https://www.mansci.ovgu.de/mansci_media/publikationen/2007/typology-EGOTEC-5t0pvr6fjifln4r4oav60tt612.pdf

An improved typology of cutting and packing problems

http://www3.eng.cam.ac.uk/~hj231/Papers/rostering.pdf

Staff scheduling and rostering: A review of applications, methods and models

Two-dimensional packing problems: A survey

The vehicle routing problem

Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

Survey of Green Vehicle Routing Problem: Past and future trends

We consider the problem of orthogonally packing a given set of rectangular items into a given strip, by minimizing the overall height of the packing. The problem is NP-hard in the strong sense, and finds several applications in cutting and packing. We propose a new relaxation that produces good lower bounds and gives information to obtain effective heuristic algorithms. These results are used in a branch-and-bound algorithm, which was able to solve test instances from the literature involving up to 200 items.

An Exact Approach to the Strip-Packing Problem

Publisher Summary Railway transportation can be split into passenger transportation and cargo transportation. This chapter discusses the European situation, where the major part of railway transportation consists of passenger transportation without addressing important problems in cargo transportation—such as car blocking, train makeup, train routing, and empty car distribution. The chapter describes several mathematical models and optimization techniques that have been developed for effectively supporting traditional planning processes in passenger railway transportation. A lot of research has been carried out in this area, both of a practical and theoretical nature. The results of this research are starting to be applied in practice. Real-time control is at the other side of the planning spectrum. The current trend in the railway industry is a shift from “planning in detail” to “effective real-time control.” Disturbances and disruptions in the railway operations are inevitable. Therefore, large parts of the operational plans are never carried out.

/pdf/passenger-railway-optimization-4csieudetg.pdf

Passenger Railway Optimization

A Lagrangian heuristic algorithm for a real-world train timetabling problem

We consider optimization problems where the exact value of the input data is not known in advance and can be affected by uncertainty. For these problems, one is typically required to determine a robust solution, i.e., a possibly suboptimal solution whose feasibility and cost is not affected heavily by the change of certain input coefficients. Two main classes of methods have been proposed in the literature to handle uncertainty: stochastic programming (offering great flexibility, but often leading to models too large in size to be handled efficiently), and robust optimization (whose models are easier to solve but sometimes lead to very conservative solutions of little practical use). In this paper we investigate a heuristic way to model uncertainty, leading to a modelling framework that we call Light Robustness. Light Robustness couples robust optimization with a simplified two-stage stochastic programming approach, and has a number of important advantages in terms of flexibility and ease to use. In particular, experiments on both random and real word problems show that Light Robustness is sometimes able to produce solutions whose quality is comparable with that obtained through stochastic programming or robust models, though it requires less effort in terms of model formulation and solution time.

Michele Monaci

Papers

Two-dimensional packing problems: A survey

An Exact Approach to the Strip-Packing Problem

Passenger Railway Optimization

A Lagrangian heuristic algorithm for a real-world train timetabling problem

Light Robustness