Repair scheduling and routing and logistics support are interdependent and critical for post-disaster distribution system restoration (PDSR), which is also influenced by the geographical characteristics of outage area. Hence, we develop a co-optimization model for the PDSR with logistics support and geographical characteristics. A hybrid improved bacterial colony chemotaxis algorithm is proposed to solve the model, in which A* algorithm is employed to route repair crews and material delivery in the transportation network considering geographical characteristics, and an improved bacterial colony chemotaxis algorithm is proposed to determine the repair scheduling and material allocation in the distribution system. Different scale of distribution system instances with different damage levels and different geographical characteristics are used to demonstrate the effectiveness of the proposed methodology.

Device-free localization (DFL) is a technology which helps locate the target without the need of equipping the target with a radio device. It uses the target-induced change in the received signal strength (RSS) of the wireless links to locate the target. It is useful for the applications in security monitoring and emergency rescue. However, it is difficult for the existing RSS-based DFL algorithms to acquire a high localization accuracy and a low computational complexity simultaneously. To address this issue, a novel hierarchic radio imaging (HRI)-based DFL algorithm is proposed in this article. The algorithm groups the wireless links according to the length of links and assigns a priority to each link group. Furthermore, a grid normalized model is proposed to calculate the grid values, and then a square human model is used to locate the target. In addition, in order to reduce the computational complexity, the location information of the target at the previous moment is used to limit the regional range of the target location. Extensive experiments are conducted with results showing that the HRI algorithm not only greatly reduces the computational complexity but also achieves high localization accuracy and high localization stability. Therefore, the HRI algorithm is very suitable for the real-time-based applications.

The logistics industry faces high risk as demands for logistics services are stochastic. Facing the market demand volatility, individual logistics service providers (called “agents”) who are individual decision makers have their own preferences with respect to their profit targets. It is commonly observed that some agents are more risk taking (and hence they set very ambitious profit targets) whereas some are more risk averse. This creates a situation in which some agents over-reserve logistics capacities but some under-reserve. In the sharing economy, the logistics capacities can be balanced out and shared via platforms. In this article, we analytically build newsvendor problem-based optimization models to explore the value of a capacity-balancing-platform, in the presence of multiple agents. We propose two capacity-balancing mechanisms (called Rules 1 and 2) for the platform: 1) Rule 1 is anequal balancing rulein which all the capacities of agents will be collected and evenly distributed to all agents and 2) Rule 2 is asurplus balancing rulein which all capacities of agents will be collected and classified, and balancing is done with respect to surplus in capacity reservation. We then compare Rule 1 and Rule 2 with the original system when the platform is absent (Rule 0). We analytically prove that Rule 2 always outperforms Rule 0 with respect to the total systems expected profit. For the homogeneous case, Rule 1 is the optimal allocation rule and outperforms (with respect to the total systems expected profit) the more complex rule, i.e., Rule 2, which interestingly shows that “simplicity is better.” To generate more insights and to check the robustness of findings, we extend the analyses to cover more cases.

Many consumer products, e.g., printer’s cartridges, milk’s glass bottles, etc., are sold with reusable parts, which can be formally collected under a used-part-collection (UPC) program. In today’s markets with high volatility, risk is present for UPC operations. In this article, building via standard consumer utility-based models, we uncover the value of the UPC program in a production system. In the basic model, we consider the case when the manufacturer is risk averse. For both the cases with and without UPC, the optimal product pricing and quality decisions are determined and the respective manufacturer’s utility and consumer surplus are derived. The values of UPC to the manufacturer and consumers are found. We find that: 1) both quality improvement and UPC can be beneficial to consumers under some situations and 2) the optimal quality level, the optimal utility, and the consumer surplus are independent of the return fee. In the extended models, we explore two cases, namely, the case when consumers are risk averse, and the case with a collection platform, which can enhance the collection rate. We find that no matter what the level of consumers’ risk averse is, it reduces consumers’ benefits. Moreover, a higher platform’s power is beneficial to the manufacturer and consumers but may reduce the platform’s profit.

