Abstract
This paper investigates various supply chain disruptions in terms of scenario planning, including node disruption and chain disruption; namely, disruptions in distribution centers and disruptions between manufacturing centers and distribution centers. Meanwhile, it also focuses on the simultaneous disruption on one node or a number of nodes, simultaneous disruption in one chain or a number of chains and the corresponding mathematical models and exemplification in relation to numerous manufacturing centers and diverse products. Robustness of the design of the supply chain network is examined by weighing efficiency against robustness during supply chain disruptions. Efficiency is represented by operating cost; robustness is indicated by the expected disruption cost and the weighing issue is calculated by the multi-objective firefly algorithm for consistency in the results. It has been shown that the total cost achieved by the optimal target function is lower than that at the most effective time of supply chains. In other words, the decrease of expected disruption cost by improving robustness in supply chains is greater than the increase of operating cost by reducing efficiency, thus leading to cost advantage. Consequently, by approximating the Pareto Front Chart of weighing between efficiency and robustness, enterprises can choose appropriate efficiency and robustness for their longer-term development.
Highlights
2014 witnessed the epidemic Ebola, affecting Cote d’Ivoire, the main supplier of cocoa to the Nestle Corporation
The studies mentioned above do not consider the robustness of supply chains; efficiency can be increased in addressing supply chain disruption if robustness is introduced in the design of supply chain network
The result is the continuous Pareto curve instead of a number of independent points, and the robustness and cost of supply chains corresponding to their efficiency can be derived
Summary
2014 witnessed the epidemic Ebola, affecting Cote d’Ivoire, the main supplier of cocoa to the Nestle Corporation. Shu et al (2014) [8] investigate risk control of production disruption in supply chains on the basis of Generic Bill of Materials (GBOM), and study the strategies related to the overall optimal profits achieved by enterprises when production is uncertain They make a hypothesis regarding corporate efficiency and the disruptions of market demand, and it is difficult to obtain the real and accurate disruption probability as a result of the uncertainty of supply chain disruptions. Yang (2008) [13] extends the firefly algorithm to address multi-objective problems and tests the validity of the algorithm by functions and applies it for designing benchmark optimization. This paper, for the first time, introduces the issue of weighing efficiency and robustness of supply chains disrupted by the multi-objective firefly algorithm, which is deployed to solve the weighting problem. The organization of this paper is as follows: the supply chain model is first constructed; the firefly algorithm and the multi-objective firefly algorithm are explicated in detail; the validity of the multi-objective firefly algorithm is tested; the multi-objective firefly algorithm is exemplified and simulated; node disruption and link disruption are considered at the same time; and the simulated results are analyzed
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