Np Nico Dellaert

Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm

We develop a genetic algorithm (GA) to solve the uncapacitated multilevel lotsizing problem in material requirements planning (MRP) systems. The major drawback of existing approaches is undoubtedly their inability to provide costefficient solutions in a reasonable computation time for realistic size problems involving general product structures. By contrast, the proposed GA can easily handle large product structures (more than 500 items) with numerous common parts, a problem type for which standard optimization software memory becomes rapidly insufficient. Based upon several hybrid operators and an original way to build up the initial population, the resultant GA provides in a moderate execution time high cost-effectiveness solutions compared with other techniques, in the extensive tests we performed.

A genetic algorithm to solve the general multi-level lot-sizing problem with time-varying costs

The multi-level lot-sizing (MLLS) problem in material requirements planning (MRP) systems belongs to those problems that industry manufacturers daily face in organizing their overall production plans. However, this combinatorial optimization problem can be solved optimally in a reasonable CPU only when very small instances are considered. This legitimates the search for heuristic techniques that achieve a satisfactory balance between computational demands and cost effectiveness. In this paper, we propose a solution method that exploits the virtues and relative simplicity of genetic algorithms to address combinatorial problems. The MLLS problem that is examined here is the most general version in which the possibility of time-varying costs is allowed. We develop a binary encoding genetic algorithm and design five specific genetic operators to ensure that exploration takes place within the set of feasible solutions. An experimental framework is set up to test the efficiency of the proposed method, which turns out to rate high both in terms of cost effectiveness and execution speed.

Comparing transportation systems for inter-terminal transport at the Maasvlakte container terminals

In this paper, a comparison between three transportation systems for the overland transport of containers between container terminals is presented. A simulation model has been developed to assist in this respect. Transport in this study can be done by either multi-trailers, automated guided vehicles or automated lifting vehicles. The model is equipped with a rule-based control system as well as an advanced planning algorithm. The model is applied to a realistic scenario for the Maasvlakte situation in the near future. The experiments give insight into the importance of the different characteristics of the transport systems and their interaction with the handling equipment. Finally, a cost analysis has been executed to support management investment decisions.

Developing a platform for comparison of hospital admission systems: An illustration

There is an increasing need to develop a platform for comparing hospital admission planning systems due to a shift in the service paradigm in the health sector. The current service concept of hospital admission planning aims at optimising the use of scarce hospital resources without paying much attention to the level of service offered to patients. As patients nowadays do not accept long waiting times for hospital admission, it becomes necessary to consider alternative admission service concepts. Waiting lists have also become a political issue, and alternative concepts have been advocated such as giving all patients an appointment for admission. A simulation model was built to examine the impacts of extreme admission service concepts in a simplified hospital setting. The alternative concepts considered are based on the ‘zero waiting time’ principle (immediate treatment), and the ‘booked admissions’ principle (using an appointment for admission). The results of these admission service concepts are compared with the results of the current concept, based on the ‘maximising resource use’ principle. The paper deals with the development of a framework and tool that allows evaluating different, somehow conflicting, hospital admission planning concepts and the usefulness of such framework and tool for more refined/real-life approaches to hospital admission planning.

Global inventory control in an academic hospital

During a recent study concerning the global inventory control in an academic hospital we faced the problem of a hospital management that wanted a very simple inventory control model, with good performance. It was required that the members of the purchase department could understand the model and also that the models control parameters could be determined in a simple way, because of the database environment of the inventory control system. Since most items have a joint supplier and the orders for a certain supplier are always placed on the same day of the week we extended an EOQ model to a so-called (R, s, c, S) model, in which the values of the control parameters s, c and S are determined in a very intuitive way. To our surprise, the performance of this rule was comparable to that of a rule in which the control parameters were determined in a more sophisticated way.

Statistical Disclosure in Two-Dimensional Tables: General Tables

Abstract Confidentiality protection of data published in tables is a major problem for statistical offices. To obtain full cooperation of the respondents, it is required that information of individual respondents with a confidential character is kept from being disclosed. One method to avoid disclosure is the method of cell suppression, in which the values of a number of statistical cells are not published but are suppressed from publication. We discuss the method of cell suppression in general two-dimensional tables, in which row totals and column totals are always published. The values of the sensitive cells are replaced by a cross (X). Usually, additional suppressions are necessary to prevent the values of the sensitive cells from being calculated from the row or column totals. Because of these additional suppressions, useful information gets lost. We want to minimize the loss of information by making the best choice for the additional suppressions. Therefore, we introduce and compare the performance o...