Today, many mobile apps (MAs) act as an efficient channel for e-tailers to sell products. In addition to selling, some of these apps even include e-payment functions. This creates e-payment convenience but also a concern on privacy. In this article, we consider a supply chain with an e-tailer which sells through such an MA and an online website. The two channels influence the demand of one another. In the basic model, the e-tailer decides the promotion efforts in both channels. We interestingly find that the optimal promotion efforts are independent of e-payment convenience and privacy concern. We analytically establish that profit-sharing contract and two-part-tariff contract can coordinate the dual channel supply chain system but not revenue-sharing contract or sales rebate contract. In the extended models, we first consider the case when the e-tailer makes product pricing as well as promotion efforts decisions together. We analytically derive the optimal solution and find that the optimal pricing and promotion efforts depend on e-payment convenience and privacy concern. We further extend the analyses to the case when the e-tailer is risk averse and uncover that: 1) when the retail selling price is exogenously given, the optimal promotion efforts between the risk-neutral and risk-averse e-tailers are the same and 2) when the retail selling price is endogenous, the corresponding optimal retail selling prices and promotion efforts between the risk-neutral and risk-averse e-tailers are different. In particular, a higher degree of risk aversion will lead to higher (lower) promotion efforts and a higher (lower) retail selling price if demand volatility is sufficiently large (small).

In supply chain systems, with the rapid growth of electronic commerce, a lot of firms are puzzled with the issue about how to best manage the distribution channels. In this article, we study the online channel management, pricing and coordination (i.e., optimization) in supply chains in which both channel members have an opportunity to introduce an online channel for retailing products to consumers. We focus on examining the roles of “channel leadership,” online channel decision and customer channel preference. Through systematic analysis and comparisons, we obtain some important insights, such as: 1) consumers do not care about who the channel leader is, but care about who establishes the online channel; 2) the dual-channel structure can make the manufacturer charge a “higher wholesale price” regardless of who establishes the online channel and who the channel leader is; and 3) no matter who the channel leader is, the dual-channel structure hurts the consumers’ benefit when the retailer establishes the online channel. We also illustrate how the proposed dual-channel supply chain can be optimized with the use of supply chain contracts.

Nowadays, governments all around the world have implemented rules and launched legislation to enhance environmental sustainability. Supply chain systems are hence operated under different forms of legislation, such as the carbon tax or extended producer responsibility tax. In this paper, we examine the newsvendor model-based supply chain systems with the consideration of all common forms of environmental taxes. We highlight how the retailer's risk attitude and the number of consumer returns affect: 1) supply chain operations; 2) performances of the environmental taxes; and 3) supply chain coordination (i.e., optimization). To be specific, we derive the optimal inventory decisions of the retailer when she is risk neutral and risk averse, respectively. We then uncover how environmental taxes and consumer returns affect the retailer's optimal inventory decisions under different risk attitudes. We further explore the supply chain coordination challenge under three different contracts, characterize their flexibility in coordinating the channel and discuss the impacts of the environmental taxes and consumer returns on each coordination mechanism. Our analytical results show that the two-part tariff contract can achieve coordination for the case with a risk neutral retailer only, while markdown sponsor (MDS) contract and revenue-sharing policy (RSP) can achieve coordination for both risk neutral or risk averse retailer cases. Besides, we reveal that the examined contracts can coordinate the supply chain with a risk neutral retailer more flexibly than that with a risk averse retailer. Finally, by comparing between the MDS contract and RSP, we find that the environmental taxes and the consumer returns will affect the coordination mechanism differently toward the risk neutral and risk averse retailers. Impacts brought by the consumer returns are also explored.

Logistics and supply chain systems (SCSs) are an important part of industrial systems. In order to establish an optimal logistics and supply chain system (SCS), the systems approach which focuses on the “whole picture” and achieving “global optimality” should be adopted. In this special issue, we collect and feature papers which explore different problems in logistics and SCSs. Important approaches, such as game theory, simulation, metaheuristics, robust control, fuzzy sets, and multiobjective optimization, are found to be employed in exploring problems in logistics and SCSs. We conclude this article by discussing “what is next” for research in logistics and SCSs engineering in the Industry 4.0 era and beyond.

