Change search
Refine search result
1 - 3 of 3
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Karakitsiou, Athanasia
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Migdalas, Athanasios
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Locating facilities in a competitive environment2017In: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480, Vol. 11, no 5, p. 929-945Article in journal (Refereed)
    Abstract [en]

    The research work dealing with the bi-level formulation of location problems is limited only to the competition among the locators, that is, it is supposed that either both the locator and the allocator are the same or the customer knows the optimality criterion of the locator and agrees passively with it. Customers’ preferences as well as externalities (such as road congestion, facility congestion, emissions etc) caused by the location decisions are either ignored or controlled by incorporating constraints in order to ensure the achievement of a predetermined target. However, this approach treats customers as irresolute beings. Thus, if, for example, the customers travel to the facilities to obtain the offered service, then there is no compulsion or intensive for them to attend the designated facility. This means that, once the facilities are open, what the locator wishes the customers to do may not coincide with their own wish and behavior. We suppose that the customers are involved in a Nash game in order to ensure what they conceive as the best level of services for themselves. In order to take into consideration the effects of such competition in the facilities location decisions we propose a bi-level programming approach to the problem.

  • 2.
    Marinakis, Yannis
    et al.
    Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete.
    Migdalas, Athanasios
    Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete.
    Pardalos, Panos M.
    Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida.
    Expanding neighborhood search-GRASP for the probabilistic traveling salesman problem2008In: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480, Vol. 2, no 3, p. 351-361Article in journal (Refereed)
    Abstract [en]

    The Probabilistic Traveling Salesman Problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In probabilistic traveling salesman problem only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a variant of the well-known Greedy Randomized Adaptive Search Procedure (GRASP), the Expanding Neighborhood Search-GRASP, is proposed for the solution of the probabilistic traveling salesman problem. expanding neighborhood search-GRASP has been proved to be a very efficient algorithm for the solution of the traveling salesman problem. The proposed algorithm is tested on a numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in six out of ten cases the proposed algorithm gives a new best solution

  • 3.
    Pei, Jun
    et al.
    School of Management, Hefei University of Technology.
    Liu, Xinbao
    School of Management, Hefei University of Technology.
    Pardalos, Panos M.
    Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville.
    Li, Kai
    School of Management, Hefei University of Technology.
    Fan, Wenjuan
    School of Management, Hefei University of Technology.
    Migdalas, Athanasios
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Single-machine serial-batching scheduling with a machine availability constraint, position-dependent processing time, and time-dependent set-up time2017In: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480, Vol. 11, no 7, p. 1257-1271Article in journal (Refereed)
    Abstract [en]

    This article considers the single-machine serial-batching scheduling problem with a machine availability constraint, position-dependent processing time, and time-dependent set-up time. The objective of this problem is to make the decision of batching jobs and sequencing batches to minimize the makespan. To solve the problem, three cases of machine non-availability periods are considered, and the structural properties of the optimal solution are derived for each case. Based on these structural properties, an optimization algorithm is developed and an example is proposed to illustrate this algorithm

1 - 3 of 3
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf