A Truck Scheduling Problem for Multi-Crossdocking System with Metaheuristics

The cross-docking is a very important subject in logistics and supply chain managements.According to the definition,cross-docking is a process dealing with transhipping inventory,in which goods and products are unloaded from an inbound truck and process through a flow-center to be directly loaded onto an outbound truck.Cross-docking is favored due to its advantages in reducing the material handing cost,the needs to store the product in warehouse,as well decreasing the labor cost by eliminating packaging,storing,pick-location and order picking.In cross-docking,products can be consolidated and transported as a full load,reducing overall distribution costs.In this paper,we focus on a truck scheduling at the multidoor,multi-crossdocking network with inventory constraints and process capability constraints.In this model,a truck can visit severals docks for loading or unloading many types products.This situation is very common in reality.This study also developed an exact mathematical model using mixedinteger linear programming(MILP)with the objective of minimizing the makespan to obtaint the benchmark in small scale problems.Large scale problems are solved through Simulated Annealing(SA)algorithm and Tabu Search(TS)algorithm.Performance of these algorithms will be compared to benchmarks obtained from solver as well as to each other.

Face Mask Recognition for Covid-19 Prevention

In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning.It influences activities such as purchasing,manufacturing,and selling,and quick adaption is required.In production planning,for reasons such as reducing costs and obtaining supplier discounts,many decisions must be made in the initial stage when demand has not been realized.The effects of non-optimal decisions will propagate to later stages,which can lead to losses due to overstocks or out-of-stocks.To find the optimal solutions for the initial and later stage regarding demand realization,this study proposes a stochastic two-stage linear program-ming model for a multi-supplier,multi-material,and multi-product purchasing and production planning process.The objective function is the expected total cost after two stages,and the results include detailed plans for purchasing and production in each demand scenario.Small-scale problems are solved through a deterministic equivalent transformation technique.To solve the problems in the large scale,an algorithm combining metaheuristic and sample average approximation is suggested.This algorithm can be implemented in parallel to utilize the power of the solver.The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given,then the problems of the first and second stages can be decomposed.

Supply Chain Management Practices, Competitive Advantages and Firm Performance： A Case of Small and Medium Enterprises （SMEs） in Vietnam

The perception of supply chain management （SCM） first appeared in 1982 and the very first papers on SCM were published in the mid 1980s （Houlihan, 1985）. However, this term is still fresh in Vietnam, especially the subject of SCM practices and their impacts on firm performance of small and medium enterprises （SMEs）. Therefore, the research was conducted to study the impact of supply chain management practices （SCMPs） on firm performance of SMEs through firm competitive advantages. Data were collected by surveying SMEs located in Ho Chi Minh City. The research findings showed that customer relationship （CR） and quality of information sharing （IQ） impact on firm performance at 0.01% significance level while the level of information sharing impacts on firm performance at 10% significance level, and strategic supplier partnership impacts on firm performance insignificantly. Moreover, we also found that customer relationship and quality of information sharing influence firm competitive advantage at 0.01% significance level, while strategic supplier partnership and the quality of information sharing impact on firm competitive advantage at 5% significance level. Competitive advantages impact significantly positively on firm performance at significance level of 0.001%. The findings imply that SMEs in Vietnam should manage customer relationship well and increase the level of information quality to improve competitive advantage in order to gain high performance.