Multimodal Transport Network Optimization Considering Safety Stock under Real-Time Information

Abstract

A production department and two distribution departments form a multimodal logistics supply network. The production department provides two ordering opportunities within one production cycle, and the real-time updates of demand information are required in the interval between two orders. Based on this, the paper studies the security inventory optimization problem of multimodal transport networks. On the basis of portraying the real-time information update process, the multimodal network optimization model without considering safety stock and the multimodal network optimization model considering safety stock are constructed, respectively, and the transshipment price and demand information of the intermodal network are updated. The parameters are analyzed for sensitivity. The analysis of examples shows that, on the one hand, with the update of demand information, the distribution department adopts the multimodal transport model with safety stock to improve the profit of the distribution department and the supply network. On the other hand, the production sector can meet market demand with a lower production scale, while the distribution department can effectively respond to stochastic demand through a multimodal model with safety stock.

Uncertain Optimization of Discrete Supply Networks with Order Delivery Disruption and Risk Preference in the Postepidemic Era

“…Moreover, the downside risk, mean variance, and utility function were used as risk measurement tools to realize the multiobjective optimization of supply networks based on the Pareto optimal concept of decision theory [1,11]. Risk preference is one of the important influencing variables of the multiobjective optimization of discrete supply networks [16][17][18][19]. Centralized decision method is a set of comprehensive analysis on multivariate information through data quantification, especially abnormal data inspection and emergency response to interruption [20][21][22].…”

“…Constraint (18) specifies that the vehicle load returned to each warehouse must be equal to 0. Constraints (19) and ( 20) are boundary constraints of load variables. Constraint (21) ensures that the total demand and supply of the warehouse cannot exceed its capacity.…”

“…en, for every feasible solution (u, x), there is a feasible solution (f, x) and vice versa. Based on (13), x h * ij � 1, h ∈ H, and based on (19),…”

A Joint Optimization Model considering the Product User's Risk Preference for Supply System Disruption

“…As the CLRIP integrates three levels, namely, strategy, military, and operation, the CLRIP has been widely used to solve the problem of supply chain integration optimization [4][5][6][7]. Enterprises need more coordinated system networks when facing disruption and hope to resist disruption, reduce costs, and improve efficiency by optimizing strategies [8][9][10].…”

Optimizing Resources of Multimodal Transport Terminal for Material Flow Service

“…At the micro-level, the main tasks are the justification of managerial decisions on servicing of material flows in transport hubs [12,20], the estimation of the intermodal terminal capacity [21], the optimization of the servicing machines' load [22], the optimization of technological operations [23][24][25], the synchronization of the modes of transport interacting in the freight terminal [26], and the inventory optimization problems [27]. It should be mentioned that problems of rationalizing the terminal resources' parameters (the terminal's capacity, the machine's load, the warehouse parameters) are being solved separately despite the production facilities constitute the unified servicing sub-system of a terminal.…”