توسعهﻱ مدل مكان‌يابي ـ تخصيص بيشينه پوشش سلسله‌مراتبي اشتراكي

Cooperative Hierarchical Maximal Covering Location-Allocation Problem

در مدلهاي مرسوم مكان‌يابي پوشش، هر نقطه‌ي تقاضا تنها ميتواند با يك وسيله پوشش داده شود. با پوشش اشتراكي كه يكي از روشهاي توسعه? اين مدلهاست، هر نقطه‌ي تقاضا را ميتوان با يك يا چند وسيله پوشش‌ داد. در اين نوشتار مفهوم «پوشش اشتراكي» به مدل مكان‌يابي بيشينه پوشش سلسله‌مراتبي با ارجاع گسسته در دو حالت بدون تخصيص براي سيگنالهاي فيزيكي و با تخصيص براي سيگنالهاي غير فيزيكي توسعه داده شده و سپس مدل پيشنهادي در مثالهاي عددي تحليل ميشود. در ادامه، الگوريتم فراابتكاري تبريد شبيهسازي شده براي حل مدل پيشنهادي در مثالهايي با ابعاد بزرگ ارايه ميشود. الگوريتم ارايه شده، براي جست‌وجوي چندين استقرار سطح پايين و انتخاب بهترين آنها در هر جابه‌جايي وسايل سطح بالا، قابل تنظيم است. مقايسهي نتايج الگوريتم با روش حل دقيق نشان ميدهد كه عملكرد الگوريتم در مسايلي با ابعاد بزرگ مناسب است و در زماني كوتاه به جواب نزديك بهينه ميرسد.

In classic covering location models, each demand point can be covered by only one facility. In cooperative covering problems, each demand point can be covered by one or more facilities. As an application of cooperative models, each facility sends signals out so that signal intensity decreases by an increase in distance. In the hierarchical maximal covering location problem (HMCLP), a fixed number of facilities with different servicing levels is located in order to maximize covered demands. In this paper, the cooperative covering concept is developed by the HMCLP with referral (HMCLP(R)) in a discrete space. It is assumed that there are two level facilities and the model is nested, so, high-level facilities provide both types of service. Each demand point is covered if its high-level demands are provided by high-level facilities directly or with referral from low-level facilities. The proposed model is presented in two forms: CHMCLP(R) for physical signals and CHMCLAP(R) for non-physical signals, while the second one considers the allocation structure as well. The proposed models are analyzed using numerical examples. The analysis shows that the covering radii have important roles to play in the performance of the developed models. For instance, with very small referral covering radius, each low level facility is located around and near a high-level facility. So, low level facilities are covered in a non-cooperative manner by the high-level facilities, and, moreover, they cover less demand points. Therefore, covering radii should be determined carefully by considering the problem, facility specifications and other determinant factors. A simulated annealing (SA) algorithm was developed and tuned for solving the proposed models in large-scaled instances. The developed algorithm was implemented using randomly generated problems with different sizes. Comparisons between results of the solution algorithm and an exact solution approach show the efficiency of the proposed solution algorithm.