It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

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Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Supply chain cooperation strategy believes that the integration of the operation process can produce value for customers, optimize the supply chain of the connection between the vertical nodes, and constantly strengthen the performance of the advantages, so as to achieve mutual benefit and win-win results. Under fuzzy uncertain environment and centralized decision-making mode, we study multiobjective decision-making optimization, which focuses on equilibrium and compensation of multiobjective problems; that is to say, the proper adjustment of the individual goal satisfaction level will make other goals’ satisfaction levels change greatly. Through coordination among the multiobjectives, supply chain system will achieve global optimum. Finally, a simulation experiment from Shaoxing textile case is used to verify its efficiency and effectiveness.

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Supply Chain Network Optimization Based on Fuzzy Multiobjective Centralized Decision-Making Model

In this survey paper, we critique the optimization studies on the distribution of finished vehicles from automobile manufacturers to dealers in the past three decades and propose promising prospective research to minimize the gap between industrial practice and academic research. First, we identify major decision makers involved in automobile distribution, summarize service models, and briefly describe the automobile shipping practice by transportation mode. After defining the automobile shipping optimization problem at the operational level, we present the automobile distribution problem taxonomy by classifying existing studies by the level of decision making, mode of transportation, and type of optimization decisions. Each subcategory of studies is reviewed in detail through comparisons by objective function, constraints, formulation, solution algorithm, and test instances. We conclude this survey paper by summarizing major review outcomes and proposing potential research directions. This survey will stimulate interested transportation researchers to conduct further research to keep pace with the rapid evolution of the automobile shipping practice.

A survey of finished vehicle distribution and related problems from an optimization perspective

Nowadays the enterprises pay the closer attention to the relationship among suppliers, manufacturers and distributors due to the global competitive market economy. And they manage the supply chain through establishing strategic cooperative partnership, which can greatly enhance the competitive advantage and obtain greater overall profits. In this paper, the fuzzy theory is applied to study the supply chain partner selection and the task coarse allocation problem in under multi-attribute fuzzy comprehensive decision-making and fuzzy constraints. Finally, the fuzzy comprehensive decision of supply chain network structure was verified through the case of Shaoxing textile.

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Research on supply chain partner selection and task allocation based on fuzzy theory under an uncertain environment

Currently, group behaviors happen frequently with the development of network technology. As a typical social group behavior, group polarization has been attracted more and more academic attention due to its significant disturbance to public's daily lives. At present, the classic J‐A (proposed by Jager and Amblard) and D‐W (proposed by Deffuant and Weisbuch) models are used to analyze group polarization process. However, the main shortcomings existing in these models are that the individuals' psychology and their network relationships are rarely considered. In order to overcome the limitations, this article integrates the influence factors such as conformity and network relationship strength integrated into the polarization model. Besides, the BA (proposed by Barabasi and Albert) network model is used as the agent adjacency model due to its closer to the real social network structure. Subsequently, the experimental simulations are carried out with the multi‐agent Monte‐Carlo method so as to testify the efficiency and effectiveness. The results indicate that different information interaction modes have essential influence on group attitude polarization. Moreover, conformity parameters and the intensity of relationship have dual impacts on both speeding up and slowing down the polarization process.

周广澜

Papers

Disruption management for vehicle routing problem with time-window changes