The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach

Karaoğlan, İsmail; Altıparmak, Fulya; Kara, İmdat; Dengız, Berna

doi:10.1016/j.omega.2011.09.002

Cited by 136 publications

(77 citation statements)

References 56 publications

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“…Albareda-Sambola et al (2012) proposed a multi-period LRP model and a stable model against time. An approximation based on replacing vehicle routes by spanning trees is proposed, and its capability for providing good quality solutions is assessed in a series of computational experiments Karaoglan et al (2012) used goods pick and delivery planning in LRP. The authors proposed two polynomial-size mixed integer linear programming formulations for the problem and a family of valid inequalities to strengthen their formulation.…”

Section: Literature Reviewmentioning

confidence: 99%

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

2017

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Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.

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Section: Literature Reviewmentioning

confidence: 99%

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

2017

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“…The initial permutation π is (8,5,1,6,4,2,7,3). In the course of running the algorithm, the pickup demands of installations 6 and 7 are split.…”

Section: Algorithm Split1mentioning

confidence: 99%

“…In our description of the flights in schedule S Split1 (3) obtained after the sorting in Step 4, all passengers are collected from an installation, unless stated otherwise. Flight M 1 visits installations 8,6 (to pick up 3 passengers), then 5 and 1 with the risk 3 + 6 + 11 + 19 = 39. Flight M 2 takes 1 person from installation 6, then picks up 2 people from installation 7, and later visits installations 4 and 2; the total risk of that flight is 1 + 3 + 9 + 19 = 32.…”

Section: Algorithm Split1mentioning

confidence: 99%

“…For example, if for Example 1 the values in Table 1 are the delivery demands, then a possible flight schedule for the non-split scenario can be converted from that found by Algorithm SPTu: flight M 1 visits the installations in the order (1,5,8), for flights M 2 and M 3 the sequences are (4,6) and (3,7), respectively, while flight M 4 visits installation 2 alone. To visualize such a schedule, reverse the order of the bars for each flight in Figure 1.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…But in all known publications on helicopter planning to offshore installations the problem is defined as a vehicle routing problem (VRP) with traditional cost or distance objectives. The vehicle routing problem is well-studied, and numerous computational techniques are available for its different variants, see for example [3], [4], [5], [6], [7], [8], [9] and [10] for the recent work.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Minimization of passenger takeoff and landing risk in offshore helicopter transportation: Models, approaches and analysis

Qian

Strusevich

Gribkovskaia

et al. 2015

Omega

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Offshore petroleum industry uses helicopters to transport the employees to and from installations. Takeoff and landing represent a substantial part of the flight risks for passengers. In this paper, we propose and analyze approaches to create a safe flight schedule to perform pickup of employees by several independent flights. Two scenarios are considered. Under the non-split scenario, exactly one visit is allowed to each installation. Under the split scenario, the pickup demand of an installation can be split between several flights. Interesting links between our problem and other problems of combinatorial optimization, e.g., parallel machine scheduling and bin-packing are established. We provide worst-case analysis of the performance of some of our algorithms and report the results of computational experiments conducted on randomly generated instances based on the real sets of installations in the oil fields on the Norwegian continental shelf. This paper is the first attempt to handle takeoff and landing risk in a flight schedule that consists of several flights and lays ground for the study on more advanced and practically relevant models.

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Heuristics for a hub location‐routing problem

Lopes

Andrade²,

Queiroz

et al. 2016

Networks

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We investigate a variant of the many‐to‐many hub location‐routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random‐key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch‐and‐cut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min‐cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large‐sized instances, while the branch‐and‐cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(1), 54–90 2016

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The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach

Cited by 136 publications

References 56 publications

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

Minimization of passenger takeoff and landing risk in offshore helicopter transportation: Models, approaches and analysis

Heuristics for a hub location‐routing problem

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