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Motivated by a problem in the context of DiDi Travel, the biggest taxi hailing platform in China, the purpose of this paper is to propose a novel facility location problem, specifically, the single source capacitated facility location problem with regional demand and time constraints, to help improve overall transportation efficiency and cost.

This study develops a mathematical programming model, considering regional demand and time constraints. A novel two-stage neighborhood search heuristic algorithm is proposed and applied to solve instances based on data sets published by DiDi Travel.

The results of this study show that the model is adequate since new characteristics of demand can be deduced from large vehicle trajectory data sets. The proposed algorithm is effective and efficient on small and medium as well as large instances. The research also solves and presents a real instance in the urban area of Chengdu, China, with up to 30 facilities and demand deduced from 16m taxi trajectory data records covering around 16,000 drivers.

This study examines an offline and single-period case of the problem. It does not consider multi-period or online cases with uncertainties, where decision makers need to dynamically remove out-of-service stations and add other stations to the selected group.

Prior studies have been quite limited. They have not yet considered demand in the form of vehicle trajectory data in facility location problems. This study takes into account new characteristics of demand, regional and time constrained, and proposes a new variant and its solution approach.

The purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.

The algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.

The proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.

In general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.

It is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.

Reliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.

The utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

This paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.

Cloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.

The obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.

Although the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.

The outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.

Although service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.

This paper aims to consider the exam timetabling of the re‐sit session in the Faculty of Economics and Management Sciences of Sfax. The objective is to find a timetable which minimizes the number of timeslots for exams required by the enrolled students.

Two heuristic procedures based on graph colouring are developed and tested on real data to solve the exam timetabling problem at the faculty.

These heuristics were tested on a simple example which shows the out‐performance of the second heuristic compared with the first one. When tested with the real data of the faculty, exam size heuristic provides a timetable with a shorter timeframe; however, the timetable obtained from the second heuristic is of better quality.

The main contribution of this paper is to create an automated exam timetabling that helps the faculty to manage its own enterprise system.

The purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information.

A heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data.

The proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability, and is more adaptive to big data applications.

The paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews.

The proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results.

The proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information.

Recently several authors have concentrated their efforts in developing models to determine the economic lot size for multi‐stage systems. This is due to the fact that an increasing number of organisations are implementing material requirements planning systems. Numerous models have been developed and tested on problems with finite and rolling horizons and with deterministic time varying demand patterns.

In practical environments, machines subject to maintenance are prevalent in many production systems. This paper aims to find a schedule that minimizes the completion time (or equivalently, the total setup time) subject to maintenance and due dates.

An efficient heuristic is presented to provide the near‐optimal solution for the problem. The performance of the heuristic is evaluated by comparing its solution with the optimal solution obtained from the integer linear programming model.

In many production systems, the sequence‐dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. The paper studies the sequence‐dependent setup time problem with periodic maintenance, where several maintenances are required. Computational results show that problems with larger time interval and smaller maintaining time can produce a smaller completion time.

Here an efficient heuristic is developed to provide the near‐optimal schedule for the problem. The proposed integer linear programming model is also presented to provide the optimal schedule. However, the proposed heuristic and the integer linear programming model developed in the paper are appropriate for those companies where maintenance is performed periodically and the sequence‐dependent setup times of their jobs are required.

The paper presents the heuristic and the integer linear programming model to deal with sequencing and maintenance problems.

This paper seeks to apply a heuristic approach to solve the facility layout problem and the description of a new computer‐aided layout design system.

The system utilizes a new approach for computing the adjacency scores, stacking of departments, and reserving or changing the department's shapes and dimensions. The system algorithms are based on calculating the minimal distance between departments and modified departmental closeness rating.

The research addressed in this paper has resulted in developing FLASP (Facility LAyout Support Program) software. FLASP could reduce the number of iterations needed to reach the optimal solution of the layout problems by restricting the location for each department depending on the relationships between them.

The system is built on a set of algorithms that are concerned with stacking, calculating the shortest rectilinear distances between departments, adjacency matrix system, modifications capabilities, and plans main aisles surrounding each department.

The program gathers the importance of both the adjacency relationships and the distances between departments in a way that depends on the concept that the adjacency score should not be nullified just because the two departments are no longer strictly adjacent. It rather considers that the adjacency score fades away gradually with the increase of distance between the two departments which leads to a main difference in distance consideration.

Recent work has demonstrated the possibility of selectively sintering polymer powders with radio frequency (RF) radiation as a means of rapid, volumetric additive manufacturing. Although RF radiation can be used as a volumetric energy source, non-uniform heating resulting from the sample geometry and electrode configuration can lead to adverse effects in RF-treated samples. This paper aims to address these heating uniformity issues by implementing a computational design strategy for doped polymer powder beds to improve the RF heating uniformity.

Two approaches for improving the RF heating uniformity are presented with the goal of developing an RF-assisted additive manufacturing process. Both techniques use COMSOL Multiphysics® to predict the temperature rise during simulated RF exposure for different geometries. The effectiveness of each approach is evaluated by calculating the uniformity index, which provides an objective metric for comparing the heating uniformity between simulations. The first method implements an iterative heuristic tuning strategy to functionally grade the electrical conductivity within the sample. The second method involves reorienting the electrodes during the heating stage such that the electric field is applied in two directions.

Both approaches are shown to improve the heating uniformity and predicted part geometry for several test cases when applied independently. However, the greatest improvement in heating uniformity is demonstrated by combining the approaches and using multiple electrode orientations while functionally grading the samples.

This work presents an innovative approach for overcoming RF heating uniformity issues to improve the resulting part geometry in an RF-assisted, volumetric additive manufacturing method.

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.