A semi-infinite programming model for earliness/tardiness production planning with simulated annealing

Li, Y; Wang, D

doi:10.1016/s0895-7177(97)00184-2

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose a simulated annealing based Algorithm 1 to solve a convex SIP and demonstrate that the optimal value of (2.8) converges in probability to the optimal value of (1.1). A similar algorithm to solve a SIP model for tardiness production planning based on simulated annealing was introduced in Li and Wang (1997), where the authors modeled the constraint functions in the form of penalties to be added to the objective function in order to obtain an unconstrained SIP, following which the unconstrained optimization problem was solved in the decision variable by means of a simulated annealing algorithm. Algorithm 1 is beneficial in applications where the SIP is an intermediate step and the decisions are taken based on the value of the SIP.…”

Section: Simulated Annealing Based Algorithm For Convex Semi-infinite...mentioning

confidence: 99%

“…Today its applications are not restricted to a particular domain, and have diversified into robust optimization (Ben-Tal et al, 2009;Bertsimas et al, 2010), mathematical physics, geometry (Hettich & Kortanek, 1993), and statistics (Dall'Aglio, 2001) to name a few. SIPs have also found applications in manufacturing engineering where the design of optimal layouts of the assembly lines under uncertainties is important (Weber, 2003;Li & Wang, 1997). In finance SIPs have found applications in various domains, particularly in risk-aware and portfolio optimization (Werner, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Near-optimal solutions of convex semi-infinite programs via targeted sampling

et al. 2022

View full text Add to dashboard Cite

We propose an approach to find the optimal value of a convex semi-infinite program (SIP) that involves identifying a finite set of relevant constraints by solving a finite-dimensional global maximization problem. One of the major advantages of our approach is that it admits a plug-and-play module where any suitable global optimization algorithm can be employed to obtain the optimal value of the SIP. As an example, we propose a simulated annealing based algorithm which is useful especially when the constraint index set is high-dimensional. A proof of convergence of the algorithm is included, and the performance and accuracy of the algorithm itself are illustrated on several benchmark SIPs lifted from the literature.

show abstract

Section: Simulated Annealing Based Algorithm For Convex Semi-infinite...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near-optimal solutions of convex semi-infinite programs via targeted sampling

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Wang and Fang (1996) present a genetic algorithm to solve the SIP. Li and Wang (1997) propose a simulated annealing algorithm combined with a heuristic and the steepest descent method. This paper extends Fang and Wu's (1994) inexact approach for LSIP to 0-1 semi-infinite programming and applies to the new product introduction planning problems.…”

Section: The Inexact Approachmentioning

confidence: 99%

New product introduction planning using a 0–1 semi-infinite programming model

Wan

Wang

Fung

2005

Production Planning & Control

View full text Add to dashboard Cite

“…Two problems from production planning were coded as described by Li and Wang [1997] and Wang and Fang [1996].…”

Section: The Test Problem Databasementioning

confidence: 99%

Sipampl

Fernandes

Gomes

2004

ACM Trans. Math. Softw.

View full text Add to dashboard Cite

SIPAMPL is an environment for coding semi-infinite programming (SIP) problems. This environment includes a database containing a set of SIP problems that have been collected from the literature and a set of routines. It allows users to code their own SIP problems in AMPL, to use any problem already in the database, and to develop and test any SIP solver. The SIPAMPL routines support the interface between a potential SIP solver and test problems coded in AMPL. SIPAMPL also provides a tool that allows the selection of problems from the database with specified characteristics. As a concept demonstration, we show how MATLAB can use SIPAMPL to solve the problems in the database. The Linux and Microsoft Windows versions together with the database of coded problems are freely available via the web.

show abstract

A semi-infinite programming model for earliness/tardiness production planning with simulated annealing

Cited by 8 publications

References 16 publications

Near-optimal solutions of convex semi-infinite programs via targeted sampling

Near-optimal solutions of convex semi-infinite programs via targeted sampling

New product introduction planning using a 0–1 semi-infinite programming model

Sipampl

Contact Info

Product

Resources

About