Comparisons of some improving strategies on MOPSO for multi-objective (r,Q) inventory system
► Some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system presents in this paper. The MOPSO algorithms created using these strategies...
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| Veröffentlicht in: | Expert systems with applications 2011-09, Vol.38 (10), p.12051-12057 |
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| description | ► Some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system presents in this paper. The MOPSO algorithms created using these strategies are evaluated and compared with each other. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.
This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions. |
| doi_str_mv | 10.1016/j.eswa.2011.01.169 |
| format | Article |
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This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2011.01.169</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Convergence ; Crowding distance ; Inventory controls ; Inventory planning and control ; Mathematical models ; Multi-objective particle swarm optimization ; Optimization ; Pareto optimality ; Searching ; Strategy ; ε-Dominance</subject><ispartof>Expert systems with applications, 2011-09, Vol.38 (10), p.12051-12057</ispartof><rights>2011 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c365t-a141b5fcdab06418afcbd47a0a0e569173096b98383976f2609478d33149ce33</citedby><cites>FETCH-LOGICAL-c365t-a141b5fcdab06418afcbd47a0a0e569173096b98383976f2609478d33149ce33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2011.01.169$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Moslemi, H.</creatorcontrib><creatorcontrib>Zandieh, M.</creatorcontrib><title>Comparisons of some improving strategies on MOPSO for multi-objective (r,Q) inventory system</title><title>Expert systems with applications</title><description>► Some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system presents in this paper. The MOPSO algorithms created using these strategies are evaluated and compared with each other. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.
This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.</description><subject>Algorithms</subject><subject>Convergence</subject><subject>Crowding distance</subject><subject>Inventory controls</subject><subject>Inventory planning and control</subject><subject>Mathematical models</subject><subject>Multi-objective particle swarm optimization</subject><subject>Optimization</subject><subject>Pareto optimality</subject><subject>Searching</subject><subject>Strategy</subject><subject>ε-Dominance</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wFNuKrhrptlNNuBFil-gVLFHIWSzs5LS3dRkW-m_N6WePc1hnnc-HkLOgeXAQNwscow_Jp8wgJxBDkIdkBFUkmdCKn5IRkyVMitAFsfkJMYFYyAZkyPyOfXdygQXfR-pb2n0HVLXrYLfuP6LxiGYAb8cpmZPX2dvHzPa-kC79XJwma8XaAe3QXoZrt-vqOs32A8-bGncxgG7U3LUmmXEs786JvOH-_n0KXuZPT5P714yy0U5ZAYKqMvWNqZmooDKtLZuCmmYYVgKBZIzJWpV8YorKdqJYKqQVcM5FMoi52NysR-brv5eYxx056LF5dL06NdRV5UqJhUXkMjLf8kkJelMe8qETvaoDT7GgK1eBdeZsNXA9M65Xuidc71zrhno5DyFbvchTN9uHAYdrcPeYuNCMqUb7_6L_wLUIYoa</recordid><startdate>20110915</startdate><enddate>20110915</enddate><creator>Moslemi, H.</creator><creator>Zandieh, M.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110915</creationdate><title>Comparisons of some improving strategies on MOPSO for multi-objective (r,Q) inventory system</title><author>Moslemi, H. ; Zandieh, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-a141b5fcdab06418afcbd47a0a0e569173096b98383976f2609478d33149ce33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Convergence</topic><topic>Crowding distance</topic><topic>Inventory controls</topic><topic>Inventory planning and control</topic><topic>Mathematical models</topic><topic>Multi-objective particle swarm optimization</topic><topic>Optimization</topic><topic>Pareto optimality</topic><topic>Searching</topic><topic>Strategy</topic><topic>ε-Dominance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moslemi, H.</creatorcontrib><creatorcontrib>Zandieh, M.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moslemi, H.</au><au>Zandieh, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparisons of some improving strategies on MOPSO for multi-objective (r,Q) inventory system</atitle><jtitle>Expert systems with applications</jtitle><date>2011-09-15</date><risdate>2011</risdate><volume>38</volume><issue>10</issue><spage>12051</spage><epage>12057</epage><pages>12051-12057</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>► Some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system presents in this paper. The MOPSO algorithms created using these strategies are evaluated and compared with each other. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.
This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2011.01.169</doi><tpages>7</tpages></addata></record> |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Convergence Crowding distance Inventory controls Inventory planning and control Mathematical models Multi-objective particle swarm optimization Optimization Pareto optimality Searching Strategy ε-Dominance |
| title | Comparisons of some improving strategies on MOPSO for multi-objective (r,Q) inventory system |
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