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By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method; (2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms; and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large. [ABSTRACT FROM AUTHOR]

Dynamic bus scheduling refers to adjusting the departure time according to the latest time-varying information or adjusting bus speed in the process of operation. These control strategies can prevent bus bunching and alleviate traffic pressure. The paper studies the multiline bus dynamic scheduling with consideration of departure time and speed meanwhile. The hyperheuristic algorithm is proposed, and low-level heuristics (LLH) operators are designed. The simulation experiment is performed for the passenger flow distribution of different strengths and types of different scenarios. By comparing the experimental results of genetic algorithm (GA) and hyperheuristic algorithm in solving different scenarios, the results show that in smooth, increasing, decreasing, and multiconvex passenger flow mode, the performance of the hyperheuristic algorithm is higher than that of GA. The promotion rate reaches 18∼28%, and especially the average value of the hyperheuristic algorithm designed under multiconvex passenger flow is up to 28.62%, significantly reducing passengers' waiting time. By comparing the stability of the three passenger flow modes, the results illustrate that the stability of the hyperheuristic algorithm is lower than that of GA. For the smooth passenger flow mode, the stability of medium and lower density of GA is higher than that of the hyperheuristic algorithm. In comparison, the high-density stability of the hyperheuristic algorithm is better than that of GA. [ABSTRACT FROM AUTHOR]

Dynamic bus scheduling is a rational solution to the urban traffic congestion problem. Most previous studies have considered a single bus line, and research on multiple bus lines remains limited. Departure schedules have been typically planned by making separate decisions regarding departure times. In this study, a joint optimization model of the bus departure time and speed scheduling is constructed for multiple routes, and a coevolutionary algorithm (CEA) is developed with the objective function of minimizing the total waiting time of passengers. Six bus lines are selected in Shenyang, with several transfer stations between them, as a typical case. Experiments are then conducted for high-, medium-, and low-intensity case of smooth, increasing and decreasing passenger flow. The results indicate that combining the scheduling departure time and speed produces better performances than when using only scheduling departure time. The total passengers waiting time of the genetic algorithm (GA) group was reduced by approximately 25%–30% when compared to the fixed speed group. The total passengers waiting time of the CEA group can be reduced by approximately 17%–24% when compared to that in the GA group, which also holds true for a multisegment convex passenger flow. The feasibility and efficiency of the constructed algorithm were demonstrated experimentally. [ABSTRACT FROM AUTHOR]

According to the requirement of energy sustainable development strategy in Jilin province, this paper evaluates the performance of wind power coupling compressed air energy storage projects for a wind farm in Jilin from the perspective of sustainability. Firstly, a sustainable criteria system is established from economic, social and ecological dimensions, and 10 secondary criteria are given. Secondly, to fully express the hesitancy and fuzziness of experts, linguistic fuzzy hesitant sets (LFHSs) are used to describe the subjective evaluation information. And then, the cloud model is developed to deal with the randomness and fuzziness of LFHS. Thirdly, a multi criteria decision-making model based on improved Choquet fuzzy integral and deviation degree is proposed for a multi criteria evaluation problem with initial data in the form of crisp values, interval numbers and linguistic fuzzy hesitant sets. The deviation degree can deal with multi-source heterogeneous data and Choquet fuzzy integral improved with Mobius transformation can eliminate the overlap information between criteria. Finally, the case study in Jilin is given to illustrate the effective of the proposed evaluation criteria and technique. From the results, the project 4: wind power coupling compressed air energy storage system with meshing switch is the most suitable alternative for the wand farm, followed by the project of wind power coupling compressed air energy storage system with variable configuration. Moreover, the sensitivity analysis reveals that the project 4 always occupies its top ranking when its weight is floating within a certain range. At last, some policy implications to promote wind power industry are given for the government. • The sustainability evaluation criteria system for wind power project is presented. • A MCDM method based on Choquet fuzzy integral and Mobius transformation is proposed. • Linguistic fuzzy hesitant sets are used to present subjective evaluation information. • The proposed model can deal with multi-source heterogeneous data. [ABSTRACT FROM AUTHOR]

