Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction

Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, a multiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products

A Visualization Review of Cloud Computing Algorithms in the Last Decade

Cloud computing has competitive advantages—such as on-demand self-service, rapid computing, cost reduction, and almost unlimited storage—that have attracted extensive attention from both academia and industry in recent years. Some review works have been reported to summarize extant studies related to cloud computing, but few analyze these studies based on the citations. Co-citation analysis can provide scholars a strong support to identify the intellectual bases and leading edges of a specific field. In addition, advanced algorithms, which can directly affect the availability, efficiency, and security of cloud computing, are the key to conducting computing across various clouds. Motivated by these observations, we conduct a specific visualization review of the studies related to cloud computing algorithms using one mainstream co-citation analysis tool—CiteSpace. The visualization results detect the most influential studies, journals, countries, institutions, and authors on cloud computing algorithms and reveal the intellectual bases and focuses of cloud computing algorithms in the literature, providing guidance for interested researchers to make further studies on cloud computing algorithms

Influences of Government Policies and Farmers’ Cognition on Farmers’ Participation Willingness and Behaviors in E-Commerce Interest Linkage Mechanisms during Farmer–Enterprise Games

E-commerce interest linkage mechanisms serve as an effective solution to the problems of farmer–market cooperation, agricultural supply-side reforms, and farmers’ income growth. This study, guided by the theory of planned behavior, undertook an evolutionary game analysis of farmer–enterprise cooperation with government interventions with farmers. Based on data from 554 questionnaires administered in Mei County, Shaanxi Province, China, this study found a difference between the realistic and optimal choices of farmers. In addition, this study used a structural equation model to investigate the influence of government policies and farmers’ cognition on the participation willingness and behaviors of farmers in e-commerce interest-linkage mechanisms. The results showed that the optimal choice for farmers in a farmer–enterprise cooperative game is participation in e-commerce, and government policies can be used to improve farmer–enterprise e-commerce interest-linkage mechanisms. Farmers’ basic characteristics and experiences impacted their cognition of e-commerce, which, in turn, had a significant positive effect on their e-commerce participation willingness and behaviors. Government policies had a positive effect on farmers’ experiences, cognition of e-commerce, and participation behaviors, but no direct positive impact on farmers’ willingness to participate. Government policies and farmers’ basic characteristics interacted and acted together on the participation willingness and behavior of farmers

A Two-Stage Approach for Medical Supplies Intermodal Transportation in Large-Scale Disaster Responses

We present a two-stage approach for the “helicopters and vehicles” intermodal transportation of medical supplies in large-scale disaster responses. In the first stage, a fuzzy-based method and its heuristic algorithm are developed to select the locations of temporary distribution centers (TDCs) and assign medial aid points (MAPs) to each TDC. In the second stage, an integer-programming model is developed to determine the delivery routes. Numerical experiments verified the effectiveness of the approach, and observed several findings: (i) More TDCs often increase the efficiency and utility of medical supplies; (ii) It is not definitely true that vehicles should load more and more medical supplies in emergency responses; (iii) The more contrasting the traveling speeds of helicopters and vehicles are, the more advantageous the intermodal transportation is

Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers

Helicopters and vehicles are often jointly used to transport key relief supplies and respond to disaster situations when supply nodes are far away from demand nodes or the key roads to affected areas are cut off. Emergency transfer centers (ETCs) are often changed due to secondary disasters and further rescue, so the extant intermodal transportation plan of helicopters and vehicles needs to be adjusted accordingly. Disruption management is used to re-plan emergency intermodal transportation with updated ETCs in this study. The basic idea of disruption management is to minimize the negative impact resulting from unexpected events. To measure the impact of updated ETCs on the extant plan, the authors consider three kinds of rescue participators, that is, supply recipients, rescue drivers, and transport schedulers, whose main concerns are supply arrival time, intermodal routes and transportation capacity, respectively. Based on the measurement, the authors develop a recovery model for minimizing the disturbance caused by the updated ETCs and design an improved genetic algorithm to generate solutions for the recovery model. Numerical experiments verify the effectiveness of this model and algorithm and discern that this disruption management method could produce recovery plans with shorter average waiting times, smaller disturbances for all the supply arrival times, intermodal routes and transportation capacity, and shorter running times. The comparison shows the advantage of this disruption management method over the rescheduling method

A Network Optimization Research for Product Returns Using Modified Plant Growth Simulation Algorithm

As product returns are eroding Internet retail profit, managers are continuously striving for a more scientific and efficient network layout to arrange the returned goods. Based on a three-echelon product returns network, this paper proposes a mixed integer nonlinear programming model with the aim of minimizing total cost and creates a high-efficiency method, the Modified Plant Growth Simulation Algorithm (MPGSA), to optimize the problem. The algorithm handles the objective function and the constraints, respectively, requiring no extrinsic parameters and provides a guiding search direction generated from the assessment of the current solving state. Above all, MPGSA keeps a great balance between concentrating growth opportunities on the outstanding growth points and expanding the searching scope. The improvements give the revaluating and reselecting chances to all growth points in each iteration, enhancing the optimization efficiency. A case study illustrates the effectiveness and robustness of MPGSA compared to its original version, Plant Growth Simulation Algorithm, and other approaches, namely, Genetic Algorithm, Artificial Immune System, and Simulated Annealing

Determinants of Buying Produce on Short-Video Platforms: The Impact of Social Network and Resource Endowment—Evidence from China

In the wake of the COVID-19 pandemic, selling by short video has become a new online selling model that enhances the communication between buyers and sellers. Therefore, it is necessary to identify the key factors influencing consumers’ purchase of agricultural products on short-video platforms. Additionally, it is also important to figure out the influencing mechanism and action path. Specifically, based on the ‘Stimulus-Organism-Response (SOR)’ framework and structural equation model, we delineate and empirically test hypotheses regarding the effects of key components on consumers’ purchase intentions and behaviors. The key components refer to three external stimuli of consumers’ social network, sellers’ resource endowment, and both sides’ infrastructure development levels. Simultaneously, we analyze the mediating role of consumers’ perceived value and perceived risk between external stimuli and consumers’ purchase intentions. This paper argues that short-video merchants improving the influence of their stores and platforms strengthening supervision and management are the keys to ensuring stable growth in consumers’ willingness to purchase agricultural products sold on short videos and promoting the development of the short-video live industry

Will the Adoption of Early Fertigation Techniques Hinder Famers’ Technology Renewal? Evidence from Fresh Growers in Shaanxi, China

Fertigation technology is key to solve water pollution and inefficient fertilizer use. However, some early techniques cannot adapt to the current situation of labor shortages and large-scale planting. Therefore, it is necessary to consider farmers’ willingness to adopt more adaptive techniques. Specifically, we focus on whether early technology adoption will hinder technology renewal and whether the factors affecting the adoption of early and latest techniques are consistent. Through theoretical analysis and a survey, we find that farmers’ endowments such as income and labor force only affect the adoption intentions to the high-cost technique (Intelligent Irrigation Control System), but not early techniques (Venturi injector and Differential pressure tank), while farmers’ information processing ability and information acquisition channels affect both. Finally, the results of Propensity Score Matching show that early technology adoption will not become an obstacle to technology renewal