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The purpose of this paper is to deal with profit maximization problem of two-layer supply chain (SC) under fuzzy stochastic demand having finite mean and unknown variance. Buyback policy is employed from the retailer to supplier. The profit of the supplier solely depends on the order size of the retailers. However, the loss of shortage items is related to loss of profit and goodwill dependent. The authors develop the profit function separately for both the retailer and supplier, first, for a decentralized system and, second, joining them, the authors get a centralized system (CS) of decision making, in which one is giving more profit to both of them. The problem is solved analytically first, then the authors fuzzify the model and solve by fuzzy Hausdorff distance method.

The analytical models are formed for both centralized and decentralized systems under non-cooperative and cooperative environment with suitable constraints. A significant assumption on density function, namely Cauchy-type density function, is introduced for demand rate because of its wider range of the retailers’ satisfactions. Fuzzy Hausdorff metric is incorporated within the fuzzy components of the fuzzy sets itself. Using this method, the authors find out closure values of both centralized and decentralized policies, which is an essential part of any cooperative and non-cooperative two-layer SC models. Moreover, the authors take care of the profit values with corresponding ambiguities for both the systems explicitly.

It is found that the centralize policy of SC could only be able to maximize the profit of both the retailers and suppliers. All analytical results are illustrated numerically along with sensitivity analysis and side by side comparative studies between Hausdorff and Euclidean distance measure are done exclusively.

The main focus of attention of the proposed model is given to usefulness of Hausdorff distance. Unlike other distances, Hausdorff distance can take special care on the similarity measures of different fuzzy sets. Researchers have been engaged to use Hausdorff distance on the different fuzzy sets but, in this study, the authors have used it within the components of a same fuzzy set to gain more closure values than other methods.

The use of this Hausdorff distance approach is totally new as per literature survey suggested yet. However, the Cauchy-type density function has not been introduced anywhere in SC management problems by modern researchers still now. In crisp model, the sensitivity on goodwill measures really provides a special attention also.

The purpose of this paper is to propose an economic production quantity (EPQ) inventory model considering imperfect items and probabilistic demand for a two-echelon supply chain. The production process is imperfect and the imperfect quality items are removed from the lot size. The demand rate of the inventory system is random and follows an exponential probability density function and the demand of the retailers is depending on the initiatives of the sales team.

Two approaches are examined. In the non-collaborative approach, any member of the supply chain can be the leader and takes decisions to optimize the profits, and in the collaborative system, all members make joint decisions about the production, supply, sales and inventory to optimize the profits of the supply chain members. The calculus approach is applied to find the maximum profit related to the members of the supply chain.

A numerical example is presented to illustrate the performance of the EPQ model. The results show that collaborative approach generates greater profits to the supply chain and the market’s demand represents the variable behavior and uncertainty that is generated in the replenishment of a supply chain.

The new and major contributions of this research are: the inventory model considers demand for products is random variable which follows an exponential probability distribution function and it also depends on the initiatives of sales teams, the imperfect production system generates defective items, different cycle time are considered in manufacturer and retailers and collaborative and non-collaborative approaches are also studied.

This paper aims to explore optimal pricing policies and characteristics of a two-level dual-channel supply chain under price- and delivery time-sensitive demand. Besides price of the product, the delivery lead time is also a crucial factor in customers’ purchase decisions. A longer delivery lead time would diminish customers’ acceptance and faithfulness on the online channel, while a shorter delivery lead time would lead to incorporation of a substantial amount of logistics costs. In formulation of mathematical model, the effects of delivery lead time on the manufacturer and the retailer’s pricing strategies and profits in cooperative and non-cooperative dual-channel supply chain are explained analytically.

The analytical models are formed for both non-cooperative and cooperative scenarios under inconsistent and consistent pricing. The authors examine whether revenue sharing (RS) contract or delivery cost sharing contract can solely coordinate the dual-channel supply chain. If a single contract fails, then the combination of RS contract with delivery cost sharing to achieve channel coordination is discussed.

It is found that the RS or delivery cost sharing contract cannot coordinate the channel individually but revenue and delivery cost sharing contract jointly coordinate the channel. All analytical results are illustrated numerically, along with sensitivity analysis.

There are many correlated issues that need to be further investigated. First, one good extension to this research may include the consideration of the channel structure with competitive retailers. It will be interesting to analyze the performance of coordination mechanisms by considering the retailer as a Stackelberg leader in retailing.

The findings and subsequent methodological discussions aim to provide practical guidance to retailers who are allowing customers to choose how, when and where they interact and purchase by offering a combination of websites (fully functional and mobile-enabled), catalogs and stores with increasing convergence of channels.

This research work introduces an imperfect production system where the demand is assumed to be stochastic and it is influenced by random selling price. The shift time from an “in-control” state to an “out-of-control” state is exponentially distributed. The accumulated inventory contains both perfect and defective items which are all sold with a free repair warranty (FRW) offer. Complete back ordering of shortages are taken into account. The purpose of this paper is to determine the optimal selling price and hence the optimal production lot size such that the expected profit is maximized.

The general model is discussed separately for both types of uniformly distributed selling price-sensitive demand pattern: additive type and multiplicative type. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the models.

