‘The micro/nano reinforced particle’ aluminum metal matrix composites (Al-MMCs) are widely used in manufacturing sector due to light-weight, superior strength-to-weight ratio, better fracture toughness, improved fatigue, and tensile property, enhanced corrosion resistance to harsh environment, etc. This article provides an overview of the manufacturing processes and different reinforcing elements used during the synthesis of Al-MMCs. Generally, the reinforced particles like carbides, nitrides, and compounds of oxides are used. Different organic, inorganic, industrial and agricultural waste which can be used for reinforcement in the aluminum matrix is highlighted with their feasible applications. The common mechanical properties (i.e. hardness, tensile and compressive strength, etc.) reported by different researchers are thoroughly discussed with the aim to highlight the amount of reinforcement and improvement occurred during processing. The formation and methodology for mixing condition and sintering behaviour of Al-MMCs are discussed to impart knowledge about the processing circumstances in powder metallurgical route. The affecting conditions during operating and responsible factor for the tribological behaviour are deliberated in a precise manner to recognize the potentiality of reinforcing particles in Al-MMCs. Finally, the different shortcomings and future prospects of the Al-MMCs are given to encourage the future research directions.

Consumers’ dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry.

This paper proposes comparisons among two different competitive power structures with and without horizontal coordination. We conduct a game theoretic analysis on the situation where two logistics service providers compete in an e-commerce logistics market with respect to the order quantity and service level decision on a particular logistics service product. Under a consumer utility-based demand, we acquire the equilibrium order quantity decisions and optimal profits under the centralised setting and decentralised setting. An analytical model is established to examine the existence of the Nash equilibrium in the service game. We then prove that the logistics service demand depends on different competitive power structures with the establishment of Cournot game and Stackelberg game model. The analytical results and the equilibrium analysis of the e-commerce logistics system are investigated. After that, we compare the equilibrium results among the centralised system, Cournot game, and Stackelberg game. To empower the horizontal carrier coordination, we design a revenue sharing contract for the decentralised model. A numerical study illustrates the impact of parameters on the optimal decision variables. In this paper, we prove the effectiveness of horizontal carrier collaboration and explicate the reason for the lower marginal profit in logistics theoretically.

This paper investigates a less-than-truckload carrier collaboration decision-making problem in the e-commerce logistics network. E-commerce less-than-truckload carrier collaboration problem considers multiple logistics service providers (LSPs) forming a collaborative alliance in an e-commerce logistics network. They share their transportation requests and vehicle capabilities to maximise the total profit of the entire alliance, improve their vehicle utilisation and cope with fluctuations in demand. An e-commerce logistics trading system with collaborative decisions is designed. A collaborative transportation planning model is introduced to maximise the total profit without reducing the individual profit of the carriers with information sharing. A stochastic plant-pollinator algorithm is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed plant-pollinated algorithm performs better than the genetic algorithm. Furthermore, the results illustrate that the higher degree of cooperation, the more benefits for carriers. Last but not least, since the increasing gasoline price leads to the decreasing margins for the small- and medium-sized LSPs. The results also show that it is critical for them to join in the alliance to survive in the competition.

This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.