Multicriteria pickup and delivery problem with transfer opportunity
In this research, we develop a multiobjective vehicle routing and scheduling heuristic for a pickup and delivery problem. The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to trans...
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Veröffentlicht in: | Computers & industrial engineering 1996-09, Vol.30 (4), p.631-645 |
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description | In this research, we develop a multiobjective vehicle routing and scheduling heuristic for a pickup and delivery problem. The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to transfer between vehicles. The objectives of scheduling are to minimize vehicle expense, tardiness and travel time. We propose a concurrent scheduling approach, which allocates customers to more than one vehicle and assigns more than one customer to a vehicle at a time. It differs from the usual concurrent approach in three aspects: (i) it uses the look-ahead strategy to construct miniroute; (ii) it adopts the head/tail, head, and tail integration techniques; and (iii) it allows interactivity. The procedure takes full advantage of due time and travel time information and is implemented through a computer program. It is a one-phase heuristic that can be reiterated when necessary. We provide detailed programming procedures and present the computational results of the proposed algorithm through the real data. |
doi_str_mv | 10.1016/0360-8352(95)00181-6 |
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The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to transfer between vehicles. The objectives of scheduling are to minimize vehicle expense, tardiness and travel time. We propose a concurrent scheduling approach, which allocates customers to more than one vehicle and assigns more than one customer to a vehicle at a time. It differs from the usual concurrent approach in three aspects: (i) it uses the look-ahead strategy to construct miniroute; (ii) it adopts the head/tail, head, and tail integration techniques; and (iii) it allows interactivity. The procedure takes full advantage of due time and travel time information and is implemented through a computer program. It is a one-phase heuristic that can be reiterated when necessary. 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We provide detailed programming procedures and present the computational results of the proposed algorithm through the real data.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Comparative studies</subject><subject>Computer programming</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Heuristic</subject><subject>Mathematical models</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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Combinatorial problems</topic><topic>Heuristic</topic><topic>Mathematical models</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Routing</topic><topic>Scheduling</topic><topic>Transportation services</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shang, Jen S.</creatorcontrib><creatorcontrib>Cuff, Carolyn K.</creatorcontrib><collection>Pascal-Francis</collection><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>Computers & industrial engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shang, Jen S.</au><au>Cuff, Carolyn K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multicriteria pickup and delivery problem with transfer opportunity</atitle><jtitle>Computers & industrial engineering</jtitle><date>1996-09-01</date><risdate>1996</risdate><volume>30</volume><issue>4</issue><spage>631</spage><epage>645</epage><pages>631-645</pages><issn>0360-8352</issn><eissn>1879-0550</eissn><coden>CINDDL</coden><abstract>In this research, we develop a multiobjective vehicle routing and scheduling heuristic for a pickup and delivery problem. The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to transfer between vehicles. The objectives of scheduling are to minimize vehicle expense, tardiness and travel time. We propose a concurrent scheduling approach, which allocates customers to more than one vehicle and assigns more than one customer to a vehicle at a time. It differs from the usual concurrent approach in three aspects: (i) it uses the look-ahead strategy to construct miniroute; (ii) it adopts the head/tail, head, and tail integration techniques; and (iii) it allows interactivity. The procedure takes full advantage of due time and travel time information and is implemented through a computer program. It is a one-phase heuristic that can be reiterated when necessary. 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ispartof | Computers & industrial engineering, 1996-09, Vol.30 (4), p.631-645 |
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source | ScienceDirect Journals (5 years ago - present) |
subjects | Algorithms Applied sciences Comparative studies Computer programming Exact sciences and technology Flows in networks. Combinatorial problems Heuristic Mathematical models Operational research and scientific management Operational research. Management science Routing Scheduling Transportation services |
title | Multicriteria pickup and delivery problem with transfer opportunity |
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