(573e) Multiobjective Optimisation of Global Supply Chain Planning Using ?-Constraint Method and Lexicographic Minimax Method

The performance of a supply chain should usually be measured by multiple criteria, in which cost, responsiveness, and customer service level are critical ones. Dealing with the global competition, how to establish a global supply chain network with reduced cost, improved responsiveness, and higher customer service level becomes a crucial issue to multinational firms. To represent the responsiveness of a supply chain, the total flow time is used, equal to the product of  product flow and the corresponding transportation time from formulation plants to markets. The customer service level is measured by the lost sales.

In this work, we address production, distribution and capacity planning problem of global supply chains, considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. In this model, two strategies for the formulation plants' capacity expansion are considered, proportional and cumulative capacity expansions, in which the maximum capacity increment is proportional to each plant’s capacity or all formulation plants’ cumulative capacity before expansion. The ε-constraint method is used to as a solution approach to tackle the multiobjective problem, which generates a Pareto surface for all objectives. Then, to obtain a fair solution with no preference to any objectives, the lexicographic minimax method is implemented as an alternative approach, and a new approach is developed to transfer lexicographic minimax problem to a minimisation problem.

Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches, and the solution differences between two capacity expansion strategies is discussed. Also, the solutions with the minimum total cost and the minimum flow time are compared.