Keyvan Roshan, Mehdi Seifbarghy, Davar Pishva,
Volume 28, Issue 4 (IJIEPR 2017)
Abstract
Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Since the developed model of the problem is of an NP-hard type, tri-meta-heuristic algorithms are proposed to solve the problem. Initially, Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) is proposed in order to solve the problem. To validate the results obtained, two popular algorithms namely, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, Taguchi method has been utilized to fine tune the parameters of all algorithms. The computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliable performances of the proposed methodology.
Abdolreza Roshani, Davide Giglio,
Volume 31, Issue 2 (IJIEPR 2020)
Abstract
Multi-manned assembly line balancing problems (MALBPs) can be usually found in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost-oriented version of MALBPs, namely, CMALBP, is addressed. This class of problems may arise in final assembly lines of products in which the manufacturing process is very labor-intensive. Since CMALBP is NP-Hard, a heuristic approach based on a tabu search algorithm is developed to solve the problem. The proposed algorithm uses two neighborhood generation mechanisms, namely swap and mutation, that effectively collaborate with each other to build new feasible solutions; moreover, two separate tabu lists (associated with the two generation mechanisms) are used to check if moving to a new generated neighbor solution is forbidden or allowed. To examine the efficiency of the proposed algorithm, some experimental instances are collected from the literature and solved. The obtained results show the effectiveness of the proposed tabu search approach.
