HYBRID HARMONY SEARCH ALGORITHM DAN SIMULATED ANNEALING ALGORITHM UNTUK MENYELESAIKAN UNCAPACITATED FACILITY LOCATION PROBLEM - Andri Ade Putra (SCIENCE MATH: Jurnal Ilmu Sains dan Matematika, 2025)
The Uncapacitated Facility Location Problem (UFLP) is a problem that aims to find the optimal location for building facilities where these facilities will serve a number of m customers. Additionally, there are a set of n locations available to build n facilities. In this problem, it is assumed that the facilities have no capacity limitations in serving customers, meaning that all demands from each customer are served by a single facility, and there is only one facility at each location. The objective function of the UFLP is to minimize the total cost. This research aims to apply the Harmony Search (HS) algorithm and Simulated Annealing (SA) algorithm to solve the Uncapacitated Facility Location Problem (UFLP). The HS algorithm, developed by Geem et al. (2001), is a method based on the process of a musician searching for the right harmony through three modes of note searching: replacing the old note, modifying the old note slightly, or changing the old note based on the musician's memory. The SA algorithm, developed by Dreo et al. (2006), mimics the annealing process in crystal hardening. To produce high-quality crystals, the temperature must be lowered, then slightly raised before being lowered again. The solution to this problem involves several processes, including initializing parameters such as Harmony Memory Size (HMS), Harmony Memory Considering (or Accepting) Rate (HMCR), Pitch Adjusting Rate (PAR), bandwidth, initial temperature , temperature decay rate (), final temperature , and maximum iterations; then generating the Harmony Memory (HM) population, calculating the objective function values for HM, evaluating the objective function for HM, modifying the worst individual in HM, checking the modification results, applying the SA modification, checking the SA modification results, determining the best solution in HM, checking for the maximum iterations, and finally calculating the total cost.