Randomized multi-level lot-sizing heuristics for general product structures

We consider the multi-level lot-sizing (MLLS) problem as it occurs in material requirements planning systems, with no capacity constraints and a time-invariant cost structure. Many heuristics have been developed for this problem, as well as optimal solution methods which are applicable only to small instances. Few heuristic approaches however have been specifically built to address the MLLS problem with general product structures of large size. In this paper we develop randomized versions of the popular Wagner–Whitin algorithm [Management Science 5 (1958) 89] and the Silver–Meal technique [Production and Inventory Management 14 (1973) 64] which can easily handle product structures with numerous common parts. We also provide randomized variants of more sophisticated MLLS heuristics such as Graves’ multi-pass method [TIMS Studies in the Management Sciences 16 (1981) 95], a technique due to Bookbinder and Koch [Journal of Operations Management 9 (1990) 7] and that of Heinrich and Schneeweiss [Multi-Stage Production Planning and Control, Lecture Notes in Economics and Mathematical Systems, Springer, 1986, p. 150]. The resultant heuristics are based on original randomized set-up cost modifications designed to account for interdependencies among stages. The effectiveness of the proposed algorithms is tested through a series of simulation experiments reproducing common industrial settings (product structures of large size with various degrees of complexity over long horizons). It is concluded that the randomized version of the Graves algorithm outperforms existing heuristics in most situations. The randomization of the Wagner–Whitin algorithm proved to be the best single-pass method while only requiring a low computational effort.

Due-date setting and production control

Abstract In this paper we consider a scheduling problem in a stochastic single-product situation with production to order For this situation we consider a general rule with small overall costs, by finding the best combination of rules for due-date setting to arriving jobs and production planning, or in other words: a rule leading to acceptable due-dates for most of the clients, with limited costs for storage and penalties, as a result of deviations from the due-dates, and with limited set-up costs.

Production strategies for a stochastic lot-sizing problem with constant capacity

Abstract This paper presents a single item capacitated stochastic lot-sizing problem motibated by a Dutch company operating in a Make-To-Order environment. Due to a highly fluctuating and unpredictable demand, it is not possible to keep any finished goods inventory. In response to a customers order, a fixed delivery date is quoted by the company. The objective is to determine in each period of the planning horizon the optimal size of production lots so that delivery dates are met as closely as possible at the expense of minimal average costs. These include set-up costs, holding costs for orders that are finished before their promised delivery date and penalty costs for orders that are not satisfied on time and are therefore backordered. Given that the optimal production policy is likely to be too complex in this situation, attention is focused on the development of heuristic procedures. In this paper two heuristics are proposed. The first one is an extension of a simple production strategy derived by Dellaert [5] for the uncapacitated version of the problem. The second heuristic is based on the well-known Silver-Meal algorithm for the case of deterministic time-varying demand. Experimental results suggest that the first heuristic gives low average costs especially when the demand variability is low and there are large differences in the cost parameters. The Silver-Meal approach is usually outperformed by the first heuristic in situations where the available production capacity is tight and the demand variability is low.

Approximate solutions for a stochastic lot-sizing problem with partial customer-order information

We address a stochastic single product manufacturing system in a make-to-stock environment with partial knowledge on future demand resulting from customers ordering in advance of their actual needs. The problem consists of determining the optimal size of a production lot to replenish inventory, so that delivery promises are met on time at the expense of minimal average costs. A Markov decision model is formulated for finding the optimal policy. However, since the optimal policy is likely to be too complex in most practical situations, we present approximate strategies for obtaining good production lot sizes. The well-known (R,S) inventory policy is compared to two rules where production decisions take into account the available information on future customer requirements and the probabilistic characterization of orders yet to be placed. Furthermore, it is shown that the (s,S) inventory policy is a special case of one of the rules. An extensive numerical study reveals that the newly developed strategies outperform classical inventory policies.