In modern cities, public transit is important for meeting people's transportation needs. Dynamic bus dispatching plays an important role in a transit system, especially when the system experiences extreme weather, fluctuations in passenger flows, etc. Consequently, the assumption presented in the literature that departure timetables of buses are pre-determined at stops is not applicable to transit systems in developing countries, where traffic conditions vary frequently and dynamically. With the development of new technology, e.g. the internet of things, real-time information for public transit systems can be obtained conveniently. Considering multiple types of real-time information such as dynamic passenger flows and road traffic status, we propose an optimization model for dynamic bus dispatching to minimize the overall waiting time of passengers in a transit system. In our model, multiple bus routes and waiting times at the transfer stations are taken into consideration. Accordingly, we develop a genetic algorithm with memory-based initialization to solve the model. The effectiveness of the proposed approach is verified for different scenarios via numerical experiments. [ABSTRACT FROM AUTHOR]

Biomass energy has extensive application foreground in power generation because of its economic and social benefits. Aiming at selecting the best biomass fuel procurement and storage modes in Jilin province, this paper proposes an evaluation model with linguistic hesitant fuzzy sets (LHFSs), which is good at expressing the hesitancy, inconsistency and uncertainty of subjective evaluation information. Based on cloud model and three entropy principle, the transformation between linguistic hesitant fuzzy sets and comprehensive cloud model is realized. In order to eliminate interdependency among criterion, a new Choquet integral aggregation operator based on the Sigmoid function is proposed. According to the expectation values, the ranking result of aggregated LHFSs is obtained. At last, an illustrative example of modes evaluation in Jilin province is taken in the present study to make the proposed method comprehensible. The result shows that mode 3: farmer + middleman + third-party logistics + power plant, is the most reasonable mode for Jilin biomass energy development. From the perspective of supply chain, middlemen and third party logistics can reduce the procurement and transportation cost in the supply chain, which are two bigger influential factors of fuel cost. Middlemen are not only a bridge between plants and farmers, but also the operator in the supply chain. Through the analysis of the result, suggestions from the perspective of power plants and the government are given. • Linguistic hesitant fuzzy sets are used for evaluating biomass power generation fuel procurement and storage modes. • A multi-criteria decision-making method based on cloud model and Choquet integral is proposed. • A new linguistic hesitant fuzzy aggregation operator based on Choquet integral and the Sigmoid function is presented. • "farmer, middleman, third-party logistics and power plant" is the best mode for Jilin biomass power development. [ABSTRACT FROM AUTHOR]

Optimisation of product family design has been emphasised and studied for many years. However, previous studies only took overall cost or profit as an optimisation objective, but ignored the supply risk of a product family. Moreover, the discount associated with the bidding price, which is common in practice, was not considered in the modelling. In this paper, we propose a new multi-objective optimisation approach integrating supplier selection into product family design. In our optimisation model, not only the profit but also the supply risk of a product family is formulated as optimisation objectives. Consequently, we can evaluate and optimise a product family from many perspectives. In addition, as a bidding price discount may affect product family's configuration and supplier selection, we include linear piecewise discount of bidding prices from suppliers in our optimisation model. The NSGA-II algorithm is developed to achieve Pareto non-dominated solutions of the multi-objective optimisation model. Sensitivity analysis on the model parameters is performed, and several managerial insights for enterprises are achieved in the case study of a printing calculator product. [ABSTRACT FROM AUTHOR]

To provide high-quality service, it is necessary to emphasize customers' emotional needs. It is necessary to determine the priority strategy for service attributes owing to limited resources and different relative levels of influence on customer perception. Therefore, this study proposed a service design framework based on the Kansei Engineering (KE) and Kano models to design services oriented toward customer perception. In the modeling analysis stage, the partial least squares algorithm and decision tree mining were used to construct a relationship model between service perception and attribute, and use the intention model. Finally, the in-flight service of a Chinese airline was considered as an application case study. The results obtained thus revealed a relationship between perception and service attributes, and their influence on customers' intentions. These validated the proposed method and provided new concepts for service design. [ABSTRACT FROM AUTHOR]