In the sharing economy system, consumer-to-consumer product-exchange (C2C-PE) for digital products is commonly seen. In this paper, we analytically explore the C2C-PE problem for digital products. To be specific, we consider the presence of a digital product developer (DPD) who develops and sells a product to consumers in the market. Consumers possess random valuation toward the digital product and the DPD needs to decide the optimal selling price for the product. We study the impacts brought by C2C-PE. We try to uncover whether (and when) DPDs and consumers will be benefited by the presence of C2C-PE. In the basic model, when all parties are risk neutral, we find that the presence of C2C-PE is always beneficial to the DPD and consumers. To show the robustness of results in the basic model, we further extend the analysis to cover a few cases and prove that this conclusion holds irrespective of the DPD's risk averse attitude, the consumers' risk averse attitude, as well as whether the utility gained from C2C-PE is effort dependent or not. Hence, we conclude that C2C-PE, which seems to be harmful to DPDs, is in fact a beneficial scheme to both the DPDs and consumers.

Mass customization (MC) programs are commonly seen in the real world. Many companies offer consumer returns and quick delivery for their MC products to the market. Thus, the quick response (QR) strategy is crucial for MC supply chains. Prior studies in related areas only examine MC supply chains with limited features and topics, such as measures for win-win coordination or profit risk analysis, but without a comprehensive consideration of these aspects together. Besides, the systems enhancement schemes are not yet explored. This paper aims to fill these gaps and generate novel insights. Among the findings, we show that a higher salvage value of the consumer returned items and a higher salvage value of the unused inventories both can reduce the value of QR for the supplier. We illustrate how a simple two-part tariff contract and a sophisticated hybrid contract can achieve win-win coordination with different levels of flexibility. We also conduct a profit risk analysis on the MC supply chain. Finally, we demonstrate how three practical measures, namely technology investment, product design improvement, standardization of component, can further improve the MC supply chain system's performance.

The proper supply of end-of-life (EOL) items plays a critical role in the success of remanufacturing systems. In many real-world practices, a remanufacturer has multiple links with independent third party collectors, while these links should be simultaneously coordinated. However, most previous studies mainly focus on reverse supply chain (RSC) coordination in a one-to-one setting (single link). Moreover, the competition among third party collectors affects the acquisition of EOL products and this is an important issue. In this paper, an RSC consisting of a single remanufacturer and duopolistic competing third party collectors is explored and a new mechanism is proposed to achieve channel coordination across multiple links. To be specific, we consider the case when the collectors compete on the acquisition prices offered to the consumers and the remanufacturer decides on the pricing and environmental effort decisions with two considerations: 1) increasing return of EOL items and 2) boosting demand of remanufactured products. The optimal decisions of RSC members and performance of RSC are analyzed under different behaviors of competing collectors. Moreover, a multilateral two-part tariff contract is proposed to coordinate the decisions across multiple links. The results indicate that the proposed contract improves not only profits of the remanufacturer and competing collectors but also the collection quantity of EOL items and demand of remanufactured products, which helps improve the environment.

The cumulative capacitated vehicle routing problem (CCVRP) focuses on minimizing the sum of arrival times at the customers. An important application of the CCVRP arises in the procurement of humanitarian aid when natural disasters occur. In this article, the multidepot CCVRP (MDCCVRP) is investigated, and its mathematical model is developed. Moreover, an effective perturb-based local search (PLS) algorithm is proposed to solve the problem. The proposed PLS algorithm starts from having a feasible solution. Regarding the PLS, the perturbing operators help to explore the searching space, while the six local search operators help to exploit the best solution within each searching basin. To test the performance, the proposed PLS is applied to a set of standard benchmark instances and compared with the recently published algorithms. Extensive computational studies reveal that the proposed PLS algorithm is able to attain better solutions with less computational time for most testing instances, and thus competes very favorably with the previously proposed approaches. A stability analysis is also presented to shed light on the robust behavior of the proposed algorithm. The results of the two-sided Wilcoxon rank sum tests clearly show that the algorithms (our PLS and other published methods) have performed with statistically significant differences.

This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimization of raw material costs. We formulate a bi-objective nonlinear 0-1 integer programming model for it. To solve the bi-objective problem which is NP-hard, we propose an evolutionary discrete particle swarm optimization algorithm (EDPSO) with a hybrid-greedy method (for generating the initial population), a new particle updating strategy, and an SPT-local search method (which has been proven for improving solutions' quality theoretically and practically), and the Pareto archive updating strategy for storing good solutions. Computational experiments verify the effectiveness of EDPSO and show that it can obtain better solutions compared to other competing algorithms based on four important performance metrics.

Air-cargo transportation has become increasingly important for global logistics systems nowadays. However, due to the intensive market competition and diverse uncertainties arising from demand and operating costs, the pricing decisions for air-cargo carriers are extremely challenging but underexplored. Besides, many airlines are holding risk-averse attitudes in decision making in order to survive in the highly volatile and competitive market. Therefore, in this paper, we apply the mean-variance theory to characterize the risk-averse behaviors of decision makers, and analytically derive the equilibrium prices for two competing risk-averse air-cargo carriers under demand and cost uncertainties. Then, we uncover how the crucial factors, like risk sensitivity coefficients, market competition, market share, demand uncertainty, and cost uncertainty, affect the carriers' optimal prices. In addition, important cost thresholds and relative risk-averse attitude thresholds are identified. Our analytical results demonstrate the symmetry in the optimal prices and critical thresholds for the two carriers. Besides, we reveal the importance to consider both carrier's own and the competitor's risk attitudes and operating characteristics in decision making when market competition exists. Moreover, we reveal the direct and indirect impacts of risk attitudes on the optimal prices, thus highlighting the importance to integrate risk considerations into the optimal pricing decision framework. Finally, we show that market situations play a critical role in characterizing the effects of diverse parameters on the equilibrium prices, which should be carefully evaluated by decision makers in air-cargo carriers.

With the rapid development of modern industries, more and more risk factors exist in industrial operations, leading to a gradual increase in the frequency of industrial failures. The industrial enterprises have taken a variety of measures to analyze the risk factors in industrial operations, but establishing an effective monitoring and evaluation system remains a technical challenge. To overcome this challenge, a practical industrial risk monitoring system, based on the wireless sensor network (WSN), is established in this paper to improve resilience of the respective industrial operations. In the proposed system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically as the anchor nodes of the localization system, and two grid cumulative probability localization (GCPL) algorithms are proposed based on the GCPL-circle intersection and GCPL-path loss methods, respectively. The GCPL algorithm determines the grid cumulative probability using prior location information of the target node and the received signal strength information between the anchor nodes and target node to locate the target node. Experimental results show that the two GCPL algorithms significantly improve the localization accuracy and stability, and hence can be implemented as a risk monitoring system for realworld applications.

This paper explores a dual-channel closed-loop supply chain (CLSC) system consisting of a manufacturer and a retailer. The manufacturer is the CLSC Stackelberg leader. We investigate the effect of exerting marketing effort on the optimal decisions and profits of supply chain members by considering several marketing effort supported models, namely the models when the manufacturer is the investor, the retailer is the investor, and the centralized CLSC system, respectively. We propose a two-part tariff contract for coordinating the CLSC. We analytically reveal that the two-part tariff contract can coordinate the CLSC when the manufacturer is the investor but unable to coordinate supply chain when the retailer is the investor. Finally, we present the numerical analysis to uncover insights on how the consumers' preference toward the direct channel affects the optimal decisions in the dual-channel CLSC system.

The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants' tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms.

Mobile platforms are prevalently used in the age of social media. They serve a two-sided market to connect users and app developers. In this system, members simultaneously sell products, publish advertisements (ads), and advertise. This paper aims to explore advertising strategies for the mobile platform incorporating their roles as sellers, ad publishers, and advertisers. Specifically, we develop a game-theoretical model which captures the relationship among the works of the platform owner and app developers in a dynamic setting. Our analysis shows that when the platform displays apps' ads, the owner may be better off to participate in apps' advertising under certain conditions, rather than always charging them aggressively as suggested by convention wisdom. Surprisingly, although the negative impacts evoked by multiple apps' entry deserve much attention, it is unnecessary for app developers to take into account when making advertising decisions. We further find that the coordinating bilateral participation in advertising is a new mechanism to improve the profitability. Unlike the result proposed in the previous literature, the mechanism here will cause free-riding among app developers when they participate in the platform's advertising. Furthermore, when accompanied with the revenue sharing policy, the relatively low participation rates could absolutely eliminate the system inefficiency.

