Lot-sizing with stock upper bounds and fixed charges

Here we study the discrete lot-sizing problem with an initial stock variable and an associated variable upper bound constraint. This problem is of interest in its own right, and is also a natural relaxation of the constant capacity lot-sizing problem with upper bounds and fixed charges on the stock variables. We show that the convex hull of solutions of the discrete lot-sizing problem is obtained as the intersection of two simpler sets, one involving just 0-1 variables and the second a mixing set with a variable upper bound constraint. For these two sets we derive both inequality descriptions and polynomial-size extended formulations of their respective convex hulls. Finally we carry out some limited computational tests on single-item constant capacity lot-sizing problems with upper bounds and fixed charges on the stock variables in which we use the extended formulations derived above to strengthen the initial mixed integer programming formulations.mixed integer programming, discrete lot-sizing, stock fixed costs, mixing sets

Single item lot-sizing with non-decreasing capacities

We consider the single item lot-sizing problem with capacities that are non-decreasing over time. When the cost function is i) non-speculative or Wagner-Whitin (for instance, constant unit production costs and non-negative unit holding costs), and ii) the production set-up costs are non-increasing over time, it is known that the minimum cost lot-sizing problem is polynomially solvable using dynamic programming. When the capacities are non-decreasing, we derive a compact mixed integer programming reformulation whose linear programming relaxation solves the lot-sizing problem to optimality when the objective function satisfies i) and ii). The formulation is based on mixing set relaxations and reduces to the (known) convex hull of solutions when the capacities are constant over time. We illustrate the use and effectiveness of this improved LP formulation on a new test instances, including instances with and without Wagner-Whitin costs, and with both non-decreasing and arbitrary capacities over time.lot-sizing, mixing set relaxation, compact reformulation, production planning, mixed integer programming

Economic Lot-Sizing Problem with Bounded Inventory and Lost-Sales

In this paper we consider an economic lot-sizing problem with bounded inventory and lost-sales. Different structural properties are characterized based on the system parameters such as production and inventory costs, selling prices, and storage capacities. Using these properties and the results on the lot-sizing problems with bounded inventory, we present improved and new algorithms for the problem. Specifically, we provide algorithms for the general lot-sizing problem with bounded inventory and lost-sales, the lot-sizing problem with nonincreasing selling prices and the problem with only lost-sales

Improved Algorithms for a Lot-Sizing Problem with Inventory Bounds and Backlogging

This paper considers a dynamic lot-sizing problem with storage capacity limitation in which backlogging is allowed. For general concave production and inventory costs, we present an O(T2) dynamic programming algorithm where T is the length of the planning horizon. Furthermore, for fixed-charge and nonspeculative costs, we provide O(Tlog T) and O(T) algorithms, respectively. This paper therefore concludes that the time complexity to solve the bounded inventory lot-sizing problem with backlogging is the same as the complexity to solve the uncapacitated lot-sizing problem for the commonly used cost structure

An O(Tˆ3) algorithm for the capacitated lot sizing problem with minimum order quantities

This paper explores a single-item capacitated lot sizing problem with minimum order quantity, which plays the role of minor set-up cost. We work out the necessary and suffcient solvability conditions and apply the general dynamic programming technique to develop an O(T³) exact algorithm that is based on the concept of minimal sub-problems. An investigation of the properties of the optimal solution structure allows us to construct explicit solutions to the obtained sub-problems and prove their optimality. In this way, we reduce the complexity of the algorithm considerably and confirm its efficiency in an extensive computational study. --production planning,capacitated lot sizing problem,single item,minimum order quantities,capacity constraints,dynamic programming

The linear dynamic lot size problem with minimum order quantities

This paper continues the analysis of a special uncapacitated single item lot sizing problem where a minimum order quantity restriction, instead of the setup cost, guarantees a certain level of production lots. A detailed analysis of the model and an investigation of the particularities of the cumulative demand structure allowed us to develop a solution algorithm based on the concept of minimal sub-problems. We present an optimal solution to a minimal sub-problem in an explicit form and prove that it serves as a construction block for the optimal solution of the initial problem. The computational tests and the comparison with the published algorithm confirm the efficiency of the solution algorithm developed here. --lot sizing problem,minimum order quantity,dynamic programming

Lot-sizing problem

Název práce: Lot-sizing problém Autor: Ondřej Kafka Katedra: Katedra pravděpodobnosti a matematické statistiky Vedoucí bakalářské práce: RNDr. Martin Branda, Ph.D. Abstrakt: V předložené práci se seznámíme se základními pojmy z oblasti lot-sizingu. Představíme si Wagner-Whitinův problém a odvodíme algoritmus dynamického programování, jak jej řešit. Dále se podíváme na případ problému PCLSP (Profit maximizing capacitated lot size problem) s pevný- mi cenami a zanedbatelnými přípravnými náklady a budeme jej řešit pomocí speciálního algoritmu lineárního programování. Vše se pokusíme vysvětlit na konkrétních příkladech. V závěru práce ověříme efektivitu uvedených algoritmů pomocí numerické studie na náhodných datech, porovnáme rychlost naprogramovaných algoritmů s profesionálním optimalizačním nástrojem Gurobi. Klíčová slova: Lot sizing, dynamické programování, lineární programováníTitle: Lot-sizing problem Author: Ondřej Kafka Department: Department of probability and mathematical statistics Supervisor: RNDr. Martin Branda, Ph.D. Abstract: In the present work, we define the basic concepts of lot-sizing. We introduce Wagner-Whitin's dynamic lot size problem and derive a dynamic programming algorithm for the solution. Next we look at the case of PCLSP (Profit maximizing capacitated lot size problem) problem with fixed prices and negligable setup costs and solve it using specialized linear programming algorithm. Everything we try to explain with concrete examples. In the end we verify the efficiency of those algorithms by numerical study on random data comparing the performance of programmed algorithms with the professional optimization solver Gurobi. Keywords: Lot-sizing, dynamic programming, linear programmingDepartment of Probability and Mathematical StatisticsKatedra pravděpodobnosti a matematické statistikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

On the complexity of the economic lot-sizing problem with remanufacturing options

In this paper we investigate the complexity of the economic lot-sizing problem with remanufacturing (ELSR) options. Whereas in the classical economic lot-sizing problem demand can only be satisfied by production, in the ELSR problem demand can also be satisfied by remanufacturing returned items. Although the ELSR problem can be solved efficiently for some special cases, we show that the problem is NP-hard in general, even under stationary cost parameters