A computational analysis of lower bounds for big bucket production planning problems

In this paper, we analyze a variety of approaches to obtain lower bounds for multi-level production planning problems with big bucket capacities, i.e., problems in which multiple items compete for the same resources. We give an extensive survey of both known and new methods, and also establish relationships between some of these methods that, to our knowledge, have not been presented before. As will be highlighted, understanding the substructures of difficult problems provide crucial insights on why these problems are hard to solve, and this is addressed by a thorough analysis in the paper. We conclude with computational results on a variety of widely used test sets, and a discussion of future research

The dynamic lot-sizing problem with convex economic production costs and setups

In this work the uncapacitated dynamic lot-sizing problem is considered. Demands are deterministic and production costs consist of convex costs that arise from economic production functions plus set-up costs. We formulate the problem as a mixed integer, non-linear programming problem and obtain structural results which are used to construct a forward dynamic-programming algorithm that obtains the optimal solution in polynomial time. For positive setup costs, the generic approaches are found to be prohibitively time-consuming; therefore we focus on approximate solution methods. The forward DP algorithm is modified via the conjunctive use of three rules for solution generation. Additionally, we propose six heuristics. Two of these are single-stepSilver–Meal and EOQ heuristics for the classical lot-sizing problem. The third is a variant of the Wagner–Whitin algorithm. The remaining three heuristics are two-step hybrids that improve on the initial solutions of the first three by exploiting the structural properties of optimal production subplans. The proposed algorithms are evaluated by an extensive numerical study. The two-step Wagner–Whitin algorithm turns out to be the best heuristic

Valid Inequalities for Two-Period Relaxations of Big-Bucket Lot-Sizing Problems: Zero Setup Case

In this paper, we investigate two-period subproblems for big-bucket lot-sizing problems, which have shown a great potential for obtaining strong bounds. In particular, we investigate the special case of zero setup times and identify two important mixed integer sets representing relaxations of these subproblems. We analyze the polyhedral structure of these sets, deriving several families of valid inequalities and presenting their facet-defining conditions. We then extend these inequalities in a novel fashion to the original space of two-period subproblems, and also propose a new family of valid inequalities in the original space. In order to investigate the true strength of the proposed inequalities, we propose and implement exact separation algorithms, which are computationally tested over a broad range of test problems. In addition, we develop a heuristic framework for separation, in order to extend computational tests to larger instances. These computational experiments indicate the proposed inequalities can be indeed very effective improving lower bounds substantially

Dynamic multi-machine lotsizing and sequencing with simultaneous scheduling of a common setup resource

In this paper we propose a new solution approach to a lotsizing and scheduling problem which explicitly includes the simultaneous consideration of a common setup operator. This type of problem has been observed in several industries. We propose a model formulation of this dynamic capacitated multi-item multi-machine one-setup-operator lotsizing problem that is based on the proportional lotsizing and scheduling problem (PLSP) of Haase [1994. Lotsizing and Scheduling for Production Planning. Springer, Berlin]. In addition, we propose a model reformulation that is based on the simple plant location analogy. Finally, we extend the model for the case of a special type of sequence-dependent setup times. The different models are applied in an industrial planning environment and it is shown that good solutions are found within a few minutes of CPU time with a standard solver. Compared to the planning procedure used in the company up to now significant reductions in setup costs as well as feasible production schedules without backorders are achievable.

Solving a Multi-Level Capacitated Lot Sizing Problem with Multi-Period Setup Carry-Over via a Fix-and-Optimize Heuristic

This paper presents a new algorithm for the dynamic Multi-Level Capacitated Lot Sizing Problem with Setup Carry-Overs (MLCLSP-L). The MLCLSP-L is a big-bucket model that allows the production of any number of products within a period, but it incorporates partial sequencing of the production orders in the sense that the first and the last product produced in a period are determined by the model. We solve a model which is applicable to general bill-of-material structures and which includes minimum lead times of one period and multi-period setup carry-overs. Our algorithm solves a series of mixed-integer linear programs in an iterative so-called Fix-and-Optimize approach. In each instance of these mixed-integer linear programs a large number of binary setup variables is fixed whereas only a small subset of these variables is optimized, together with the complete set of the inventory and lot size variables. A numerical study shows that the algorithm provides high-quality results and that the computational effort is moderate.Lot Sizing, MIP, Decomposition, MLCLSP-L, Fix-and-Optimize heuristic.