"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Service quality is a critical element for fashion boutique operations. However, owing to limitation of resources, fashion boutiques usually cannot afford to establish a very formal service quality management system. Motivated by the importance of service management for fashion boutiques, this paper examines the respective service quality issues in two related parts: based on the revised retail service quality scale (RSQS) model, the first part conducts a service gap analysis via an empirical survey. It is found that the RSQS model is valid, the respective service gaps exist, and the problem-solving dimension has the largest service gap. Focusing on this most significant problem-solving service gap (PSSG) identified in the empirical analysis, the second part analytically studies the optimal PSSG decision for fashion boutiques and examines the impact of retail competition via a game theoretic analysis. The closed-form analytical findings reveal several insights, such as: 1) for both monopoly and homogeneous duopoly cases, the equilibrium demands depend solely on the profit margin, and the service gap enhancement efficiency and 2) the relative PSSG dependent demand sensitivity is critically important because it affects the equilibrium PSSG significantly.

Sustainability is a timely topic. A sustainable supply chain is one which aims to maximize the system's performance in three dimensions, namely environment, economics, and society. In the fashion industry, with the advance of big data related information technologies, achieving a sustainable supply chain is no longer a dream. In this paper, we first establish that a fashion supply chain is in fact a typical system of systems. Then, we examine several critical big data related technologies and applications which are related to sustainable fashion supply chain management. After that, we develop and present the design principles, from a system of systems perspective, for developing a sustainable fashion supply chain. Based on these principles, we further propose a novel five steps framework for achieving sustainable fashion supply chain management in the big data era. A sustainable fashion supply chain system of systems matrix is also constructed.

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.

The papers in this special section focus on Big Data, with particular emphasis on the exploitation of data collected via industrial sensor networks (be it wireless or not) for Internet-based industrial applications. The papers explore the handling and analysis of the collected data. It is expected that analyzing the data can improve the reliability of industrial systems by predicting the occurrence potential risks and then rectification can be made accordingly. Such risks are inevitably linked to uncertain factors hidden in the systems that can be revealed by the data analysis process.

As a novel evolutionary searching technique, ant colony optimization (ACO) has gained wide research attention and can be used as a tool for optimizing an array of mathematical functions. In transportation systems, when ACO is applied to solve the vehicle routing problem (VRP), the path of each ant is only “part” of a feasible solution. In other words, multiple ants' paths may constitute one feasible solution. Previous works mainly focus on the algorithm itself, such as revising the pheromone updating scheme and combining ACO with other optimization methods. However, this body of literature ignores the important procedure of constructing feasible solutions with those “parts”. To overcome this problem, this paper presents a novel ACO algorithm (called AMR) to solve the VRP. The proposed algorithm allows ants to go in and out the depots more than once until they have visited all customers, which simplifies the procedure of constructing feasible solutions. To further enhance AMR, we propose two extensions (AMR-SA and AMR-SA-II) by integrating AMR with other saving algorithms. The computational results for standard benchmark problems are reported and compared with those from other ACO methods. Experimental results indicate that the proposed algorithms outperform the existing ACO algorithms.

This paper explores supply chain systems coordination challenges in the presence of multiple heterogeneous risk sensitive retail buyers using the commonly seen markdown contract under both information symmetric and asymmetric settings. For each setting, we explore two scenarios. The first scenario allows the upstream manufacturer to freely set a separate contract to each risk averse retailer, whereas the second scenario specifies that the manufacturer has to grant the same contract to each risk averse retailer under the fair trade rule. We analytically show that the markdown contract which can achieve “perfect coordination” only exists in the first scenario (without the fair trade rule) under the information symmetric setting. For all the other scenarios, we find that perfect coordination cannot be achieved by the markdown contract, and hence we develop the computational algorithms to help identify the markdown contract parameter(s) which can achieve the “best possible coordination.” In addition, we reveal that the manufacturer's risk attitude does significantly affect the achievability of perfect coordination. The findings of this paper also provide analytical evidence to show that the fair trade rule would do more harm than good for supply chain systems optimization under simple supply contracts.

Radio frequency identification (RFID) is an important tool for enhancing the performance of inventory management. Motivated by our real-world observations in local hospitals, we study the case where a hospital, which is using a bar-coding system, considers switching to an RFID system because the RFID technology can potentially enhance the hospital's inventory management under the quick response system (QRS). In order to examine the value of RFID, we develop a formal analytical Bayesian model for the information updating process. We derive the expected value of information of the RFID system, and reveal the conditions in which the RFID system outperforms the bar-coding system. In addition, we evaluate the impacts of the QRS toward the expected profit and level of risk of the hospital, the supplier, and the whole supply chain (SC) system. We further propose two policies to help achieve SC coordination. Numerical analyses are reported and important insights are generated.

