APPLICATION OF POSSIBILISTIC LINEAR PROGRAMMING TO MULTI-OBJECTIVE DISTRIBUTION PLANNING DECISIONS

ABSTRACT In real-world distribution planning decision (DPD) problems, the decision maker (DM) must simultaneously handle conflicting objectives, and input data and related parameters are often imprecise/fuzzy owing to incomplete and/or unavailable information. This work develops an interactive possibilistic linear programming (PLP) method for solving multi-objective DPD problems involving imprecise available supply, forecast demand and unit cost/time coefficients with triangular possibility distributions. The multi-objective PLP model designed here aims to simultaneously minimize the total distribution costs and the total delivery time with reference to available supply constraint at each source, as well as forecast demand and warehouse space constraints at each destination. Additionally, the interactive PLP method provides a systematic framework that facilitates the decision-making process, enabling a DM to interactively modify the imprecise data and related parameters until a satisfactory solution is obtained. An industrial case is presented to demonstrate the feasibility of applying the interactive PLP method to real DPD problems. Consequently, the PLP method yields a set of efficient compromise solutions and overall degree of DM satisfaction with the determined objective values. Especially, several significant finding relating to the practical application of the interactive PLP method are presented.