A hybrid method for modeling and solving supply chain optimization problems with soft and logical constraints
type: Item
instance of: scholarly article
title: A hybrid method for modeling and solving supply chain optimization problems with soft and logical constraints
zbMATH Open document ID: 1400.90036
DOI: 10.1155/2016/1532420
author: Krzysztof Bzdyra
author: Jarosław Wikarek
author: Pawel Sitek
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). The \(\mathrm{ECL}^{\mathrm{i}} \mathrm{PS}^{\mathrm{e}}\) system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.
Mathematics Subject Classification ID: 90B06
Mathematics Subject Classification ID: 90B50
zbMATH DE Number: 6952802
describes a project that uses: SCIP
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/1532420
OpenAlex ID: W2554819632
Wikidata QID: Q57516475
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description: scientific article; zbMATH DE number 6952802