Empowering Cash Managers Through Compromise Programming

2017 ◽  
pp. 149-173 ◽  
Author(s):  
Francisco Salas-Molina ◽  
David Pla-Santamaria ◽  
Juan A. Rodríguez-Aguilar
Keyword(s):  
Compromise Programming

Adaptation of a compromise programming approach for evaluating the localized impacts of water allocation

2021 ◽  
Author(s):  
Ebrahim Mokallaf Sarband ◽  
Shahab Araghinejad ◽  
Jalal Attari ◽  
Kumars Ebrahimi
Keyword(s):  
Water Allocation ◽  
Compromise Programming ◽  
Programming Approach

Composite Programming as an Extension of Compromise Programming

1985 ◽  
pp. 375-408 ◽  
Author(s):  
Andras Bárdossy ◽  
Istvan Bogárdi ◽  
Lucien Duckstein
Keyword(s):  
Compromise Programming ◽  
Composite Programming

scholarly journals Bi-objective load balancing multiple allocation hub location: a compromise programming approach

2020 ◽  
Vol 296 (1-2) ◽  
pp. 363-406 ◽  
Author(s):  
Rahimeh Neamatian Monemi ◽  
Shahin Gelareh ◽  
Anass Nagih ◽  
Dylan Jones
Keyword(s):  
Spatial Distribution ◽  
Transportation Cost ◽  
Compromise Programming ◽  
Mixed Integer ◽  
Programming Approach ◽  
Hub Location ◽  
Hub And Spoke ◽  
Classical Models ◽  
Linear Programming Formulation ◽  
Multiple Allocation

AbstractIn this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both capacitated and uncapacitated variants wherein the number of hub nodes traversed along origin-destination pairs is not constrained to one or two as in the classical models.. From the perspective of an infrastructure owner, we want to make sure that there exists a choice of design for the hub-level sub-network (hubs and hub edges) that considers both objectives of minimizing cost of transportation and balancing spatial distribution of flow across the hub-level network. We propose a bi-objective (transportation cost and hub-level flow variance) mixed integer non-linear programming formulation and handle the bi-objective model via a compromise programming framework. We exploit the structure of the problem and propose a second-order conic reformulation of the model along with a very efficient matheuristics algorithm for larger size instances.

Interactive Compromise Programming

1987 ◽  
Vol 38 (2) ◽  
pp. 163 ◽  
Author(s):  
Ramazan Evren
Keyword(s):  
Compromise Programming
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