Simulation and Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem

This study proposes a novel methodology towards using ant colony optimization (ACO) with stochastic demand. In particular, an optimizationsimulation-optimization approach is used to solve the Stochastic uncapacitated location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. ACO is modeled using discrete event simulation to capture the randomness of customers’ demand, and its objective is to optimize the costs. On the other hand, the simulated ACO’s parameters are also optimized to guarantee superior solutions. This approach’s performance is evaluated by comparing its solutions to the ones obtained using deterministic data. The results show that simulation was able to identify better facility allocations where the deterministic solutions would have been inadequate due to the real randomness of customers’ demands

The obnoxious facilities planar p-median problem

In this paper we propose the planar obnoxious p-median problem. In the p-median problem the objective is to find p locations for facilities that minimize the weighted sum of distances between demand points and their closest facility. In the obnoxious version we add constraints that each facility must be located at least a certain distance from a partial set of demand points because they generate nuisance affecting these demand points. The resulting problem is extremely non-convex and traditional non-linear solvers such as SNOPT are not efficient. An efficient solution method based on Voronoi diagrams is proposed and tested. We also constructed the efficient frontiers of the test problems to assist the planers in making location decisions

Algorithmic and technical improvements: Optimal solutions to the (Generalized) Multi-Weber Problem

Rosing has recently demonstrated a new method for obtaining optimal solutions to the (Generalized) Multi-Weber Problem and proved the optimality of the results. The method develops all convex hulls and then covers the destinations with disjoint convex hulls. This paper seeks to improve implementation of the algorithm to make such solutions economically attractive. Four areas are considered: sharper decision rules to eliminate unnecessary searching, bit pattern matching as a method of recording a history and eliminating duplication, vector intrinsic functions to speed up comparisons, and profiling a program to maximize operating efficiency. Computational experience is also presented

A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies

Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)

Environmental concerns and energy security have led governments to establish legislations to convertConventional Petroleum Supply Chain (CPSC) to Sustainable Petroleum Supply Chain (SPSC). The United States(US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and con-sumption for the last half century. Though these regulations have created new opportunities, they have also added newburdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to studythe impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM),which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policyimpact on the SPSC using cellulosic ethanol. The model, as any other general model available in the literature, makesit highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derivea Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently byarriving at robust managerial insights much faster. We present a case study of the policy impact on the SPSC in theState of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak 2020)

A new local search for . . .

This paper presents a new local search approach for solving continuous location problems. The main idea is to exploit the relation between the continuous model and its discrete counterpart. A local search is first conducted in the continuous space until a local optimum is reached. It then switches to a discrete space that represents a discretisation of the continuous model to find an improved solution from there. The process continues switching between the two problem formulations until no further improvement can be found in either. Thus, we may view the procedure as a new adaption of formulatio

Extensions du problème de Weber

Le problème de weber sur la sphère -- Le problème de weber avec coûts concaves -- Le problème de weber avec contraintes de localisation et de passage -- Le problème de weber multi-sources -- Le problème de weber multi-sources avec contraintes de localisation et de passage