The distributed energy system (DES) has been regarded as a socio-technical innovation that can improve energy efficiency and reduce carbon emission. Based on the construction and planning of a DES project in Henan Province, this paper aims to develop a comprehensive decision-making framework for selecting the most appropriate DES composition schemes with multi-source heterogeneous data. Thus, a new multi-criteria decision-making method based on fuzzy preference relations and improved Choquet integral is generated. Firstly, an evaluation criteria system is established from economic, technical, environmental and social dimensions, and 10 secondary criteria are presented. Secondly, based on the interval fuzzy preference relations of criteria given by experts, the iteration of weights of criteria is continued until the group consistency degree reaches the threshold. Thirdly, the deviation degree is used to transform the initial data, which include crisp values, interval numbers and linguistic terms to isomorphism data. Thereafter, the fuzzy measures are calculated using Mobius transformation coefficients and the final scores of the alternatives are obtained by means of Choquet integral, which can eliminate the overlap information between criteria. Finally, a real case is given to illustrate the effectiveness of the proposed evaluation criteria and technique. The result shows the multi energy complementary system as the most appropriate alternative for the project, followed by the solar-air source heat pump system, fuel cell system, natural gas system, internal combustion engine system and traditional system. Conclusively, the discussion, comparative analysis and some policy implications to promote the development of distributed energy industry are given. This study not only can assist stakeholders to obtain greater benefits of the DES, but also can serve as a reference for the government towards the development of more robust regulations and policies. • Six different distributed energy system (DES) composition schemes are analyzed. • The fuzzy preference relations are developed with the requirement of consistency degree. • A new decision-making method with multi-source heterogeneous data is proposed. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies has created more opportunities for the channel innovation in residential curbside collection and the technical revolution of material recovery facilities. In waste management, single stream allows the households to bundle the recyclable waste, and dual stream requires the households to separate the recyclable waste individually. However, the sustainable development of the environment has led the government to focus on the transition between two waste recycling systems. In this study, we establish an analytical framework of the sequential game to study the sustainable evolution from single stream to dual stream and from dual stream to single stream. When the households are heterogeneous about the recyclable waste, there is a duopoly price competition in waste management, and two waste recycling systems can sequentially adopt the neutral pricing, penetration pricing and skim pricing strategy. Numerical experiments discuss the impacts of collecting costs and bundling discounts on the sustainable evolution. No matter what the evolution sequence is, two waste recycling systems can set a full or partial bundling discount policy to maximize the profit. [ABSTRACT FROM AUTHOR]

As the world's largest energy production and demand country, China's energy security is a hot issue concerned by the whole society. Most existing research focused on China's energy security performance from the point of view of the country. But, as a huge geographical country, provinces energy security performance varies hugely in different regions. So, this paper aims at evaluating Chinese provinces energy security, analyzing reasons and providing policy implications. Firstly, a comprehensive evaluation criteria system, including four dimensions: energy supply, energy using, energy economy and energy environment, is proposed. The criteria consist of 14 indicators. Secondly, Mahalanobis-Taguchi Gram-Schmidt is presented to obtain criteria weights, which not only considers subjective and objective information, but also eliminates the overlap information in criteria. Thirdly, considering that TOPSIS method ignores the correlation between the two distances (alternatives to ideal and to negative ideal), an improved TOPSIS model with set pair analysis is proposed to assess the China's regional energy security performance from 2013 to 2017. From the results, 30 provinces' energy security performance improves in general, but there is still a huge gap among different regions. The north reaches of yellow river and northwest regions are the most energy-secure, while the northeast and central regions are least energy-secure. SHAANXI, INNERMONGOLIA and SHANXI are always top three in the ranking, while the energy security performance of NINGXIA is the lowest. Unlabelled Image • A comprehensive energy security evaluation criteria system which consists of 14 indicators is presented. • To eliminate the overlap information between criteria, Mahalanobis-Taguchi Gram-Schmidt is used to obtain weights of criteria. • A multi criteria decision making method based on set pair analysis and TOPSIS is proposed to evaluate energy security performance. • From 2013 to 2017, Chinese provinces energy security performance improve in general, but there is still a huge gap among different regions. [ABSTRACT FROM AUTHOR]

