Lot Sizing Heuristics Performance

Abstract

Each productive system manager knows that finding the optimal trade‐off between reducing inventory\ud and decreasing the frequency of production/replenishment orders allows a great cut‐back in operations costs. Several authors have focused their\ud contributions, trying to demonstrate that among the various dynamic lot sizing rules there are big differences\ud in terms of performance, and that these differences are not negligible. In this work, eight of the best known lot sizing algorithms have been described with a unique modelling approach and have then been exhaustively tested on several different scenarios, benchmarking\ud versus Wagner and Whitin’s optimal solution. As distinct from the contributions in the literature, the operational behaviour has been evaluated in order to determine\ud which one is more suitable to the characteristics of each scenario

On Just-In-Time Production Leveling

“…For example, the MRP algorithm includes a lot-sizing phase, which results in product batching; this tends to generate higher stock levels compared to the JIT approach. Several studies have been carried out on MRP lot-sizing [6] and trying to improve the algorithm performance [7,8,9]; however, it seems that JIT can outperform MRP given the heijunka condition, in case of leveled production both in quantity and in mix. The traditional JIT technique to manage production flow is named kanban.…”

The optimal replenishment policy for time-varying stochastic demand under vendor managed inventory

“…Baciarello et al (2013), Kropp et al (1983), and Jeunet and Jonard (2000) pointed out that for unstable environments, Silver-Meal and the part period simplified algorithm represents the best trade-off between cost effectiveness and robustness. Baciarello et al (2013) further presented an extensive experiment for eight of the main lot sizing techniques (Least unit cost, Silver-Meal, Groff's method, part period balancing, Freeland and Colley, part period simplified, McLauren's order moment, and maximum part period gain) and then benchmarked these techniques against Wagner and Whitin's exact algorithm. Pujawan (2004) considered the effects of relatively low variability in demand and evaluated the impact of two lot sizing methods such as the SilverMeal heuristic and Least unit cost on the order variability under a stochastic case.…”

Analysis of lot-sizing methods’ suitability for different manufacturing application scenarios oriented to MRP and JIT/Kanban environments

“…T o evaluate the performance of the heuristi c methods in situati ons with diff erent variati on scenarios on demand, several disti nct contexts were generated following a normal distributi on, characterized by an average (µ) and standard deviati on (σ) (Baciarello et al 2013). The average demand value was defi ned as µ = 250 for each context or applicati on scenario, and only the standard deviati on varies from 10 ≤ σ ≤ 80 with an increment of 10.…”