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The purpose of this paper is to investigate optimal decision methods under a cooperative situation in two-echelon logistic models.

The authors propose the optimal strategies of exporters in the three types of rival game behaviors: Stackelberg, Collusion, and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario.

From the empirical studies the paper finds that among three scenarios, the oligopolistic treatment of forwarders’ actions demonstrates that Stackelberg behavior can carry out the maximum profit, and Collusion game can achieve the maximum profit for the shippers.

Proposed an optimal decision methods in two-echelon logistic models.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

Online retailers have become gradually popular to offer consumers installment payment services in the past few years. This paper aims to study how to determine the duration and rate of installment payment services, as well as the price of products to increase online retailers’ profits.

By modeling the utility functions, the behavior of consumers for strategic choosing the payment method and payment timing is analyzed. Thus, the market segments are obtained through the comparison of the consumer’s utilities. Combined with the given assumptions, the installment payment strategies for online retailers is investigated. This paper focuses on the impact of installment payment services on consumers’ purchasing behavior and online retailers’ profits by modeling and comparative analysis. No installment payment service as a benchmark, it is demonstrated whether online retailers can obtain more profits by offering installment payment services or what are the applicable conditions for installment payments.

If the installment payment service is offered, online retailers can gain more profits and need to adopt appropriate strategies based on different market conditions. During the depression or the peak shopping season, online retailers should take the strategy of free installment rate, and moderately increasing the product price of no installment service. When market demand is stable or during non-peak season, online retailers need to set a higher installment rate and maintain the product price without installment service. Finally, online retailers should determine the maximum duration of installments they can afford based on own risk control cost and allow consumers to freely choose the length of the installment within the duration limit.

First, the authors deeply analyze consumers’ payment and purchase behavior when the online retailer offers the installment payment service. Then, it is theoretically proved why many online retailers have offered installment payment services to consumers from a profit perspective. Finally, this paper proposes the optimal duration of installments, installment rate and product price in different market environments for online retailers, to provide theoretical basis and managerial insights for the development of installment payment service in online shopping.

Vertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.

This paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.

The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.

In this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.

Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.

The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This research aims to examine which cooperative contract (wholesale-price contract or cost-sharing contract) can more effectively upgrade the green degree of product and promote demand when considering consumer reference price effect under different power structures.

This research investigates a dyadic green supply chain composed of one manufacturer and one retailer. Four Stackelberg game models with a cost-sharing contract or a wholesale-price contract are built in retailer-led and manufacturer-led scenarios, respectively. Using backward induction, the optimal green decision under each model is obtained. In addition, the optimal cooperative contract is proposed by comparing these four models.

It is found that under consumer reference price effect, a cost-sharing contract outperforms a wholesale-price contract in upgrading product greenness and promoting demand. Under any single contract, the retailer-led situation is more conducive to improving product greenness than the manufacturer-led situation. Moreover, consumer reference price effect would reduce the sharing ratio of a cost-sharing contract when the manufacturer dominates, but it could mitigate the problem of double marginalization by reducing wholesale and retail prices under both types of contracts, which would enhance consumer surplus.

It is a new attempt to incorporate consumer reference price effect and power structure into a green supply chain framework and proposes a novel demand function that simultaneously emphasizes consumer reference price effect, consumer environmental awareness and product green attribute. In addition, it provides managerial insights for business managers to choose green cooperative contracts with consumer reference price effect under different power structures.

Based on the theoretical viewpoints of criminal geography and environmental criminology, this research uses spatial multi-criteria decision-making methods. In the process of spatial decision-making and optimization of police resources, researchers fully consider the dynamic application of Geographic Information System (GIS) and the effects of spatial prevention and control.

Researchers use an integrated method combining Policing Geographic Information System (PGIS) and multi-criteria decision analysis (MCDA). On the one hand, police GIS has an excellent visual data analysis platform and integrated decision support system in data management, spatial analysis, data exploration and regression analysis. On the other hand, through the design of the indicator system, the quantification of indicators, the determination of weights, comprehensive evaluation and sensitivity analysis, MCDA can select the best plan from a large number of alternatives. When joining MCDA, the spatial dimension will bring the research results closer to the real world.

The study finds that the crime of burglary is affected to a certain extent by the distribution of police forces, the location of police units. Another important finding of this research is the correlation between more precise preventive measures and the crime of burglary.

From a practical point of view, this research would help advance the role of police units and law enforcement agencies in preventing burglary crimes and provide experience for the allocation of regional police resources.

This study investigates the pricing and inventory control decisions of a company that owns a coffeehouse chain providing drip coffee and a coffee manufacturing factory producing ready-to-drink (RTD) coffee. To determine a way to maximize profit, optimal pricing and inventory control strategies are studied for both the RTD coffee as well as the coffeehouse chain network.

It is assumed that the company sells only drip coffee through its coffeehouse chain and would like to launch RTD coffee via other channels such as convenience stores, supermarkets and so on to maximize its total profit. A mathematical model–based optimization is adopted to address the decision-making for the given problem situation, where the demand for both drip and RTD coffee are dependent on the values of decision variables. To solve the proposed mathematical model, particle swarm optimization (PSO) is applied due to nonlinearity of the developed model.

It is confirmed that a company can earn more profit by launching RTD coffee, even though the profit from drip coffee would reduce. In addition, the scenario analysis shows that by launching RTD coffee under various conditions, the total profits would also improve.

The value of this study is the proposed basic framework for the industry. In addition to the modeling framework and cost structure, realistic cost figures and technical details can be considered when applying the model to a practical setting.