In this paper, we model and formulate the search-based advertising auction problem with multiple slots, choice behaviors of advertisers, and the popular generalized second-price mechanism. A Lagrangian-based method is then proposed for tackling this problem. We present an extension to the subgradient algorithm based on Lagrangian relaxation coupled with the column generation method in order to improve the dual multipliers and accelerate its convergence. Simulation results show that the proposed algorithm is efficient and it shows significant improvement compared to the greedy algorithm.

In this paper, we propose and explore a novel panel data-based particle-filter (PDPF) model to conduct fashion sales forecasting. We evaluate the performance of proposed model by using real data collected from the fashion industry. The experimental results indicate that the proposed panel data models outperform both the traditional statistical and intelligent methods, which provide strong evidence on the importance of employing the panel-data approach. Further analysis reveals that: 1) our proposed PDPF method yields a better forecasting result in item-based sales forecasting than in color-based sales forecasting; 2) a larger degree of Granger causality relationship between sales and price will imply a better sales forecasting result of the PDPF model; 3) increasing the amount of historical data does not necessarily improve forecasting accuracy; and 4) the PDPF method is suitable for conducting fashion sales forecasting with limited data. These findings provide novel insights on the use of panel data for conducting fashion sales forecasting.

In this paper, we analyze how carbon emissions affect the selection of transportation modes and social welfare by using a two-stage Stackelberg gaming model. Based on this model, the government's optimal carbon-emission tax scheme and the company's optimal transportation mode and production decisions are explored. We find that: 1) whether or not the transport carbon-emission tax can increase social welfare depends on the relationships among the social cost of carbon (SCC), the transportation mode shifting threshold (TMST), and the biggest carbon-emission tax that a company can afford (BCRA); 2) a greater SCC implies a higher probability of improving social welfare via imposing transportation carbon-emission tax; and 3) a smaller TMST or BCRA yields a higher probability of improving social welfare when a carbon-emission tax is imposed. Further study shows that imposing a carbon-emission tax on the product with a higher production cost, a bigger product volume, or a bigger product density can increase the probability of improving social welfare.

This paper is motivated by observed industrial practices. We conduct a mean variance (MV) analysis of a fast fashion supply chain with returns policy. Different from the conventional newsvendor type products, fast fashion brands plan to have stock-out because it is a feature of fast fashion and can bring some benefit. Based on the fast fashion features, we build an analytical MV optimization model for a two-echelon fast fashion supply chain to address the following research questions. 1) What are the differences and similarities in the structural properties between the supply chains that carry fast fashion products and conventional newsvendor type products? 2) How do we optimize a fast fashion supply chain with multiple retailers under the MV framework? 3) Can a simple returns policy optimize (and “coordinate”) such a multiretailer supply chain? 4) How do individual retailers' degrees of risk aversion affect the achievability of coordination? 5) Can the above simple contract help coordinate the supply chain under information asymmetry? We propose a novel approach called “negotiated space” in the analysis. We generate several important insights which include an interesting finding that a simple returns policy can be applied to coordinate the fast fashion supply chain even in the presence of multiple retailers.

Advance-selling (AS) is a commonly observed industrial practice in which a retailer allows consumers to prebook the fashionable product before the real selling season starts. Motivated by this practice, this paper studies AS strategy for a retailer who sells a newsvendor-type of fashionable product in light of potential consumer opportunistic returns. In our model, the consumers face valuation uncertainty and know their valuation realization only after product acquisition. There also exists aggregate demand uncertainty, captured in the conventional newsvendor model. All preorders are fulfilled at the beginning of a normal-selling season. We build analytical optimization models and consider three strategic options for the retailer, namely, no advance-selling allowed (NAP), advance-selling with full refund (AFP) and advance-selling with partial refund (APP), where there are two suboptions under APP. We derive the retailer's optimal pricing and refund policies for each option. By comparing the results in the above options, important insights are generated. Finally, we conduct a numerical analysis to further examine the impacts brought by consumers valuation, market condition, and consumers classification on the optimal strategy.