Objectives. Although some studies have shown role stressors can lead to unsafe behaviors, it is unclear how role stressors induce delivery riders’ unsafe behaviors. We found that delivery riders suffered from tremendous role stressors during the delivery process and had to conduct frequent smartphone interactions. This study aimed to explore the effects of role stressors and smartphone interactions on delivery riders’ unsafe behaviors. Methods. First, a questionnaire survey (N = 326) was used to collect data, and correlation and regression analyses were conducted to explore the relationship between role stressors, smartphone interaction frequency and delivery riders’ unsafe behaviors. Second, a scenario survey (N = 35) was conducted, and comparative analysis was used to further explore how smartphone interactions affect delivery riders’ unsafe behaviors. Results. The questionnaire survey revealed that role stressors, smartphone interaction frequency and delivery riders’ unsafe behaviors were positively correlated. In addition, the role stressors forced delivery riders to conduct necessary and unnecessary smartphone interactions. The scenario survey found that smartphone interactions reduced delivery riders’ motion speed and motion ability, and increased their psychology, so they had a risk-taking mentality, which led to an increase in unsafe behaviors. [ABSTRACT FROM AUTHOR]

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

In this paper, hybrid best proximity points for a class of proximally mixed monotone set-valued mappings are discussed. In order to get rid of the fetter of continuity, we introduce the proximally ordered contraction instead of various proximal distance contractions widely applied in recent literatures and present some new existence and iterative approximation theorems of best proximity points for such mappings. Some examples are given to illustrate the advantages of the obtained results. [ABSTRACT FROM AUTHOR]

Motivated by the worldwide development of shared mobility, we investigate a vehicle routing problem with time windows and deadlines called the first-mile ridesharing problem (FMRSP). The FMRSP involves routing a fleet of vehicles, each servicing customers within specific time windows. The FMRSP generalizes the well-known vehicle routing problem with time windows (VRPTW), additionally imposing that each vehicle route arrives at the destination before the earliest deadline associated with the set of customers served by the route. The FMRSP is also related to the VRPTW and release dates, where in addition to time window constraints, a release date is associated with each customer defining the earliest time that the order is available to leave the depot for delivery. For the FMRSP, we present an exact method based on a branch-price-and-cut (BPC) algorithm combining state-of-the-art techniques and an innovative pricing algorithm. The pricing algorithm is based on a bidirectional bucket graph-based labeling algorithm, in which the backward extension of a label is computed in a constant time. Effective dominance rules used to speed up the computation are also described. Extensive computational studies demonstrate that our proposed BPC algorithm can solve optimality-modified Solomon benchmark instances involving up to 100 customers and real-world instances involving up to 290 customers. Funding: This research was supported by the National Natural Science Foundation of China [Grants 71831003, 72171043, 71831006, and 71901180]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0139. [ABSTRACT FROM AUTHOR]

With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a "showroom" effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers' optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-person zero-sum differential game with incomplete information on the initial state, namely, the first player has a private information while the second player knows only a probability distribution on the initial state, and obtain the existence of its value functions. The main novelty lies in the method for which we apply a generalized weak contraction for dynamic systems and terminal cost functions instead of the Lipschitz contractive condition on a partially ordered metric space. [ABSTRACT FROM AUTHOR]

Motivated by rising concerns regarding global warming and traffic congestion effects, we study the time-dependent green vehicle routing problem with time windows (TDGVRPTW), aiming to minimize carbon emissions. The TDGVRPTW is a variant of the time-dependent vehicle routing problem (TDVRP) in which, in addition to the time window constraints, the minimization of carbon emissions requires determination of the optimal departure times for vehicles, from both the depot and customer location(s). Accordingly, the first exact method based on a branch-cut-and-price (BCP) algorithm is proposed for solving the TDGVRPTW. We introduce the notation of a time-dependent (TD) arc and describe how to identify the nondominated TD arcs in terms of arc departure times. In this way, we reduce infinitely many TD arcs to a finite set of nondominated TD arcs. We design a state-of-the-art BCP algorithm for the TDGVRPTW with labeling and limited memory subset row cuts, together with effective dominance rules for eliminating dominated TD arcs. The exact method is tested on a set of test instances derived from benchmark instances proposed in the literature. The results show the effectiveness of the proposed exact method in solving TDGVRPTW instances involving up to 100 customers. Summary of Contribution: Due to the environmental situation, green vehicle routing problems (GVRPs) aim to consider greenhouse gas emissions reduction, while routing the vehicles, and play a key role in transportation and logistics. Vehicle greenhouse gas emissions strongly depend on the vehicle speeds and traffic conditions which in real life vary continuously over time. To tackle these challenges, we address the time-dependent green vehicle routing problem with time windows (TDGVRPTW) aimed at reducing total carbon emissions under time-dependent travel times and time window constraints. We design an effective exact method for the TDGVRPTW based on a state-of-the-art branch-cut-and-price algorithm. The paper is both of methodological value for researchers and of interest for practitioners. For researchers, the presented algorithm is amenable for various routing constraints and provides a ground for further studies and research. For practitioners, the paper suggests insights on how the carbon emissions change based on different vehicle speed profiles. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research is supported by the National Natural Science Foundation of China [Grants 71831003, 71831006, 72171043, and 71901180] and the Fundamental Research Funds for the Central Universities [Grants N170405005 and N180704015]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/ijoc.2022.1195. [ABSTRACT FROM AUTHOR]

