Robust supply chain network design: an optimization model with real world application

Zokaee, Shiva; Jabbarzadeh, Armin; Fahimnia, Behnam; Sadjadi, Seyed Jafar

doi:10.1007/s10479-014-1756-6

Cited by 98 publications

(37 citation statements)

References 35 publications

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“…Optimizing a nonlinear system is quite challenging as the output solution is not unique and simply a linear combination of the independent parts [4]. So while mathematical models should be helpful to explore the space of possible strategies and propose optimal solutions when operations become complex [5], solving such models becomes more difficult as the number of strategies and considered variables increases [6].…”

Section: Introductionmentioning

confidence: 99%

Freight Time and Cost Optimization in Complex Logistics Networks

et al. 2020

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The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to the large number of variables and nonlinear dependencies involved. Here, we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer’s demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. In addition, using customer logistics and the k-means algorithm, we propose additional warehouse locations. For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.

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Section: Introductionmentioning

confidence: 99%

Freight Time and Cost Optimization in Complex Logistics Networks

et al. 2020

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“…In this paper, for the modeling of the uncertainty in the demand parameter, the approach used by Thiele [40] and Zokaee et al [43] is utilized. It should be mentioned that the parameter DF git has uniform symmetrical and independent distribution within the range of [DF git − DF git , DF git + DF git ].…”

Section: Robust Modelmentioning

confidence: 99%

“…Considering χ * i as the robust solution to the model, the probabilistic guarantee of the robust solution feasibility corresponding to the ith constraint is determined by Zokaee [43]:…”

Section: Robust Modelmentioning

confidence: 99%

Customer relationship management and new product development in designing a robust supply chain

2020

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New product development is a basic requirement for any company to survive in the competitive market. Most of the organizations realized that relying solely on the traditional competitive levers, such as increasing the quality, reducing costs, and distinguished provision of goods and services is not enough anymore. In addition to the aforementioned competitive advantages, a company needs to introduce new products and out-phase the old ones at the right time. In this research, the design of a supply chain network considering new product development is investigated. Here, the notion of customer relationship management is incorporated into the proposed mathematical model. Moreover, product demand that is inherently uncertain in the real situations is embedded in the proposed robust model. Objectives of new products development, and customer satisfaction along with maximization of the profit is considered in the model. Moreover, an improved multi-choice goal programming method is implemented to solve the model. Finally, the model performance is evaluated for a real-world case.

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“…The relative importance of the globalization aspects has decreased while the number of papers devoted to environmental aspects and reverse (5) Altmann (2014), Brandenburg (2015), Cui (2015), Klibi and Martel (2012) Note: The number of papers for each specific problem is given in parenthesis. See key from Table 3.1 for abbreviations 3.4 Focus Areas for Supply Chain Configuration (7) Brandenburg (2015), Castellano et al (2013), Petridis (2015), Tian and Yue (2014), Tiacci et al (2014), You and Grossmann (2008), Zokaee et al (2014) ENV (9) Altmann ( (7) Baghalian et al (2013), Bassett and Gardner (2013), Habermann et al (2015), Hearnshaw and Wilson (2013), Klibi and Martel (2012), Madadi et al (2014), Mizgier et al (2015) RL (12) Amin and Zhang (2012), Chaabane et al (2012), Das and Rao Posinasetti (2015), Ghayebloo et al (2015), Kara and Onut (2010), Metta andBadurdeen (2013), Quariguasi Frota Neto et al (2008), Ramezani et al (2013), Ramezani et al (2014), Santibanez-Gonzalez and, Soleimani et al (2013), Vahdani and Naderi-Beni (2014) RP (6) Akanle and Zhang (2008), Bottani andMontanari (2010), Cheung et al (2012), Jamshidi et al (2015), Vanteddu et al (2011), You and…”

Section: Focus Areas For Supply Chain Configurationmentioning

confidence: 99%