Fixed-Charge Transportation with Product Blending

Papageorgiou, Dimitri J.; Toriello, Alejandro; Nemhauser, George L.; Savelsbergh, Martin

doi:10.1287/trsc.1110.0381

Cited by 22 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors would like to thank Ahmet Keha for bringing reference [11] to their attention. This work benefitted from numerous insightful discussions with Andrew Miller.…”

Section: Acknowledgementmentioning

confidence: 99%

Valid Inequalities for the Pooling Problem with Binary Variables

d'Ambrosio

Linderoth

Luedtke

2011

Integer Programming and Combinatoral Optimization

View full text Add to dashboard Cite

The pooling problem consists of finding the optimal quantity of final products to obtain by blending different compositions of raw materials in pools. Bilinear terms are required to model the quality of products in the pools, making the pooling problem a non-convex continuous optimization problem. In this paper we study a generalization of the standard pooling problem where binary variables are used to model fixed costs associated with using a raw material in a pool. We derive four classes of strong valid inequalities for the problem and demonstrate that the inequalities dominate classic flow cover inequalities. The inequalities can be separated in polynomial time. Computational results are reported that demonstrate the utility of the inequalities when used in a global optimization solver.

show abstract

“…The authors would like to thank Ahmet Keha for bringing reference [11] to their attention. This work benefitted from numerous insightful discussions with Andrew Miller.…”

Section: Acknowledgementmentioning

confidence: 99%

Valid Inequalities for the Pooling Problem with Binary Variables

d'Ambrosio

Linderoth

Luedtke

2011

Integer Programming and Combinatoral Optimization

View full text Add to dashboard Cite

show abstract

“…The intrinsic flexibility of such blending process can be utilized to optimize the allocation and transportation of raw materials to production centres through TS. Some important studies related on product blending are given as: Papageorgiou et al [27] who first analyzed TP in the presence of product blending including fixed-charge. Gao and Kar [9] introduced product blending on a solid transportation problem (STP).…”

Section: Introductionmentioning

confidence: 99%

“…Also they analyzed only single objective TP as of authors (cf. [9,27,34,39]), did not include other objectives (such as transportation time, carbon emission, deterioration, etc. together) which are conflicting nature, and may be effected on the cost objective function.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-rough multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station

Ghosh

Roy

2021

RAIRO-Oper. Res.

View full text Add to dashboard Cite

In this contribution, for the first time, an efficient model of multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station is formulated. Transfer station inserts transfer cost and type-I fixed-charge. Our aim is to analyze an extra cost that treats as type-II fixed-charge and truck load constraints in the designed model that required when the amount of items exceeds the capacity of vehicle for fulfilling the shipment by more than one trip. Type-II fixed-charge is added with transportation cost and other cost from transfer station. We consider here an important issue of the multi-objective transportation problem as product blending constraints for transporting raw materials with different purity levels for customers' satisfaction. In realistic point of view, the parameters of the model are imprecise in nature due to existing several unpredictable factors. These factors are apprehended by incorporating the fuzzy-rough environment on the parameters. Expected-value operator is utilized to derive the deterministic form of fuzzy-rough data, and the model is experienced with help of fuzzy programming, neutrosophic linear programming and global criteria method. Two numerical examples are illustrated to determine the applicability of the proposed model.

show abstract

“…1 Variants of the pooling problem have applications including 2 : crude oil scheduling, [3][4][5][6] water networks, 7 natural gas production, 8,9 fixed-charge transportation with product blending, 10 hybrid energy systems, 11 and multiperiod blend scheduling. 12 The difficulty of the pooling problem arises from the nonconvex bilinear terms representing linear blending in the pools.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Using functional programming to recognize named structure in an optimization problem: Application to pooling

2016

View full text Add to dashboard Cite

in Wiley Online Library (wileyonlinelibrary.com) Branch-and-cut optimization solvers typically apply generic algorithms, e.g., cutting planes or primal heuristics, to expedite performance for many mathematical optimization problems. But solver software receives an input optimization problem as vectors of equations and constraints containing no structural information. This article proposes automatically detecting named special structure using the pattern matching features of functional programming. Specifically, we deduce the industrially-relevant nonconvex nonlinear Pooling Problem within a mixed-integer nonlinear optimization problem and show that we can uncover pooling structure in optimization problems which are not pooling problems. Previous work has shown that preprocessing heuristics can find network structures; we show that we can additionally detect nonlinear pooling patterns. Finding named structures allows us to apply, to generic optimization problems, cutting planes or primal heuristics developed for the named structure. To demonstrate the recognition algorithm, we use the recognized structure to apply primal heuristics to a test set of standard pooling problems.

show abstract

Fixed-Charge Transportation with Product Blending

Cited by 22 publications

References 22 publications

Valid Inequalities for the Pooling Problem with Binary Variables

Valid Inequalities for the Pooling Problem with Binary Variables

Fuzzy-rough multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station

Using functional programming to recognize named structure in an optimization problem: Application to pooling

Contact Info

Product

Resources

About