Robust Two-Stage Location Allocation for Emergency Temporary Blood Supply in Postdisaster

Disaster medical rescue in China mainly adopts the “on-site rescue” model. Whether the location of emergency temporary blood supply sites is reasonable or not directly affects the rescue efficiency. The paper studies the robust location-allocation for emergency temporary blood supply after disaster. First, the factors of several candidate sites were quantified by the entropy-based TOPSIS method, and 12 candidate blood supply sites with higher priority were selected according to the evaluation indicators. At the same time, the uncertainty of blood demand at each disaster site increased the difficulty of decision-making, and then, a robust location model (MIRP) was constructed with minimum cost with time window constraints. It is also constrained by the uncertain demand for blood in three scenarios. Second, the survival probability function was introduced, and the time window limit was given at the minimum cost to maximize the survival probability of the suffered people. Finally, the numerical example experiments demonstrate that the increase in demand uncertainty and survival probability cause the MIRP model to generate more costs. Compared with the three MIRP models, the MIRP-ellipsoid set model gained better robustness. Also, given the necessary restrictions on the time window, the cost can be reduced by about 13% with the highest survival probability. Decision-makers can select different combinations of uncertainty levels and demand disturbance ratios and necessary time constraints to obtain the optimal location-allocation solution according to risk preference and actual conditions.

Fashion Supply Chain Optimization: A Linear Programming Model

Fashion supply chain management is challenging due to demand uncertainty, short product life, fierce global competition, and increasing expectations such as product diversity and keeping pace with the current and emerging trends. In this paper, a brief review of the published works in the field of fashion and clothing supply chain is given and an integrated linear model is presented to minimize a fashion brand’s costs, including production and logistics costs, enabling the firm to make better decisions with regard to its production and replenishment policies and responding in a reasonable time to customers demands for various designed items. We show that our model can successfully account for complex structure of the supply chain consisting of designer, manufacturers, distributors, and stores. The model is validated using the General Algebraic Modelling System (GAMS) with the data collected from an Iranian fashion brand.

Location-Routing Problem of Emergency Facilities under Uncertain Demand by Branch-Price and Cut

This paper studies the location-routing problem of emergency facilities with time window under demand uncertainty. We propose a robust mathematical model in which uncertain requirements are represented by two forms: the support set defined by cardinal constraint set. When the demand value of rescue point changes in a given definition set, the model can ensure the feasibility of each line. We propose a branch and price cutting algorithm, whose pricing problem is a robust resource-constrained shortest path problem. In addition, we take the Wenchuan Earthquake as an example to verify the practicability of the method. The robust model is simulated under different uncertainty levels and distributions and compared with the scheme obtained by the deterministic problem. The results show that the robust model can run successfully and maintain its robustness, and the robust model provides better protection against demand uncertainty. In addition, we find that cost is more sensitive to uncertainty level than protection level, and our proposed model also allows controlling the robustness level of the solution by adjusting the protection level. In all experiments, the cost of robustness is that the routing cost increases by an average of 13.87%.

Why Do Companies Need Operational Flexibility to Reduce Waste at Source?

We analyze the environmental benefits of operational flexibility that emerge in the form of less product waste during the sourcing process by reducing overproduction. We consider three different options for operational flexibility: (1) lead-time reduction, (2) quantity-flexibility contracts, and (3) multiple sourcing. We use a multiplicative demand process to model the evolutionary dynamics of demand uncertainty. We then quantify the impact of key modeling parameters for each operational-flexibility strategy on the waste ratio, which is measured as the ratio of excess inventory when a certain operational-flexibility strategy is employed to the amount when an offshore supplier is utilized without any operational flexibility. We find that the lead-time reduction strategy has the maximum capability to reduce waste in the sourcing process of buyers, followed by the quantity-flexibility and multiple-sourcing strategies, respectively. Thus, our results indicate that operational-flexibility strategies that rely on the localization of production are key to reducing waste and improving environmental sustainability at source.

The risk assessment of manufacturing supply chains based on Bayesian networks with uncertainty of demand

The great changes in the external environment of the manufacturing supply chain make its demand more complex and difficult to control. This paper takes China as an example. According to questionnaire survey and principal component analysis, the risk indicators caused by uncertain demand are screened and classified to construct evaluation system and complete risk identification. The Bayesian network integrating fuzzy set theory and left and right fuzzy ranking is used to explore the relationship between risk indicators and supply chain to achieve risk evaluation. In view of the highest risk factors, an incentive mechanism model based on information sharing is put forward to prove theoretically that information sharing is an important strategy to reduce risk. The results are as follows: The uncertain demand will lead to a high level of risk in China’s manufacturing supply chain, in which the level of information technology is the biggest cause. Only when manufacturing enterprises are willing to share information and other node enterprises join the information sharing team, can demand uncertainty be fundamentally reduced. The proposed risk assessment model realizes the method innovation and theoretical innovation. It can practical and effectively help relevant enterprises to determine and control risks.