scholarly journals A New Optimization Technique for the Location and Routing Management in Agricultural Logistics

2020 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Chalermchat Theeraviriya ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan ◽  
Sasitorn Kaewman ◽  
Monika Kosacka-Olejnik
Keyword(s):  
Processing Time ◽  
Agricultural Sector ◽  
Cost Minimization ◽  
Optimization Technique ◽  
Fuel Cost ◽  
Problem Size ◽  
Routing Problem ◽  
Solution Approach ◽  
Fuel Cost Minimization

This paper aims to solve the location and routing problem (LRP) in the agricultural sector with the objective function of fuel cost minimization. Many farmers may have problems when transporting and selling products because of high costs and unfair prices. The proper location of standardized procurement centers and suitable routes will relieve farmers’ problems. This paper includes a realistic constraint that a farm can be visited to collect product more than once. A mathematical model was formulated to be solved by Lingo software, but when the problem size was larger, Lingo was unable to solve the problem within a reasonable processing time. The variable neighborhood strategy adaptive search (VaNSAS) is proposed to solve this LRP. The main contributions of this paper are a real case study problem and the first introduction of VaNSAS. Furthermore, the different combinations of the solution approach are proposed to prove which combination is the best algorithm. The computational results show that VaNSAS can find the solutions for all problem sizes in much less processing time compared to Lingo. In medium and large-sized instances, the VaNSAS can reduce processing times by 99.91% and 99.86%, respectively, from solutions obtained by Lingo. Finally, the proposed VaNSAS has been deployed in a case study problem to decide the best locations and transportation routes with the lowest fuel cost.

scholarly journals Optimization of medical waste routing problem: The case of TRB1 region in Turkey

2019 ◽  
Vol 9 (2) ◽  
pp. 197-207
Author(s):  
Suleyman Mete ◽  
Faruk Serin
Keyword(s):  
Environmental Sustainability ◽  
Fundamental Problem ◽  
Medical Waste ◽  
Future Research ◽  
Risk Of Infection ◽  
Routing Problem ◽  
Solution Approach ◽  
Potential Risks ◽  
Health Institutions

A fundamental problem concerning medical waste disposal is the evaluation of the real and potential risks arising from waste with the focus on the risk of infection. Therefore, the optimization of medical waste routing from collection to disposal center can minimize the risk of infection. The routing of medical waste considers significant to determine potential routes and select the route with minimum distance. The management of the medical waste is important decision for environmental sustainability and includes the collection, transportation and disposal of these materials. In this paper, a geographic information system (GIS) solution approach is proposed to determine the best location of disposal center. Proposed approach is applied to medical waste transportation between 167 health institutions (collection centers) and predetermined 5 disposal centers through TRB1 region in Turkey, which consist of Malatya, Elaz??, Bingöl and Tunceli provinces. The results of case study are examined and suggestions for future research are provided.

Optimal Power Flow and Optimal Reactive Power Dispatch Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 117-157
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Cost Minimization ◽  
Voltage Profile ◽  
Fuel Cost ◽  
Loss Minimization ◽  
Optimal Reactive Power Dispatch ◽  
Reactive Power Dispatch ◽  
Fuel Cost Minimization

This chapter describes grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) algorithms to solve both single and multi-objective optimal power flow (MOOPF) and optimal reactive power dispatch (ORPD) problems while satisfying various operational constraints. The proposed HCRO approach along with GWO, TLBO, BBO, KHA, and CRO algorithms are implemented on IEEE 30-bus system to solve four different single objectives: fuel cost minimization, system power loss minimization, voltage stability index minimization, and voltage deviation minimization; two bi-objectives optimization, namely minimization of fuel cost and transmission loss; minimization of fuel cost and voltage profile; and one tri-objective optimization, namely minimization of fuel cost, minimization of transmission losses, and improvement of voltage profile simultaneously. The simulation results clearly suggest that the proposed is able to provide a better solution than other approaches.

scholarly journals Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

2018 ◽  
Vol 12 (1) ◽  
pp. 168 ◽  
Author(s):  
Z.M. Yasin ◽  
N.F.A. Aziz ◽  
N.A. Salim ◽  
N.A. Wahab ◽  
N.A. Rahmat
Keyword(s):  
Carbon Emission ◽  
Search Algorithm ◽  
Cost Minimization ◽  
Operating Cost ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Multiobjective Particle Swarm Optimization ◽  
Fuel Cost Minimization

In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.

scholarly journals Model relaxations for the fuel cost minimization of steady-state gas pipeline networks

2000 ◽  
Vol 31 (2-3) ◽  
pp. 197-220 ◽  
Author(s):  
Suming Wu ◽  
R.Z. Ríos-Mercado ◽  
E.A. Boyd ◽  
L.R. Scott
Keyword(s):  
Steady State ◽  
Cost Minimization ◽  
Gas Pipeline ◽  
Fuel Cost ◽  
Pipeline Networks ◽  
Fuel Cost Minimization
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