Motivated by the popular markdown money policy (MMP) in the textiles and clothing (TC) industry, in this paper, we explore how this policy performs in a two-stage TC/fashion supply chain with an upstream risk-averse manufacturer (supplier) and a downstream risk-neutral retailer. Specifically, we investigate both the optimal decisions of the risk-averse supplier with respect to the MMP contract parameters and the optimal ordering decision of the risk-neutral retailer so that the whole supply chain can be coordinated (i.e., optimized). We then conduct a numerical study with the real data from two companies to explore the performance of the optimal MMP proposed in our paper. Important insights and specific implications to the industry practitioners are discussed.

The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit.

In time-series forecasting, statistical methods and various newly emerged models, such as artificial neural network (ANN) and grey model (GM), are often used. No matter which forecasting method one would apply, it is always a huge challenge to make a sound forecasting decision under the condition of having very few historical data. Unfortunately, in fashion color trend forecasting, the availability of data is always very limited owing to the short selling season and life of products. This motivates us to examine different forecasting models for their performances in predicting color trend of fashionable product under the condition of having very few data. By employing real sales data from a fashion company, we examine various forecasting models, namely ANN, GM, Markov regime switching, and GM+ANN hybrid models, in the domain of color trend forecasting with a limited amount of historical data. Comparisons are made among these models. Insights on the appropriate choice of forecasting models are generated.

In this paper, we study the impacts of imposing a minimum order quantity (MOQ) on a two-echelon supply chain implementing quick response (QR) and consider the issue of coordination for such a system. By exploring the QR-MOQ supply chain system, we analytically prove that the retailer's expected profit (REP) is nonincreasing in the MOQ. We further find that the MOQ that maximizes the manufacturer's expected profit can significantly reduce the REP and the supply chain's efficiency. Understanding that the static nature of the preagreed MOQ hinders the information updating capability brought about by QR, which, in turn, decreases the supply chain's efficiency, we propose an innovative dynamic MOQ policy and derive the analytical conditions under which channel coordination with Pareto improvement is achieved.

Mass customization (MC) is a pertinent industrial practice. Different from the non-MC products, consumer return for MC products is typically prohibited. MC retailers can thus gain significant competitive advantages by offering a consumer return policy. By constructing an analytical model with both demand and return uncertainties, we study in this paper the optimal policy with three dimensional decisions on pricing, consumer return, and level of modularity under a mean-variance formulation. Structural properties of the model are revealed, and the closed-form solutions for the optimal decisions are derived. An extensive sensitivity analysis is subsequently conducted to explore how the risk sensitivity, demand uncertainty, return uncertainty, and other important parameters affect the optimal decisions and profits. A few counterintuitive findings are obtained, and important insights are generated.

In marketing, it is well known that social needs play an important role in the purchase of conspicuous products such as high-end luxury fashion labels. In this paper, we analytically study the optimal advertising and pricing decisions for luxury fashion brands in a market that consists of two consumer groups with contrasting social needs for fashion products, namely, the leader group (LG) and the follower group (FG). We consider a situation where the LG consumers have the desire to distinguish themselves from the FG consumers, whereas the FG consumers would like to assimilate themselves with the LG consumers. We first develop an original optimization model for this problem. We then explore the optimal solution scheme by separating the problem into tactic-based subproblems and conduct an extensive sensitivity analysis. Our analysis reveals that the optimal strategies follow different scenarios, and it can be optimal for a brand of conspicuous product to do the following: 1) Advertise to only one group while sell to the whole market; 2) advertise and sell to the FG only; and 3) advertise and sell to the LG only. Important insights are also reported.

RFID technology is an important tool in modern supply chain management. This paper analytically studies the use of RFID in a two-echelon single-manufacturer single-retailer supply chain with the vendor managed inventory (VMI) scheme. First, the supply chain models under a retail replenishment problem with and without RFID are constructed. Second, both the levels of risk and the expected profits of the supply chains are explored. Third, measures which can coordinate the supply chains with and without RFID are proposed. Fourth, comparisons between the cases with and without RFID are made. This paper analytically illustrates several important managerial insights which include: (i) when the RFID tag cost is very small, employing the RFID technology yields an improved supply chain with both larger expected profit and smaller risk; (ii) there exist multiple return policies which can coordinate the supply chain with RFID and the respective upper and lower bounds are identified; (iii) it is beneficial for the manufacturer to take the initiative to share the retailer's cost of RFID implementation, and this action not only can help coordinate the supply chain but also lower the manufacturer's risk; and (iv) compared to the case without RFID, the return rate under the coordinating return policy for the case with RFID can be lower if the RFID tag cost is appropriately shared between the retailer and the manufacturer. These insights are important for both industrialists and academicians.

