Dynamic Scheduling of Handling Equipment at Automated Container Terminals

In this paper we consider the problem of integrated scheduling of various types of handling equipment at an automated container terminal in a dynamic environment. This means that the handling times are not known exactly beforehand and that the order in which the different pieces of equipment handle the containers need not be specified completely in advance. Instead, (partial) schedules may be updated when new information on realizations of handling times becomes available. We present an optimization based Beam Search heuristic and several dispatching rules. An extensive computational study is carried out to investigate the performance of these solution methods under different scenarios. The main conclusion is that, in our tests, the Beam Search heuristic performs best on average, but that some of the relatively simple dispatching rules perform almost as good. Furthermore, our study indicates that it is effective important to base a planning on a long horizon with inaccurate data, than to update the planning often in order to take newly available information into account.beam search;dynamic scheduling;container terminal;dispatching rules

Effective algorithms for integrated scheduling of handling equipment at automated container terminals

In this paper we consider the problem of integrated scheduling of various types of handling equipment at an automated container terminal, where the objective is to minimize the makespan of the schedule. We present a Branch & Bound algorithm that uses various combinatorial lower bounds. Computational experiments show that this algorithm is able to produce optimal or near optimal schedules for instances of practical size in a reasonable time. We also develop a Beam Search heuristic that can be used to tackle very large problem instances. Our experiments show that for such instances the heuristic obtains close to optimal solutions in a reasonable time.AGV's;Container terminal;beam search;branch & bound;scheduling

A solution approach for dynamic vehicle and crew scheduling

In this paper, we discuss the dynamic vehicle and crew schedulingproblem and we propose a solution approach consisting of solving asequence of optimization problems. Furthermore, we explain why itis useful to consider such a dynamic approach and compare it witha static one. Moreover, we perform a sensitivity analysis on ourmain assumption that the travel times of the trips are knownexactly a certain amount of time before actual operation.We provide extensive computational results on some real-world datainstances of a large public transport company in the Netherlands.Due to the complexity of the vehicle and crew scheduling problem,we solve only small and medium-sized instances with such a dynamicapproach. We show that the results are good in the case of asingle depot. However, in the multiple-depot case, the dynamicapproach does not perform so well. We investigate why this is thecase and conclude that the fact that the instance has to be splitin several smaller ones, has a negative effect on the performance.transportation;vehicle and crew scheduling;large-scale optimization;dynamic planning

Moeilijk doen als het ook makkelijk kan

Rede, in verkorte vorm uitgesproken op vrijdag 20 september 2002 bij de aanvaarding van het ambt van bijzonder hoogleraar aan de Faculteit der Economische Wetenschappen, vanwege de Vereniging Trustfonds Erasmus Universiteit Rotterdam, met als leeropdracht Mathematische Besliskunde, in het bijzonder Toepassingen in Transport en Logistiek.operations research;management science;applied research;mathematical programming;public transport planning;logistics management

A dynamic lot-sizing model with demand time windows

One of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the period. If backlogging is allowed, the demand of a given period cannot be delivered earlier than the period, but can be delivered later at the expense of a backordering cost. Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. In most real life applications, the customer offers a grace period - we call it a demand time window - during which a particular demand can be satisfied with no penalty. That is, in association with each demand, the customer specifies an earliest and a latest delivery time. The time interval characterized by the earliest and latest delivery dates of a demand represents the corresponding time window. This paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. If shortages are not allowed, the complexity of the proposed algorithm is of the order T square. When backlogging is allowed, the complexity of the proposed algorithm is of the order T cube.dynamic programming;lot-sizing;time windows

Moeilijk doen als het ook makkelijk kan

One of the main points of criticism on academic research in operations research (management science ) is that there is too much emphasis on the mathematical aspects of the discipline. In particular, the mathematical models that lend themselves to rigorous mathematical analysis are often rough simplifications of the actual decision problems that need to be solved in practice. Moreover, advanced mathematical solution methods may lead to overkill, since sometimes acceptable solutions may already be found by relatively simple ad hoc methods. In this address, we argue that although these observations may be true, this does not necessarily mean that mathematically oriented research is not useful in solving practical decision problems. We believe that the criticism ignores both the role of academic research within the discipline as well as the fact that certain recent successful applications of operations research owe much to mathematically oriented research. We illustrate the usefulness of this type of research by discussing research projects in container logistics and public transport scheduling.Rede, in verkorte vorm uitgesproken op vrijdag 20 september 2002 bij de aanvaarding van het ambt van bijzonder hoogleraar aan de Faculteit der Economische Wetenschappen, vanwege de Vereniging Trustfonds Erasmus Universiteit Rotterdam, met als leeropdracht Mathematische Besliskunde, in het bijzonder Toepassingen in Transport en Logistiek

The two-dimensional cutting stock problem within the roller blind production process

In this paper we consider a two-dimensional cutting stock problem encountered at a large manufacturer of window covering products. The problem occurs in the production process of made-to-measure roller blinds. We develop a solution method that takes into account the characteristics of the specific problem. In particular, we deal with the fact that fabrics may contain small defects that should end up with the waste. Comparison to previous practice shows significant waste reductions.cutting;trim loss;two-dimensional cutting stock problem

A Polynomial Time Algorithm for a Deterministic Joint Pricing and Inventory Model

In this paper we consider the uncapacitated economic lot-size model, where demand is adeterministic function of price. In the model a single price need to be set for all periods. Theobjective is to find an optimal price and ordering decisions simultaneously. In 1973 Kunreuther and Schrage proposed an heuristic algorithm to solve this problem. The contribution of our paper is twofold. First, we derive an exact algorithm to determine the optimal price and lot-sizing decisions. Moreover, we show that our algorithm boils down to solving a number of lot-sizing problems that is quadratic in the number of periods, i.e., the problem can be solved in polynomial time.pricing;inventory;production;lot-sizing

A savings based method for real-life vehicle routing problems

This paper describes a Savings Based algorithm for the Extended Vehicle Routing Problem. This algorithm is compared with a Sequential Insertion algorithm on real-life data. Besides the traditional quality measures such as total distance traveled and total workload, we compare the routing plans of both algorithms according to non-standard quality measures that help to evaluate the "visual attractiveness" of the plan. Computational results show that, in general, the Savings Based algorithm not only performs better with respect to these non-standard quality measures, but also with respect to the traditional measures.distribution;vehicle routing;road transport