Abstract: Quality function deployment (QFD) is a planning and problem-solving methodology that is widely used for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Acquiring ECs in product planning house of quality (PPHOQ) is a crucial step of QFD methodology. In previous studies, methods for acquiring ECs are qualitative and subjective. In this paper, a novel quantitative methodology for acquiring ECs in PPHOQ is presented. In the proposed methodology, a hierarchical framework for generating the refined set of EC candidates from the EC candidates obtained by brainstorming is established. A concept of a general relationship between a CR and an EC is introduced, and then a novel approach for identifying the general relationship measures with the help of intelligent characteristics of rough set is proposed. Another concept of the intension threshold of the general relationship corresponding to a CR is introduced, and then a set-operation-based method of acquiring ECs used in PPHOQ is presented. An example of a two-cylinder washing machine is used to demonstrate the proposed methodology. [Copyright &y& Elsevier]

Abstract: Owing to the typical vagueness or imprecision of customer requirements (CRs) in product planning house of quality (PPHOQ), determining the final importance of CRs is very difficult. Combining rough set theory, Kano’s model, analytical hierarchy process (AHP), and scale method, an integrated method is proposed to obtain the final importance of CRs in PPHOQ. Firstly, by using relative reduction and relative core in rough set theory, a decision system is built to acquire CRs in PPHOQ. Secondly, based on relative positive field in rough set, the decision system is simplified and its corresponding new decision system is established to determine the fundamental importance ratings of CRs. Thirdly, by integrating scale method into AHP approach, calculating formulas of the importance rating of achieving the improvement ratio of satisfaction estimation of a CR are developed. Next, for every CR, based on a combination of its fundamental importance rating, the importance rating of achieving the improvement ratio of its satisfaction estimation, and “its sales point”, its final importance rating is determined. Finally, a case study is provided to illustrate the effectiveness of the presented method. [Copyright &y& Elsevier]

The integrated energy system (IES) has been regarded as a promising energy supply solution due to its high efficiency and low emissions. However, various IES composition schemes exhibit different performance, and decision-makers or stakeholders have experienced difficulty in selecting the most appropriate alternative. This paper aims to develop a multi-criteria decision-making method for a selection problem involving integrated energy system composition schemes in Zhejiang Province, China. As existing research has neglected the interaction between criteria and ignored the subjective risk attitude, this paper proposes a new method based on modified fuzzy integral and prospect theory. First, the criteria's weights are calculated by the Mahalanobis-Taguchi Gram-Schmidt method; the λ fuzzy measures of these criteria are then calculated based on the φs transformation function and weights. This approach not only considers both subjective and objective information, but also eliminates overlapping information between criteria. Second, based on the grey correlation degree and the prospect theory, a prospect decision matrix is obtained that considers decision-makers' subjective risk attitudes. Finally, the alternatives are scored using a fuzzy integral. The results reveal that a complimentary solar and wind based system is the preferred scheme in project construction. Further, comparison and sensitivity analyses verify the proposed model's validity and feasibility, and some suggestions are given to further develop the IES. • Different integrated energy systems exhibit different performance results. • Multi-criteria decision-making is used in selecting integrated energy systems. • The case study involves an industrial park in Zhejiang Province, China. • The Mahalanobis-Taguchi Gram-Schmidt method is used to calculate criterion weights. • This approach considers subjective and objective information without overlaps. [ABSTRACT FROM AUTHOR]

