A Double‐Layer Combination Algorithm for Real‐Time Information‐Sharing Network Design Problem

Abstract

The accumulation of real-time data has attracted the attention of various industries because valuable information can be extracted from the effective model and method design. This paper designs a low-carbon model and focuses on the real-time information-sharing network in order to get sustainable strategies promptly and exactly. The design problem is concerned with determining optimal integration strategies on a series of multilocation, multipath, and multiwarehouse freight provided by an information-sharing network to find an effective balance between the total costs and carbon emission. Firstly, the biobjective information-sharing network model is established to describe real-time problem with total cost and carbon emission factor. Secondly, a double-layer bacterial foraging algorithm is divided into inner and outer layers to solve the model, in which the inner section solves the transportation and inventory problems, and the outer section solves the supplier location problem. The double-layer bacterial foraging algorithm realizes the optimization of multisource e-commerce information-sharing model through nesting inside and outside layers. Finally, double-layer bacterial foraging algorithm can be confirmed to get the global optimal solution rapidly based on test data and the e-business case study of Jingdong, China.

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

“…Supply disruption analysis is of great practical significance to the system decision of the CLRIP. Scholars pay more attention to logistics disruption and supplier disruption [18,19], among which supplier disruption and transportation disruption caused by external environmental factors are the most serious [20,21]. Some scholars considered whether the supplier is regular or disrupted.…”

“…Constraints ( 18) and ( 21) ensure that each user accepts only one warehouse order allocation. Formulas (19) and (20) ensure that the manufacturer warehouse facilities' service capacity is sufficient to meet the demands of product users. Constraints ( 21) and ( 22…”

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 (16) means that the total load of any arc cannot exceed the vehicle's capacity. Constraint (17) ensures that the total load of each warehouse is equal to the total demand of its downstream firms. Note that constraint (17) strengthens the constraint condition, but it is unnecessary and will be simplified in the subsequent solution process.…”

“…Constraint (17) ensures that the total load of each warehouse is equal to the total demand of its downstream firms. Note that constraint (17) strengthens the constraint condition, but it is unnecessary and will be simplified in the subsequent solution process. Constraint (18) specifies that the vehicle load returned to each warehouse must be equal to 0.…”