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With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers' physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach. [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]

Government intervention plays an indispensable role in the development of the elderly care service supply chain (ECSSC). However, no research has focused on the optimal decision-making of the ECSSC considering government intervention under different channel power structures. This study establishes a three-stage ECSSC model, including the government sector, elderly care service provider and elderly care service integrator, while considering different government intervention measures and channel power structures. The service-level elasticity coefficient is defined as an uncertain variable, that can affect service demand and ESI service costs. This study explores the optimal decisions and market performance of the government and ECSSC under different channel power structures and government intervention, providing guidance for the ECSSC development and government decision-making. The results show that, first, government intervention is beneficial for the ECSSC, customers, and social welfare. Compared with the subsidy and service-level regulation scenario, only the former is beneficial for the supply chain, social welfare, and unit customer expenditure, but at the cost of lower service level and higher government expenditure. Second, different channel power structures do not affect service level, social welfare, and unit customer expenditure. However, the ESI-dominated structure favors ECSSC development. Third, the setting of the reward-penalty factor should not be too high, as, in this case, supply chain members, customers, and social welfare will benefit from the increased expected value of service level elasticity. At last, managerial insights are presented. [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]

Consumer choice behaviour is important in the product line optimisation problem. The extant literature on product line optimisation is mostly based on traditional consumer choice models, such as the multinomial logit and multinomial probit models. These models either assume that the utility errors are independent from irrelevant alternatives (IIA) or are limited by complex calculation processes or pre-given, specific distributions of measuring errors. The marginal moment model (MMM), which is classified as a semiparametric choice model, does not require specific distributions of errors; thus, it can overcome the IIA shortcoming. This study focuses on the concavity of the profit functions of a product line optimisation model based on MMM. We prove that the profit function based on MMM is concave in market share under a monopoly or oligopoly. Numerical experiments show that the choice probabilities obtained from the MMM, multinomial logit, and multinomial probit models are similar although they are obtained under different assumptions. Experimental results under monopolistic, Cournot, and Bertrand competition based on MMM are compared. Some interesting managerial insights are summarised based on the sensitivity analysis of the various model parameters. [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]

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]

We consider a make-to-stock system, where a supplier produces a single product to meet a single customer's order. The order quantity is fixed, but the order time is random. Before the order becomes due, it goes through multiple stages, and the transition time from one stage to the next is random. The customer provides advance demand information (ADI) by updating the order status information to the supplier when the order progresses to a key stage. We study the supplier's optimal production policy. By formulating this problem as a Markov decision process, we show that the optimal production policy is a state-dependent base-stock policy, and the optimal base-stock level after receiving the update is higher than before. We extend the model to the case with multiple updates. We numerically examine the configuration of information updating schemes, i.e. the timing and number of updates. We find that partial ADI with one update can capture most of the benefits of full ADI in most cases. However, the timing of update is crucial for the benefit of ADI. For a few cases where only one update is less effective, increasing the number of updates slightly can capture most of the benefits of full ADI. [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]

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]

Incorporating consumer choice behavior into a product line design optimization model enhances the understanding of consumer choices and improves the opportunities to increase profit. Most product line optimization problems assume that parameters are precisely known in consumer choice model. However, the decision maker does not precisely know the model parameters because of insufficient sample data, measurement problems, and other factors. We investigate the problem of establishing robust product line pricing under a multinomial logit model to account for the uncertainty of the valuation parameter. First, we present a nominal product line model to maximize profit. We then establish a robust product line model to maximize the worst-case expected profit, where the valuation parameter lies in an uncertainty set. We consider both single and multiple products development and derive the optimal prices' closed-form expressions. Through numerical experiments, we illustrate the benefit of robust product line pricing to address parameter uncertainty. We demonstrate that the difference between the expected nominal profit and the worst-case profit increases with the increase of the interval of the uncertainty set, and the robust profit relative to the worst-case nominal profit improves. The robust product line design can ensure steadier, even higher profit. [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]

In practice, before purchasing products, customers would compare the current price with past observed prices (referred to as the intertemporal reference price effect) and retail prices of other sellers (referred to as the horizontal reference price effect). This study considers a two-period supply chain with one manufacturer who sells a product through two competing retailers. We assume that the manufacturer acts as the Stackelberg game leader, and both retailers act as followers. The manufacturer can quote the same or different wholesale prices for the two retailers, whereas both retailers can employ a pre-announced or responsive pricing strategy. We study the impact of intertemporal and horizontal reference price effects on the selection of pricing formats. Our results show that both retailers and manufacturers can benefit from intertemporal effects. However, the horizontal effect hurts the retailer's profit, but still improves that of the manufacturer. In equilibrium, the manufacturer sets different wholesale prices to retailers who would employ the pre-announced pricing strategy. Finally, we find that the intertemporal reference price effect plays a key role in the pricing strategy equilibrium of the manufacturer and retailers. Our results complement the dynamic pricing strategy literature and provide new managerial implications for supply chain management. Pricing format selection problems with IRPE and HRPE are investigated. IRPE hurts the payoff of both retailers but improves the manufacturer. In equilibrium, the manufacturer prefers the strategy "DP". IRPE plays an important role in pricing format selecting than HRPE. [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]

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]

Selection of appropriate suppliers is an important decision task for enterprises. In literature papers, supplier selection is only considered from the view of components or parts, and evaluation of the overall outsourcing cost and supply risk is not considered from the view of product as a whole. In addition, the traditional process of supplier selection usually happens in the stage of production after the product design has been finished. In fact, suppliers of product components can be pre-selected in the product design stage, which can avoid supplier shortage or high component-bidding cost by modifying the product design scheme. In this research, a new supplier pre-selection method for platform-based products is proposed to obtain the minimal overall outsourcing cost and supply risk probability from the perspective of whole product, help engineers evaluate and improve product design scheme in early time of product design and reduce the probability of design change at the stage of product production. Analytic hierarchy process and reliability matrix are applied to evaluate the supply risk of candidate suppliers and non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the established optimisation model. A case study is provided to illustrate the effectiveness of the proposed optimisation model and algorithm. [ABSTRACT FROM PUBLISHER]

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]

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]

Although research in flow shop production scheduling has been carried out for many decades, there is still a gap between theoretical research and industrial applications - especially in some manufacturing paradigms in which adaptive production control is necessary, such as in one-of-a-kind production (OKP). This paper presents an average processing time and lever (APT-LVR) heuristic, integrated with a closed-loop feedback control scheme, to achieve adaptive production scheduling and control in flow shop production. Having been implemented in a local OKP company, our APT-LVR based production scheduling and control system demonstrates a significant potential to improve the company's production efficiency and competitiveness. [ABSTRACT FROM AUTHOR]

In a one-of-a-kind production (OKP) company, the operation routing and processing time of an order are usually different from the others due to high customisation. As a result, an OKP company needs to dynamically adjust the production resources to keep the production lines reconfigurable. Through a proper assignment of operators in different sections of a production line, bottlenecks and operator re-allocation during production can be reduced effectively. In this paper, a mathematical model is introduced for optimal operator allocation planning on a reconfigurable production line in OKP. The optimisation objectives are to minimise the total number of the operators, total job earliness and tardiness, and the average work-in-process storage. A branch-and-bound algorithm with efficient pruning strategies is developed to solve this problem. The proposed model and the algorithm are empirically validated by using the data of a windows and doors manufacturing company. A software system based on the proposed approach has been implemented in the company. [ABSTRACT FROM AUTHOR]

This paper investigates the product-positioning problem for a monopolist developing of a new product. The majority of the literature on product positioning assumes that the consumer choice model and parameters are both precisely known. In reality, however, the marketer may be uncertain about the model structure or the parameters due to incomplete information, small sample data size, etc. In this paper, we investigate a robust product-positioning problem based on the multinomial logit model to account for parameter uncertainty. We first establish a nominal product-positioning model, in which valuation parameters are assumed to be known. Then, we investigate a static robust product-positioning problem with the multinomial logit model to obtain the maximin revenue. We also propose a two-period dynamic robust model. For each setting, we consider both homogeneous and heterogeneous customers' preferences. Finally, numerical experiments are presented to compare the nominal model with the static robust model, and to compare the static robust model with the dynamic robust model. The experiments show that when facing uncertain parameters, the robust model obtains more stable revenue than the nominal model; the dynamic robust model obtains higher revenue than the static robust model. [ABSTRACT FROM AUTHOR]

