At present, the most commonly used satisficing method for multi-objective linear programming (MOLP) is goal programming (GP) based methods but these methods do not always generate efficient solutions. Recently, an efficient GP-based method, which is called reference goal programming (RGP), has been proposed. However, it is limited to only a triangular preference. The more flexible preferences such a convex polyhedral type is preferred in many practical problems. In this research, a satisfactory effective linear coordination method for MOLP problems with convex polyhedral preference functions is proposed. It can be solved by existing linear programming solvers and can find all of the efficient solutions, which satisfy decision maker's requirements. The convex polyhedral function enriches the existing preferences for efficient methods and increases the flexibility in designing preferences.
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