Two modifications of the least cost per period heuristic for dynamic lot-sizing

Ho, Johnny C.; Chang, Yih–Long; Solis, Adriano O.

doi:10.1057/palgrave.jors.2602076

Cited by 12 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluate nLPC' against the seven heuristics reviewed in Section 2 and the nLPC(i) heuristic [12] using the 20 benchmarking test problems given in Baker [2]. The data set is significant because it is precisely specified and has been shown to be capable of highlighting many differences in performance among heuristics.…”

Section: Twenty Benchmarking Test Problems Resultsmentioning

confidence: 99%

“…The net average period cost, nAPC(i,j), is defined as the ratio of total cost to the number of non-zero demand period(s) the order horizon is to span, i.e., the number of periods considered is net of zero demand period(s) [12]. Hence,…”

Section: The Proposed Heuristicmentioning

confidence: 99%

“…Ho et al [12] develop two robust LPC-based constructive heuristics, known as nLPC and nLPC(i), which are based on the net average period cost concept. According to the results of their extensive computational study, they conclude that both nLPC and nLPC(i) outperform all seven popular existing heuristics evaluated under a wide variety of experimental conditions.…”

Section: Introduction* *mentioning

confidence: 99%

See 2 more Smart Citations

A Backward Approach LPC-Based Constructive Heuristic

Tseng

Ruíz-Torres

et al. 2008

Journal of the Chinese Institute of Industrial Engineers

View full text Add to dashboard Cite

In this paper, we propose a heuristic for the well-known single-level un-capacitated dynamic lot-sizing problem. The proposed heuristic is based on the average period cost criterion introduced by the heuristic from Silver and Meal [18], also known as Least Period Cost (LPC). Two desirable features of the proposed heuristic are simplicity and the heuristic's ability to complement LPC effectively. We examine the effectiveness of the proposed heuristic by using a set of 20 benchmarking test problems attributed to Baker [2] and Berry [3]. Furthermore, we perform a large-scale simulation involving 80,000 randomly generated problems to evaluate the proposed heuristic against several popular heuristics in the literature. Our computational results show that the proposed heuristic is able to achieve highly competitive results.

show abstract

Section: Twenty Benchmarking Test Problems Resultsmentioning

confidence: 99%

Section: The Proposed Heuristicmentioning

confidence: 99%

Section: Introduction* *mentioning

confidence: 99%

See 1 more Smart Citation

A Backward Approach LPC-Based Constructive Heuristic

Tseng

Ruíz-Torres

et al. 2008

Journal of the Chinese Institute of Industrial Engineers

View full text Add to dashboard Cite

show abstract

“…For example, Ho et al (2006) developed two construction heuristics for the uncapacitated dynamic lot-sizing problem that are extensions of earlier heuristics by Silver and Meal (1973), and show that they outperform six other construction heuristics including the original Silver and Meal heuristic. James and Almada-Lobo (2011) propose, along with other heuristics, a MILP-based 'relax-and-fix' construction heuristic for the parallel-machine capacitated lotsizing and scheduling problem with sequence-dependent setups (CLSD-PM).…”

Section: Related Workmentioning

confidence: 99%

A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

et al. 2017

View full text Add to dashboard Cite

Biopharmaceutical manufacturing requires high investments and longterm production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lotsizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space.

show abstract

“…One way of simplifying the solution process of a complex production planning problem is to divide it into several simpler subproblems in a recursive manner by using dynamic programming, as these authors do: Berk et al 2008 Moreover, the simulation tools are used either in isolation (Alwan et al, 2008;Li et al, 2009b;Mohebbi et al, 2007) or combined with mathematical programming in hybrid models (Albey and Bilge, 2011;Almeder et al, 2009), which are complementary approaches that can be applied to model complex production planning problems. Other modeling approaches, such as Laplace transform (Bogataj and Bogataj, 2007;Huynh, 2006a, 2006b), heuristics (Ho et al, 2007(Ho et al, , 2006 and MRP logic (Barba- Gutierrez et al, 2008;Ram et al, 2006), are also considered in the reviewed papers.…”

Section: Modeling Approachmentioning

confidence: 99%

A review of discrete-time optimization models for tactical production planning

Díaz‐Madroñero

Peidro

2014

International Journal of Production Research

View full text Add to dashboard Cite

This paper presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, setup times, parallel machines, overtime capacities and network-type multi-site configuration stand out in terms of extensions; (4) the most widely used modeling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modeling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.

show abstract

Two modifications of the least cost per period heuristic for dynamic lot-sizing

Cited by 12 publications

References 11 publications

A Backward Approach LPC-Based Constructive Heuristic

A Backward Approach LPC-Based Constructive Heuristic

A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

A review of discrete-time optimization models for tactical production planning

Contact Info

Product

Resources

About