The complex network effect of the product exhibits has a significant impact on the product line optimization design. The multinomial logit (MNL) model which is used to simulate consumer choice behavior is applied in most of product line optimization problems. However, its assumptions, independence of irrelevant alternatives (IIA) and the same Gumbel distribution of random error terms, are usually difficult to be met in practice. The marginal moment model (MMM) can be used when the mean and variance of consumer's utility error are known. The MMM not only has weak assumption conditions but also overcomes the IIA problem of MNL model. In this paper, we study the product pricing problem based on MMM with endogenous negative network effect. Firstly, we construct a variant of MMM considering network effect in product line optimization design. Secondly, we prove that the revenue function is concave in market share. We propose the solving methods of the model to obtain the optimal price, the corresponding market share, and the revenue under three different scenarios, i.e., developing single product, homogeneous products, and heterogeneous products. Finally, numerical experiments show that the proposed model can better simulate consumer choice behavior and potentially increase revenue. [ABSTRACT FROM AUTHOR]

Abstract: Recently, manufacturers have increasingly begun to sell products bundled with core services. However, customers are inconvenienced by individually returning the product or refunding the service after they have purchased a bundle. This paper presents a return and refund policy under two‐stage demand uncertainty (uncertainty at time 0 and inherent uncertainty), for products bundled with core services in the dual‐channel supply chain. Consumers can use the direct channel to return the default product–service bundle. In contrast, consumers in the retail channel can unsubscribe from the retailer's diverse core service by first receiving an unused service refund; the retailer then returns the product to the manufacturer. We use an approximate algorithm to solve for the optimal product quantity in the retail channel, demonstrating that the proportional marginal profit has a greater positive influence on the retailer's profit, and especially for the demand with a higher mean and lower variance. [ABSTRACT FROM AUTHOR]

This paper proposes a general model for the bus route schedule design (BRSD) problem considering the uncertainty in the bus travel times to minimize the expected total schedule deviation from a reliability standpoint. The preferences of bus operators are considered in the objective function. Based on the general model, four stochastic linear programming models are subsequently developed by successively incorporating different operation strategies; they include (1) no control strategy (NCS), (2) bus drivers' schedule recovery (DSR), (3) holding control strategy (HCS), and (4) both DSR and HCS (BRHS). Then, a Monte Carlo simulation based solution method is designed to solve these four models, respectively. Finally, numerical tests based on a real bus route are used for a comparative analysis of different operation strategies used in the BRSD. The results show that HCS and BRHS outperform other operation strategies in terms of the schedule deviation of the buses. Interestingly, HCS performs slightly better than BRHS when bus operators assume that later arrival at scheduled point is more harmful than earlier arrival. Compared to NCS, HCS can save objective function approximately 67.9% in all cases. In addition, operation strategies that could better fit a particular scenario are suggested. [ABSTRACT FROM AUTHOR]

Product family design optimization is an important decision task in the early stages of product development. The extant optimization models for product family design assume that the known information for modeling can be determined precisely. However, many collected information for product design are prone to be imprecise due to the inherent uncertainty of human knowledge and expression. For example, when human experts estimate market demand of a product, imprecise information may be involved and influence the results of optimization. In this research, an optimization model with maximizing imprecise part-worth utility function is established for product family design problem. A linear programming embedded genetic algorithm is proposed to solve the proposed fuzzy optimization model. An industrial case of printing calculator product is used to illustrate the proposed approach. Experiments and sensitivity analysis based on the case study are also performed to analyze the relationship among the parameters and to explore the characteristics of the optimization model. [ABSTRACT FROM PUBLISHER]