We study in this correspondence paper a solution scheme which solves a periodic review multiperiod inventory problem under a mean-variance (MV) framework. We first investigate a primal inventory problem with an MV objective function. Owing to the nonseparable nature of variance, we construct an auxiliary problem which is separable. By solving the auxiliary problem, we identify the conditions under which the solutions of the primal and auxiliary problems converge. Hence, we propose the algorithm and show that a base-stock policy is optimal.

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time-cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance.

Motivated by the popularity of VaR measure in financial applications, we study the classical newsvendor problem with Value-at-Risk (VaR) consideration and price-dependent demands. We first investigate the problem's structural properties and derive analytically the optimal joint stocking and pricing decisions. We then explore the difference between the optimal decisions under the VaR formulation and the classical expected profit-maximization model. Finally, we reveal an interesting analytical relationship between the inventory service level and the VaR measure. Insights are generated.

This paper develops game-theoretic models to investigate the optimal competitive capacity-price decisions for two build-to-order manufacturers when they face the disruption of a random demand surge. Both manufacturers have their fixed capacity and pricing decisions for the low-demand period. When there is a sudden demand increase, they can temporally acquire extra capacity and change their pricing decisions. Our goal is to determine the optimal joint capacity and pricing decisions for both low- and high-demand periods. We show that there exists a unique subgame perfect Nash equilibrium that is affected by the distribution of the disrupted amount of demand, the duration of the demand change, the market scale, the unit production cost, and the subcontracting cost. The recommendations on how and when the manufacturers should strategically increase their profits by adjusting their capacities and prices are provided. We also find that the demand disruption largely influences the motivation of the manufacturers to acquire capacity information when the cost of acquiring capacity information is considered. The effects of capacity and pricing competition are investigated. Insights are generated, and future research directions are outlined.

Fabric selection is a crucial step in fashion product development. Prior research works have studied the prediction of fabric specimens based on the fabric hand descriptors via either traditional statistical methods or artificial intelligence methods. Despite showing good prediction accuracy, these methods usually lack an understandable ruleset, which means their “interpretability” is low. In this paper, a fuzzy neural network (FNN) based intelligent fabric hand prediction system is explored. Unlike some traditional FNN models in which a full ruleset of the artificial neural network (ANN) is presumed, the proposed FNN system includes a simplification of the network structure and feature selection, so that the number of rules is significantly reduced without big sacrifice on prediction accuracy. Real datasets collected from 30 participants' evaluation on a set of ten fabric specimens are used to train and test the performance of the proposed system. The system's prediction accuracy is found to be over 80%. Applications of the proposed system are discussed and future research directions are outlined.

In this paper, we apply the game theory to study some strategic actions for retailers to fight a price war. We start by modeling a noncooperative pure pricing game among multiple competing retailers who sell a certain branded product under price-dependent stochastic demands. A unique Nash equilibrium is proven to exist under some mild conditions. We demonstrate mathematically the incentives for retailers to start a price war. Based on a strategic framework via the game theory, we illustrate the use of service level to build price walls which can prevent a huge drop in price, as well as profit. Three kinds of price walls are proposed, and the respective strengths and weaknesses have been studied. Analytical conditions, under which a price wall can effectively prevent big drops in both market share and profit, are developed. Aside from the proposed price walls, two other pricing strategies, which can lead to an all-win situation, are examined.

The newsvendor problem is a fundamental building block for inventory management with a stochastic demand. The classical newsvendor problem focuses on a sole objective of either minimizing the expected cost or maximizing the expected profit. However, the performance measure with expected value alone is insufficient, and it ignores the risk preferences of the decision makers. As a result, we carry out a mean-variance analysis of the newsvendor problem. We construct analytical models and reveal the problem's structural properties. We propose the solution schemes which help to identify the optimal solutions. Interesting findings regarding the efficient frontier, the case with a stockout penalty cost, and the safety-first objective are discussed.

This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.