In cognitive radio networks (CRNs), multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time, i.e., request arrivals usually show an aggregate manner. Moreover, a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted, and this impatient behavior inevitably has an impact on the system performance. Aiming to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs, in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is established. By constructing a Markov chain and presenting a transition rate matrix, the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the system. Accordingly, some important performance measures such as the loss rate, the balk rate and the average delay of secondary user packets are given. Moreover, system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters, different batch sizes of secondary user packets, and different arrival rates of primary user packets. Finally, a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.

In this paper, we develop the price competition model of two supply chains, in which each supply chain includes one core manufacturer and one retailer, respectively. The manufacturer in each supply chain sells products to the retailer through a commonly-used wholesale price contract. Each manufacturer has two options to implement the wholesale price contract: playing the Stackelberg game with the retailer and playing the bargaining game with the retailer. Based on the manufacturer’s two alternative performing modes in each supply chain, we consider four combined performing modes of two competitive supply chains in the model. By comparing equilibrium results, we find that when both manufacturers choose to bargain with retailers, the sales volume increases and the sales price decreases. Moreover, the manufacturers’ mode option is affected by bargaining power, product quality level, and the cost of improving product quality. Specifically, when both bargaining power and the cost of improving product quality are relatively small, both manufacturers choose to play Stackelberg game with retailers. When manufacturers’ bargaining power is sufficiently large, regardless of the cost of improving product quality, both manufacturers choose to bargain with retailers. Surprisingly, when the manufacturer chooses to bargain the wholesale price with the retailer, higher product quality is not always beneficial to the retailer because the retailer may have to share part of the cost of the manufacturer.

We study a supply chain in which an original equipment manufacturer (OEM) outsources manufacturing functions to a contract manufacturer (CM) for cost saving. In addition to accepting the OEM’s order, the CM can develop self-branded products and enter the end-market. Because of the OEM’s well-known brand and long-term good reputation, we take the asymmetric customer loyalty into consideration - a fraction of customers are loyal customers (LCs) who only consider buying the product from the OEM, while other customers in the market are non-loyal customers (NCs) who might switch to purchasing the CM’s product because of the lower selling price. We explore whether the CM will enter the end-market and if so, what is the impact of the market entry on the OEM and the entire supply chain. Counterintuitively, we find that there will be no market entry when the CM is relatively strong, i.e., the CM is able to provide self-branded products with relatively high acceptance and the CM has a relatively large bargaining power. In addition, when the NC segment is an important profit source for the OEM, i.e., the NCs’ market size is relatively large and their willingness-to-pay is relatively high, the market entry will not occur, either. The OEM’s profit is always hurt by the CM’s market entry unless the OEM always only sells the product to the LCs, while the supply chain sometimes can get benefit. Through numerical simulations, we reveal the relationships between the profit change of different parties and the key parameters, such as the relative willingness-to-pay and market sizes of two types of customers, the CM’s bargaining power and the acceptance of the CM’s product, when the market entry becomes an available option for the CM. The impact of production cost difference on the market entry decision and profit changes is also analyzed in the extension when producing the OEM’s product is more costly for the CM than producing the self-branded product.

Garbage collection is an important issue in urban environmental management. With the increased awareness of urban residents regarding safety, environmental protection, and health in recent years, it is necessary to logically organize municipal solid waste collection and transportation routes while also considering economic and social benefits. This article focuses on the optimization of the waste transportation routes of garbage trucks. With the objective of minimizing transportation costs and maximizing resident satisfaction, we establish a robust optimization model for the multi-trip collection and transportation of municipal solid waste in an uncertain environment. Resident satisfaction is defined as the penalty cost against a time window constraint. The Bertsimas robust optimization method is applied to characterize the uncertainty, and the decision-making scheme of the receiving route is used to adapt to waste volume changes. We conduct a case study based on real-world data for municipal solid waste collection and transportation in the Dongcheng District of Beijing, China. The solution is validated using the CPLEX program, and the validity of the model is verified. In addition, a sensitivity analysis of related parameters is conducted to study the impacts of variations in work hour limits and time windows on the total cost and service levels, as well as their relationships with the level of robustness. This could help decision-makers make reasonable choices based on actual conditions and to balance reductions in total cost with service level improvements.

This paper provides data envelopment analysis methods based on partially ordered set theory. These methods reveal the special relationships between two decision making units from the perspective of mathematical theory and offer the classification, projection and improvement methods of decision making units. It is proved that an efficient decision making unit must be a maximal element of the related poset, and the maximal element may not be efficient. For this, we introduce the concepts of minimum envelope and efficiency envelope which further reveal the special relationship among efficient and inefficient decision making units. Compared with the previous methods, this method not only reveals theoretically the complex relationship among decision making units and the causes of the ineffectiveness, but also gives a new importance and competitiveness measurement method to each decision making unit. Finally, related algorithm and examples are given for the application of these methods to complex decision making problems.

With the rapid development of information technology and fast growth of Internet users, e-commerce nowadays is facing complex business environment and accumulating large-volume and high-dimensional data. This brings two challenges for demand forecasting. First, e-merchants need to find appropriate approaches to leverage the large amount of data and extract forecast features to capture various factors affecting the demand. Second, they need to efficiently identify the most important features to improve the forecast accuracy and better understand the key drivers for demand changes. To solve these challenges, this study conducts a multi-dimensional feature engineering by constructing five feature categories including historical demand, price, page view, reviews, and competition for e-commerce demand forecasting on item-level. We then propose a two-stage random forest-based feature selection algorithm to effectively identify the important features from the high-dimensional feature set and avoid overfitting. We test our proposed algorithm with a large-scale dataset from the largest e-commerce platform in China. The numerical results from 21,111 items and 109 million sales observations show that our proposed random forest-based forecasting framework with a two-stage feature selection algorithm delivers 11.58%, 5.81% and 3.68% forecast accuracy improvement, compared with the Autoregressive Integrated Moving Average (ARIMA), Random Forecast, and Random Forecast with one-stage feature selection approach, respectively, which are widely used in literature and industry. This study provides a useful tool for the practitioners to forecast demands and sheds lights on the B2C e-commerce operations management.