This paper helps the manager to manage future situations and it may be considered as a base work for the researchers to work in this direction.

The main limitation of this model is to consider a single item for a single channel system. There are many correlated issues that need to be further investigated. The future study in this direction may include the consideration of multi-items, diverse demand pattern with different types of price distributions.

In the production inventory literature, plenty of articles are available considering imperfect production but none of them have considered selling price-sensitive stochastic demand where the sales price is random in character under an FRW offer.

The purpose of this study is to formulate two multi-product single-machine economic production quantity (EPQ) models by considering imperfect products. Two policies are assumed to deal with imperfect products: selling them at discount and applying a reworking process.

A screening process is used to identify imperfect items during and after production. Selling the imperfect items at a discount is examined in the first model and the reworking policy in the second model. In both models, demand during the production process is satisfied only by perfect items. Data collected from a case company are used to illustrate the performance of the two models. Moreover, a sensitivity analysis is carried out by varying the most important parameters of the models.

The case study in this research is used to demonstrate the applicability of the proposed models, i.e. the EPQ model with salvaging and reworking imperfect items. The models are applied to a high-tech un-plasticized polyvinyl chloride (UPVC) doors and windows manufacturer that produces different types of doors and windows. ROGAWIN Co. is a privately owned company that started in 2001 with fully automatic production lines. Finally, the results of applying the different ways of handling the imperfect items are discussed, along with managerial insights.

In real-world production systems, manufacturing imperfect products is unavoidable. That is why, it is important to make a proper decision about imperfect products to reduce overall production costs. Recently, applying a reworking strategy has gained the most interest when it comes to handling this problem. The principal idea of this research is to maximize the total profit of manufacturing systems by optimizing the period length under some capacity constraints. The proposed models were applied to a company of manufacturing UPVC doors and windows.

This paper aims to embark a mathematical model based on investigation and comparison of airport pricing policies under various types of competition, considering both per-passenger and per-flight charges at congested airports.

In this model, four-game theoretic strategies are assessed and closed-form formulas have been proved for each of the mentioned strategies. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the models.

The rectitude of the presented formulas is evaluated with sensitivity analysis and numerical examples have been put forward. Finally, managerial implications are suggested by means of the proposed analysis.

The represented model is inherently limited to investigate all the available and influential factors in the field of congestion pricing. With this regard, several studies can be implemented as the future research of this study. The applications of other game theoretic approaches such as Cartel games and its combination with the four mentioned games seem to be worthwhile. Moreover, it is recommended to investigate the effectiveness of the proposed model and formulations with a large-scale database.

The authors formulate a novel strategy that put forwards a four-game theoretic strategy, which helps managers to select the best suitable ones for their specific airline and/or air traveling companies. The authors find that by means of the proposed model, the application of Stackelberg–Bertrand behavior in the field of airport congestion pricing will rebound to a more profitable strategy in contrast with the other three represented methods.

Social media has given customers more power over sharing their knowledge, opinions and experiences with each other. Tourists as customers of destinations are also using text ads on social media and websites to share their experiences. The purpose of this study is to find out the factors which have affect on the decisions of tourists towards the most popular destinations in Tehran, Isfahan and Shiraz of Iran.

Netnography methodology has been applied to 2,852 comments showing travelers’ experiences through TripAdvisor.com. As a result, ten major factors have been discovered. According to these factors, a questionnaire has been designed and distributed among 449 tourists. In the second step, the collected data are used by rough set theory to discover the rules of destination recommendation based on the factors discovered before. Finally, eight main rules are determined to further analysis.

The findings confirm that beauty, cultural attractions, safety, welfare, costs and dealing with passengers are more important than other observed dimensions.

In this study, first the factors affecting consumer behavior in the tourism industry have been investigated. Based on this, the comments of tourists who have traveled to one of the cities Shiraz, Isfahan or Teheran and shared their experiences on TripAdvisor.com are studied. Further, the rules are discovered based on the rough set theory, and owing to the large number of objects (449 customer), the Rosetta software has been used.

This study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.

The proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.

The findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.

The outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.

The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.

Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).

The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.

The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).

This paper aims to deal with optimal pricing and production tactics for a bi-echelon green supply chain, including a producer and a vendor in presence of three various scenarios. Demand depends on a price, refund and quality where the producer controls quality and the vendor proposes a refund policy to purchasers to encourage them to order more.

In the first scenario, the members seek to optimize their optimum decision variables under a centralized decision-making method while in the second scenario, a decentralized system is assumed where the members make a decision about variables and profits under a non-cooperative game. In the third scenario, a cost-sharing agreement is concluded between the members to provide a high-quality item to the purchasers.

The performance of the proposed model is investigated by illustrating a numerical example. A sensitivity analysis of some key parameters has been done to study the effect of the changes on the optimal values of the decision variables and profits. From sensitivity analysis, the real features are observed and mentioned in this section.

This research examines the behavior of partners in a green supply chain facing with a group of purchasers whose demand is the function of a price, greenery degree and refund rate. This proposed mathematical model is developed and analyzed which has an implication in supply chain model.