Analysis of the uncapacitated dynamic lot size problem (original) (raw)

A heuristic algorithm for determining lot sizes of an item subject to regular and overtime production capacities

Journal of Operations Management, 1983

The problem considered in this paper deals with the sizing and timing of re~~en~shmentsfor an item facirzg a time-vurying, but known, pattern of requirements. Regular time and overtime (the latter at a cost premium) production options are available where there are production capacities that also can vary with time. The problem is to establish thepattern of repIen~shments so us to keep the total of setup, carrying and overtime premium costs as low as possible without any backlogging of demand and without violating any of the capacity constraints. A heuristic procedure, simple enough to implement mum&y, is developed and tested on a large representutive set of problems. The resulting performance is excellent, namely an average cost penalty of only 0.5%.

Heuristic Lot-Sizing Performance in a Rolling-Schedule Environment

Decision Sciences, 1980

This paper examines the impact of a rolling-schedule implementation on the performance of three of the better known lot-sizing methods for single-level assembly systems-Part-Period-Cost-Balancing, Silver-Meal, and Wagner-Whitin algorithms-and a modified version of the Silver-Meal procedure. The main finding is that under certain conditions the computationally simpler Silver-Meal heuristic can provide cost performance superior to that of the Wagner-Whitin algorithm.

Lot Size Optimization of Purchased Products

2019

In Turkey, Pinar Et is one of the leading companies in meat sector since 1985. Within the scope of senior year project, this paper considers the optimization of lot sizes of auxiliary and packaging materials, purchased by the company. Aim of the study is the minimization of total inventory costs, which include unit purchasing, setup, and holding costs subject to a service level. For this purpose, multi-item dynamic lot sizing models are formulated with time-varying demand, deterministic lead time, with backorders allowed. The first model is formulated by considering Joint Replenishment of items supplied from the same suppliers. The second model is formulated to reflect Vendor-Based minimum lot sizes and rounding quantities to the replenishment quantity. Although these models produce an optimal solution in a reasonable time, a Wagner-Whitin based heuristic algorithm is developed as an additional solution to the company. All of the models run according to Rolling Horizon (RH) approach...

A heuristic method for lot-sizing in multi-stage systems

1997

This article considers the lot-sizing problem in multi-stage production settings with capacity-constrained resources. This problem deals with the determination of a production plan for the end item and its components in order to meet the forecast demand in each period of a planning horizon. The production plan should minimize the sum of production, setup and inventory costs. A heuristic method build upon a formulation of the problem in terms of echelon stock is developed.

A comparative performance analysis of the Wagner-Whitin algorithm and lot-sizing heuristics

Computers & Industrial Engineering, 1990

Lot-sizing for dynamic demand has received considerable attention in the literature over the past two decades. The Wagner-Whitin (WW) algorithm, although known to produce optimal ordering plans for dynamic lot-sizing problems, has not been utilized or promoted due to the unjustified claims about its inefficiency. In this paper, we show the relative performances of four popular heuristics against the WW. We use the cost performance and CPU time as our criteria. Our results show that the WW algorithm will solve problems in linear time, and among all of the algorithms tested, the Silver-Meal heuristic exhibits the best overall performance, in terms of speed (17 times faster than WW), and in quality of solution (with an average loss of 1.6% of optimality).

Stochastic lot-sizing: Solution and heuristic methods

International Journal of Production Economics, 1996

We consider a single item stochastic lot-sizing model motivated by a Dutch company operating in a make-to-order environment. Since there is no possibility for having stocks on hand, every customer's order receives a fixed delivery date upon arrival. The objective is to determine the optimal size of production lots so that delivery dates are met as closely as possible at the expense of minimal costs. These include set-up costs, holding costs for orders that are finished before their delivery date and penalty costs for orders that are not satisfied on time and therefore backordered. We model this problem as a Markov Decision Process. Given that the optimal production policy is likely to be too complex, attention is focused on the development of heuristic procedures. In this paper three lot-sizing rules are proposed for both the uncapacitated and capacitated versions of the problem. The first one is a simple production strategy where the orders for a certain number of periods are produced whenever the demand for the current period is above a given value. The second lot-sizing strategy is based on the well-known Silver Meal algorithm for the case of deterministic time-varying demand. The third rule is a fixed cyclic strategy. Numerical results are presented for some test problems with demand distributions close to real situations. The performance of the lot-sizing rules is analysed considering several capacity levels.

The Role of Heuristic Methods as a Decision-Making Tool in Aggregate Production Planning

International Journal of Business Administration, 2015

This study aims to explain the role of heuristic methods in the decision making process and as a tool for knowledge capture. As a result, we conclude that heuristic methods give better support to the decision maker than mathematical models in many cases especially when time and cost are critical factors in decision making. ). The complexity of the planning activity stems from two main factors. The first is turbulence of the environment that introduces frequent disturbances as a result of demand fluctuation, changes in commercial priorities, raw materials availability and production capabilities. Therefore production planning is a highly dynamic activity and these leads to a tendency for a short term view. Secondly, there are numerous factors and objectives that have to be considered, the most important of which can be grouped as follows: