The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.

This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.

The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.

The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.

/pdf/an-adaptive-large-neighborhood-search-heuristic-for-the-54a14dq2lk.pdf

An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.

The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.

The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.

- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications

Table of Contents

Foreword (Ugo Montanari) 
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) 
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) 
Chapter 3. Constraint Propagation (Christian Bessiere) 
Chapter 4. Backtracking Search Algorithms (Peter van Beek) 
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) 
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons) 
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) 
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith) 
Part II : Extensions, Languages, and Applications

Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) 
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) 
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) 
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) 
Chapter 17. Constraints over Structured Domains
(Carmen Gervet) 
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings) 
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) 
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

/pdf/handbook-of-constraint-programming-53srutguo2.pdf

Handbook of Constraint Programming

Introduction to stochastic programming

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

An improved typology of cutting and packing problems

https://www.iro.umontreal.ca/~ferland/Seminars/Local_search/Pisinger,Ropke_ALNS_2007.pdf

A general heuristic for vehicle routing problems

Pension systems differ across countries and are subject to country specific regulations. In addition, various countries offer different types of pension plans to their citizens. In some pension plans individuals have a lot of flexibility regarding managing their savings; in others, pension funds or life insurers manage the members' savings without taking into account the individuals' preferences. Nevertheless, in most of the pension plans individuals face some challenges. They have to decide on the investment and consumption of their retirement savings either by choosing a pension provider and a specific pension product, or by choosing a particular annuity, and, possibly, a life insurance policy. In some countries they have to make decisions as soon as they are employed; in other countries, not until they approach the time of retirement. To help individuals make the right decisions regarding their retirement savings, this thesis presents some optimization techniques that could be applied by pension providers and financial advisers to provide individuals with such guidelines. For a given objective function and a number of constraints, we search for the optimal solution, which indicates, for example, how to invest the savings, which annuity to purchase, and which level of death benefit to choose. In most of the papers we follow a classical approach and maximize the expected CRRA utility of either the final wealth, or the consumption and bequest amount. Scholars refer to the aforementioned decisions as the consumption-investment problems. A classical approach to solving these problems is stochastic optimal control (SOC), which aims to find a closed-form solution to a given problem. However, as the explicit solution exists only for relatively simple models, this approach cannot be applied to the problems considered in this thesis. Therefore, in this thesis we use a multi-stage stochastic programming (MSP) approach that is known for its practical applications and thus broadly used in operations research. In a few chapters of the thesis we apply a mixed approach, i.e., a combination of the MSP and SOC approaches, and in one of the chapters we apply Monte Carlo simulations. Each chapter of the thesis focuses on different challenges that individuals face, given a different set of constraints, determined either by national regulations or by individuals' personal preferences. The first two chapters deal with defined contribution pension plans, where an individual makes the consumption, investment, and life insurance decisions, both before and after retirement. Our results indicate that retirement savings management differs for each individual, and that it should not only depend on the individual's degree of risk aversion or time left to retirement, but also characteristics such as current wealth, expected income before and after retirement, expected pension contributions, impatience factor, lifetime expectancy, and preferences regarding portfolio composition and the level of death benefit. Consequently, pension providers should offer variable life annuities that are tailored to the individuals' needs in terms of the underlying asset allocation, the payout profile, and the level of death benefit.The next three chapters discuss the purchase of the right annuity. We start with investigating whether individuals should wait until retirement to purchase a life annuity providing fixed payments, whether they should invest in this annuity already some years before retirement by purchasing a deferred annuity with the same fixed payments, or whether they should invest their savings in stocks and bonds. Despite simple model assumptions, our findings indicate that individuals should invest part of their savings in deferred annuities. The proportion in these products increases with the degree of risk aversion and the expected lifetime, and decreases with the bequest motive. Afterwards, we investigate the optimal annuity choice under inflation risk, which is often ignored both by practitioners advising on the retirement planning and by scholars investigating the consumption-investment problems. We search for an optimal level of retirement income in real terms, given investment opportunities in inflation-linked, nominal, and variable annuities, as well as in stocks and bonds. Our findings show that real annuities are a crucial asset in every portfolio, and that trying to hedge inflation without investing in inflation-linked products leads to a lower and more volatile retirement income. In the last chapter discussing the annuity purchase, we differentiate between a wide variety of annuities. In addition to stocks and bonds with different maturities, we search for the optimal investment in annuities contingent on a single and joint lifetime, with fixed and variable payments, with immediate and deferred payments, and with temporary and life long payments. We conclude that the optimal portfolio for a single and a two-person household not only requires frequent rebalancing, but also consists of too many different assets. Accordingly, we argue that despite so many annuities available in the market, the products that individuals need the most are not available. Finally, in the last chapter of this thesis we apply Monte Carlo simulations to investigate the value of the interest rate guarantee incorporated in the Danish with-profit products. In these products the pension provider offers its members a guaranteed interest rate as well as some bonus rate that depends on the pension provider's realized investment returns. We argue that with-profit products with such a bonus mechanism often provide lower returns than pure unit-linked products without any guarantee because, to meet the solvency requirements, the pension provider has to invest individuals' assets more conservatively.

/pdf/optimization-based-guidelines-to-retirement-planning-and-2q27ndu6q0.pdf

Optimization-based guidelines to retirement planning and pension product design

Advanced algorithms for improved baggage handling

The cost for bunker fuel represents a major part of the daily running costs of liner shipping vessels. The vessels, sailing on a fixed roundtrip of ports, can lift bunker at these ports, but prices in each port may be differing and fluctuating. The stock of bunker on a vessel is subject to a number of operational constraints such as capacity limits, reserve requirements and sulphur content. Contracts are often used for bunker purchasing, ensuring supply and often giving a discounted price. A contract can supply any vessel in a period and port, and is thus a shared resource between vessels, which must be distributed optimally to reduce overall costs. An overview of formulations and solution methods is given, and computational results are reported for some representative models.

Bunker Purchasing in Liner Shipping

/pdf/dynamic-routing-of-short-transfer-baggage-45j2me1l71.pdf

David Pisinger

Papers

Optimization-based guidelines to retirement planning and pension product design

Advanced algorithms for improved baggage handling

Bunker Purchasing in Liner Shipping

Dynamic Routing of Short Transfer Baggage

The off-line group seat reservation problem