Identifying ambient service location problems and its application using a humanized computing model

Abstract

The purpose of this study is to identify a humanized computing model for solving ambient service location problems with a developed heuristic algorithm. In real situations, space constraints may result in a practical facility location planning problem in an urban environment. Because of space constraints, large warehouse facilities may not be allocated for a single location; and they need to be partitioned and placed at different locations with synergy and collaboration patterns to fulfill orders for many retail facilities. Therefore, properly locating different types of warehouse facilities to make logistics and supply efficient is important. This study considers the multiple-distinct facilities service location problems (MDFSLP) that are constrained in four attributes of single sourcing, p-median, p-dispersion, and hierarchical location. Although past research has discussed the same types of warehouse location problems, the MDFSLP has sparsely been explored and reviewed. Thus, this study proposes a fast-iterated local search (FSILS) algorithm to solve the problem efficiently. The results show that the FSILS algorithm contributes to the better efficiency of computational time than other listed similar approaches and are of high importance in a realistic location environment to promote industry development on business strategies.