Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

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From evolutionary computation to the evolution of things

In this paper we give an overview of recent developments in the field of modeling deterministic single-level dynamic lot sizing problems. The focus of this paper is on the modeling of various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research.

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Modeling Industrial Lot Sizing Problems: A Review

Single item lot sizing problems

Disaster relief presents many unique logistics challenges, with problems including damaged trans- portation infrastructure, limited communication, and coordination of multiple agents. Central to disaster relief logistics is the distribution of life-saving commodities to beneficiaries. Operations research models have potential to help relief agencies save lives and money, maintain standards of humanitarianism and fairness and maximize the use of limited resources amid post-disaster chaos. Through interviews with aid organizations, reviews of their publications, and a literature review of operations research models in transportation of relief goods, this paper provides an analysis of the use of such models from the perspective of both practitioners and academics. With the complexity of disaster relief distribution and the relatively small number of journal articles written on it, this is an area with potential for helping relief organizations and for tremendous growth in operations research.

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Disaster relief routing: Integrating research and practice

This paper presents a review of four decades of research on dynamic lot-sizing with capacity constraints. We discuss both different modeling approaches to the optimization problems and different algorithmic solution approaches. The focus is on research that separates the lot-sizing problem from the detailed sequencing and scheduling problem. Our conceptional point of reference is the multi-level capacitated lot-sizing problem (MLCLSP). We show how different streams of research emerged over time. One result is that many practically important problems are still far from being solved in the sense that they could routinely be solved close to optimality in industrial practice. Our review also shows that currently mathematical programing and the use of metaheuristics are particularly popular among researchers in a vivid and flourishing field of research.

Dynamic capacitated lot-sizing problems: a classification and review of solution approaches

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. Computational results are reported and the solutions' quality is evaluated through Lagrangean lower bounds.

A heuristic for lot-sizing in multi-stage systems

Lot sizing and scheduling by mixed integer programming has been a hot research topic in the last 20 years. Researchers have been trying to develop stronger formulations, as well as to incorporate real-world requirements from different applications. This paper illustrates some of these requirements and demonstrates how small- and big-bucket models have been adapted and extended. Motivation comes from different industries, especially from process and fast-moving consumer goods industries.

/pdf/industrial-insights-into-lot-sizing-and-scheduling-modeling-2gybt2qzuj.pdf

Industrial insights into lot sizing and scheduling modeling

Material requirements planning of batch production in multi-stage manufacturing systems is discussed where component parts may have significant non-zero production or purchasing lead time. The presence of such lead time poses a synchronization problem for the rolling horizon planning of component part production in the system. The synchronization problem is analysed, discussed and modelled first for the case of assembly product structures where a component has a unique successor component. The analysis is then extended to the more complex case of general product structures where a component may have multiple successor components. The associated general structure multi-stage lot-sizing problem is then formulated as a mixed integer linear program first in terms of conventional stock, and then reformulated in terms of echelon stock. The echelon stock quantity of a component is its lead-time adjusted total system stock, counted both as a stand-alone component and as part of successor components. The ...

Echelon stock formulation for multi-stage lot-sizing with component lead times

The application of valid inequalities to the multi-stage lot-sizing problem

https://uwe-repository.worktribe.com/preview/929249/Araujo%20Clark%202013%20A%20priori%20reformulations%20for%20joint%20rolling-horizon%20scheduling%20of%20materials%20processing%20and%20lot-sizing%20problem.pdf

Alistair Clark

Papers

A heuristic for lot-sizing in multi-stage systems

Industrial insights into lot sizing and scheduling modeling

Echelon stock formulation for multi-stage lot-sizing with component lead times

The application of valid inequalities to the multi-stage lot-sizing problem

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem