respect previous simplified literature models, this approach allows to quantify a number of additional factors which are critical for successful implementation of cost-effective parts feeding systems, allowing comparison of alternative policies on a consistent basis.

This paper aims to presents the bandwidth enhancement of a hybrid slot–loop antenna using a modified feed structure.

The conventional monopole feed of the hybrid slot–loop radiator is loaded with a flat microstrip patch to excite higher-order modes. The proposed antenna combines the resonant modes of the slot antenna, the loop antenna and the patch loading.

The antenna exhibits a dual-band response suitable for GSM 1800/1900 and ultrawideband (UWB) standards. The impedance bandwidth extends from 1.65 to 1.95 GHz (11.42 per cent) and 3 to 11.1 GHz (114.9 per cent). The proposed antenna has the smallest footprint with a peak gain of 5.07 dBi at 1.8 GHz and 4.97 dBi at 6 GHz. The prototype antenna is fabricated and the simulation results are validated using experimental measurements. The performance of the bandwidth-enhanced hybrid slot–loop antenna is compared with that of other slot antennas.

Thus, a hybrid slot–loop antenna with an enhanced bandwidth has been reported in this study. The conventional monopole feed of the antenna is replaced with a monopole ending with a microstrip patch load. The antenna covers the operating bands of GSM 1800/1900 and UWB. The proposed antenna has a smaller footprint compared with other wide-slot antennas reported in the literature.

Propagation characteristics of millimeter wave (mmW) frequencies that are being explored for implementing 5G network are quite different from sub 3GHz frequencies in which 4G network is operating, and hence antenna design for mmW 5G network is going to be significantly different. The purpose of this paper is to bring forth the unique challenges and opportunities of planar antenna design for mmW 5G network.

A lot of notable contemporary work has been investigated for this study and reported in this paper. A comparison of 4G and 5G technologies has been carried out to understand the difference between the air interface of two technologies that governs the antenna design. Important research gaps found after collating the work already done in the field have been bullet pointed for the use by many researchers working in this direction.

Several antenna design considerations have been laid out by the authors of this work, and it has been claimed that mmW 5G antenna design must satisfy these design considerations. In addition, prominent research gaps have been identified and thoroughly discussed.

As research in the field of mmW antenna design for 5G applications is still evolving, a lot of work is currently being done in this area. This study can prove to be important in understanding different challenges, opportunities and current state-of-art in the field of mmW planar antenna design for 5G cellular communication.

The purpose of this paper is to propose a decision model to choose between kitting and line stocking at the level of single parts, while taking into account the variable operator walking distances. Different ways of feeding assembly lines, such as kitting and line stocking not only have an impact on in-plant logistics flows but also determine the amount of stock that is available at the line. This, in turn, has an impact on operator walking distances during assembly.

A mixed integer linear programming model is developed for the assignment of parts to one of both methods, and to be able to extensively test the model, an algorithm is created for the construction of representative datasets.

Parts are often kitted because of a space constraint at the line, but even without a space constraint, the shorter walking distances might give preference to kitting. An analysis is presented that demonstrates how specific part characteristics influence the chances of a part being kitted.

Our research model can be extended to include, e.g., the study of alternative in-plant logistic designs and the outsourcing of kitting to a third-party logistics provider (3PL) or to the suppliers.

The objective assignment model and the insights obtained from it are valuable for logistics and production engineers that otherwise have to rely solely on intuition. In situations with thousands of components, intuition mostly falls far short.

First, existing models do not consider variable walking distances, which are shown to have a crucial impact on the decision. Second, the data instances created allow for a systematic comparison of future research in the field.

The purpose of this paper is to analyze the performance of the gas turbine cycle integrated with solid oxide fuel cell technology. In the present work, intermediate temperature solid oxide fuel cell has been considered, as it is economical, can attain an activation temperature in a quick time, and also have a longer life compared to a high-temperature solid oxide fuel cell, which helps in the commercialization and can generate two ways of electricity as a hybrid configuration.

The conceptualized cycle has been analyzed with the help of computer code developed in MATLAB with the help of governing equations. In this work, the focus is on the performance investigation of a Gas turbine intermediate temperature solid oxide fuel cell hybrid cycle. The work also analyzes the performance behavior of the proposed cycle with various design and operating parameters.

It is found that the power generation efficiency of the IT-SOFC-GT hybrid system reaches up to 60% (LHV) for specific design and operating conditions. The cycle calculations of an IT-SOFC-GT hybrid system and its conceptual design have been presented in this work.

The unique feature of this work is that IT-SOFC has been adopted for integration instead of HT-SOFC, and this work also provides the performance behavior of the hybrid system with varying design and operating parameters, which is the novelty of this work. This work has significant scientific merit, as the cost involved for the commercialization of IT-SOFC is comparatively lower than HT-SOFC and provides a good option to energy manufacturers for generating clean energy at a low cost.