Integer programming models for the semi-obnoxious p-median problem

The p-median problem concerns the location of facilities so that the sum of distances between the demand points and their nearest facility is minimized. We study a variant of this classic location problem where minimum distance constraints exist both between the facilities and between the facilities and the demand points. This specific type of problem can be used to model situations where the facilities to be located are semi-obnoxious. But despite its relevance to real life scenarios, it has received little attention within the vast literature on location problems. We present twelve ILP models for this problem, coupling three formulations of the p-median problem with four formulations of the distance constraints. We utilize Gurobi Optimizer v9.0.3 in order to compare these ILP models on a large dataset of problems. Experimental results demonstrate that the classic p-median model proposed by ReVelle \& Swain and the model proposed by Rosing et al. are the best performers

Pivoting rules for the revised simplex algorithm

Pricing is a significant step in the simplex algorithm where an improving nonbasic variable is selected in order to enter the basis. This step is crucial and can dictate the total execution time. In this paper, we perform a computational study in which the pricing operation is computed with eight different pivoting rules: (i) Bland’s Rule, (ii) Dantzig’s Rule, (iii) Greatest Increment Method, (iv) Least Recently Considered Method, (v) Partial Pricing Rule, (vi) Queue Rule, (vii) Stack Rule, and (viii) Steepest Edge Rule; and incorporate them with the revised simplex algorithm. All pivoting rules have been implemented in MATLAB. The test sets used in the computational study are a set of randomly generated optimal sparse and dense LPs and a set of benchmark LPs (Netliboptimal, Kennington, Netlib-infeasible)