A class of linear impulsive fuzzy dynamic equations on time scales is considered by using the generalized differentiability concept on time scales. Some novel criteria and general forms of solutions are established for such models whose significance lies in proposing the possibility to get unifying forms of solutions for discrete and continuous dynamical systems under uncertainty and to unify corresponding problems in the framework of fuzzy dynamic equations on time scales. Finally, some examples show the applicability of our results. [ABSTRACT FROM AUTHOR]

• A new industry-oriented physical safety evaluation index system was created for wearable exoskeleton human-robot collaboration using literature analysis, group decision-making, and the Delphi method. • The new index system improved the comprehensiveness of the existing evaluation index system for wearable exoskeleton human-robot collaboration. An industrial exoskeleton combination safety evaluation model was established to address both the physical safety of industrial exoskeleton human-robot collaboration and the perceived safety of human operators. The new model made the industrial exoskeleton evaluation more comprehensive and systematic, and it has the potential to be applied across different human-robot collaboration scenarios. Wearable exoskeletons are a type of collaborative robots that are widely used in human–robot collaboration, and their safety in the collaboration process has received significant attention. An industrial exoskeleton was the research object of this study. First, the influencing factors found in the relevant literature were analyzed and summarized. Subsequently, a group decision-making method was used to formulate preliminarily evaluation indicators. These indicators were then evaluated using the Delphi method. Based on the finalized evaluation indices, a physical safety evaluation index system for industrial exoskeleton human–robot collaboration was constructed, and an evaluation index model was established to evaluate the physical safety. Simultaneously, a questionnaire on the perceived safety was used to evaluate the perceptions of human operators on the safety of industrial exoskeleton human–robot collaboration. These two types of safety, the physical and perceived safety, were combined in an industrial exoskeleton human–robot collaboration safety evaluation. Three different industrial exoskeletons were used in the case study for this research. Their safety was evaluated using the model proposed in this study, which enabled human operators to quickly and effectively select an industrial exoskeleton through comparative analysis. [ABSTRACT FROM AUTHOR]

Abstract In (Ju et al., 2018) Ju et al. investigate the responsiveness of affirmative action in school choice. For the minority reserves type affirmative action they introduce a new matching mechanism and claim that their mechanism is responsive to the minority reserves policy (Theorem 1 in Ju et al., 2018). However, we construct a counterexample, which shows that it is not the case. Highlights • This note investigates the responsiveness of the minority reserves type affirmative action in school choice. • We provide a counterexample which shows that a mechanism in Ju et al. (2018) is not responsive to the minority reserves type affirmative action. • For the responsiveness of Dogan's mechanism, it is crucial to iteratively remove only the last-step minority interrupters. [ABSTRACT FROM AUTHOR]

The ridesourcing service has taken a large portion of the taxi market in the past few years. Many studies have explored the influencing factors of traditional taxi and ridesourcing demand in different areas of the city. Few studies investigated the market share of ridesourcing in different regions of the city. Understanding the spatial distribution of the market share of ridesourcing service and the factors that determine this distribution could help government agencies understand the role that each type of service plays in the transportation system and evaluate the effect of different public policies on the two types of services. This paper studies this topic by constructing a panel fractional regression model based on the taxi trip data of New York City in 2017. Results show that the market share of the traditional taxi is declining while that of ridesourcing service is increasing. The market share of ridesourcing services is higher in remote areas, areas with low population density, low density of transportation facilities, low household income, and high proportion of young residents. It indicates that ridesourcing service could provide more equitable service by better serving the underserved areas while traditional taxi caters more to the high-demand areas. • We explore built environment factors influencing market share of ridesourcing (RS). • A panel fractional regression model is used to analyze taxi trip data of NYC. • Remote areas, high ratio of low income and young residents are positively related. • Density of population, bus stop, road, and mixed land use are negatively related. • Results could help us understand the interaction of RS and traditional taxi. [ABSTRACT FROM AUTHOR]

Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network. [ABSTRACT FROM PUBLISHER]