Particle Swarm Optimization with Group Decision Making

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively. Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

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Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539317400010 ◽

2017 ◽

Vol 24 (06) ◽

pp. 1740001 ◽

Cited By ~ 5

Author(s):

Amit Kumar ◽

T. V. Vijay Kumar

Keyword(s):

Decision Making ◽

Particle Swarm Optimization ◽

Response Times ◽

Particle Swarm ◽

Strategic Decision ◽

Experimental Comparison ◽

Selection Algorithm ◽

Interaction Operator ◽

Swarm Optimization ◽

View Selection

A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.

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Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411918794721 ◽

2018 ◽

Vol 232 (9) ◽

pp. 930-948 ◽

Cited By ~ 2

Author(s):

Javad Ansarifar ◽

Reza Tavakkoli-Moghaddam ◽

Faezeh Akhavizadegan ◽

Saman Hassanzadeh Amin

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Particle Swarm Optimization ◽

Heuristic Algorithms ◽

Particle Swarm ◽

Operating Rooms ◽

Programming Approach ◽

Test Problems ◽

Swarm Optimization ◽

Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

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A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches ◽

10.4018/978-1-4666-2145-9.ch001 ◽

2012 ◽

pp. 1-16

Author(s):

Rahul Roy ◽

Satchidananda Dehuri ◽

Sung Bae Cho

Keyword(s):

Decision Making ◽

Particle Swarm Optimization ◽

Combinatorial Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective ◽

Discrete Values ◽

Continuous Domain

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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