scholarly journals Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat
2017 ◽  
Vol 10 (5) ◽  
pp. 853
Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat

Purpose: Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management.Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move.Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively.Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.


2013 ◽  
Vol 791-793 ◽  
pp. 1176-1179
Author(s):  
Yan Fen Jiang ◽  
Chun Ling Feng

The location routing problem (LRP), which simultaneously tackles both facility location and the vehicle routing decisions to minimize the total system cost, is of great importance in designing an integrated logistic distribution network. In this paper a simulated annealing algorithm (SA) based hybrid genetic algorithm was developed to solve the LRP with capacity constraints (CLRP) on depots and routes. The proposed hybrid genetic algorithm modified the population generation method, genetic operators and recombination strategy and realized the combination of the local searching ability of SA and global searching ability of GA. To evaluate the performance of the proposed approach, we conducted an experimental study and compared its results with other heuristics on a set of well-known Barreto Benchmark instances. The experimental results verified the feasibility and effectiveness of our approach.


2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.


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