Supply chain optimization with variable demand by considering financial criteria and scenarios

Abstract

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

“…Besides, several objectives must be considered as the key performance of the supply chain, such as those proposed by Refs. [ [111] , [112] , [113] , [114] , [115] ]. Finally, methodologies to solve stochastic supply chain models could be considered, such as the Sample Average Approximation (SAA) ([ [116] , [117] , [118] , [119] , [120] ]) and the scenario-based method ([124]).…”

Optimisation of multi-objective supply chain networks considering cost minimisation and environmental criteria

“…There are algorithms and basic methods that have been defined using hierarchical analytical processes, fuzzy logic and genetic algorithms (Gursoy, 2010). Some of the common design activities include the locations of plants and warehouses, the selection of suppliers, inventory control, distribution schedules, product dispatch periods, supply strategies, and the planning of transportation routes (Escobar, 2012;Escobar et al, 2013a;Escobar, 2017;Apornak et al, 2018).…”

“…Robust supply networks with multiple links present uncertainties associated with the final consumers or the interfaces of suppliers in factors such as demand, prices and exchange rates. These uncertainties or variations produced by a lack of information, measurement errors, or attempts to project the future can directly affect the design of a network that addresses long-term decisions (Escobar, 2017). There are two methodologies developed for the design and operation of supply chains that consider demand uncertainty: the probabilistic approach and the scenario-based approach (Tsiakis et al, 2001).…”

“…The probabilistic approach requires knowledge of the probability distributions that have the random variables necessary to consider the uncertainty of the different parameters, which adds mathematical complexity (Escobar, 2012). The scenario-based approach consists of creating and defining a fixed quantity of possible scenarios that capture the uncertainty of the stochastic parameters, with the objective of obtaining a robust solution that works perfectly under all the probable scenarios (Escobar, 2017;Escobar et al, 2018).…”

Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area

“…Also, stochastic considerations must be incorporated into the proposed model. Solution strategies for the stochastic model such as Scenarios [43,44] or Sample Average Approximation [45,46] must be applied. Similarly, a heuristic method can be developed to solve problems with larger-scale instances and compare these with the results obtained in this paper.…”