Jully Jeunet

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.

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.

Single-point stochastic search algorithms for the multi-level lot-sizing problem

Among the most common decisions in manufacturing and distribution companies are probably those regarding Material Requirements Planning. However, that firms are daily confronted with these decisions does not mean they are easy to handle. The multi-level lot-sizing (MLLS) problem is a combinatorial optimization problem which can only be solved optimally within reasonable delays when small instances are considered. This has motivated the search for heuristic techniques achieving a satisfactory balance between computational demands and cost effectiveness. In particular, the MLLS problem has characteristic features that have permitted the development of specific methods: interdependencies exist among stages in the product structure. In this paper, we examine the performance of single point stochastic techniques and compare them to several problem specific algorithms that exist in the literature. A large set of 280 variants of stochastic search algorithms is designed and applied to a variety of problems of small and large size. We find that these techniques, despite their simplicity and the widespread belief that they are generally efficient, only seldom outperform problem-specific algorithms, and when they do so it is usually associated with a much longer execution time. We also exhibit an efficient combination of search and annealing which is found able to produce significant and consistent improvements over problem-specific algorithms.

An alternative to safety stock policies for multi-level rolling schedule MRP problems

We study the impact of positive lead times on the multi-level lot-sizing problem in a rolling schedule environment. We show how stockout situations may arise even in a context of deterministic demand. We therefore develop a procedure to avoid such stockouts and we compare its performance through a simulation study to a safety stock strategy. Simulation results show the superiority of the proposed procedure.

Assessing and controlling the impact of hospital capacity planning on the waiting time

In the literature, tactical plans of elective patients aim at increasing hospital efficiency through a better resource utilisation, although hospitals claim that patient satisfaction, usually measured by the waiting time, is also important. In this regard, the purpose of this paper is to show how patient satisfaction can be associated with any tactical plan, by developing a method to compute exact waiting time distributions. We also present a procedure to calculate the exact levels of resource utilisation. Therefore, with our procedures, hospital managers can determine the operational performance of their tactical plan. We then explore two strategies to improve tactical plans in terms of waiting time: slack planning and smooth allocation. A case study based on data from a Dutch cardiothoracic surgery centre shows that slack planning leads to a trade-off between waiting time and hospital efficiency. When slack planning is combined to smooth allocation, additional improvements of the waiting time can be reached.

Patient mix optimisation for inpatient planning with multiple resources

This contribution addresses the planning of admissions of surgical patients, requiring different resources such as beds and nursing capacity at wards, operating rooms and operating theatre personnel at an operating theatre, intensive care beds and intensive care nursing capacity at an intensive care ward. We developed a modelling approach for this planning problem, starting from a very simplified base model with deterministic resource requirements only for elective patients to a model with also stochastic resource requirements and finally a model extended to emergency patients. We developed the consecutive models over a period of 6 years together with a cardiothoracic surgeon who acted as problem owner and user of the model in healthcare practice. Each of the steps taken in the development of the models provided new insights and added to the knowledge of the planning problem and approach. We present the steps taken and the models developed, show the results obtained and the lessons learned.

Time Cost Quality Trade-off Problems: A survey exploring the assessment of quality

Abstract Time Cost Trade-off Problems have received considerable attention in the literature on deterministic project scheduling problems but integrating the quality factor to these problems dates back to the mid-nineties with the pioneering work of Babu and Suresh (1996) . Since then, to the best of our knowledge, about twenty papers have been published on this topic. The present paper analyses these Time Cost Quality Trade-off Problems in light of the usual classification for Time Cost Trade-off Problems that is based upon the number and category of resources and on the continuous or discrete type of the relationship between duration and cost or resources utilisation. In this survey, the emphasis is on the definition of project activities quality and on aggregation methods used to derive the overall project quality. We report the absence of a direct relationship between quality and resources allocated to activities and a lack of use of the lexicographic method to solve the problem.

Integrated capacity and inventory decisions

This paper deals with the simultaneous acquisition of capacity and material in a situation with uncertain demand, with non-zero lead-times for the supply of both material and capacity. Although there is a lot of literature on the time-phased acquisition of capacity and material, most of this literature focuses on one of the two decisions. By using a dynamic programming formulation, we describe the optimal balance between using safety stocks and contingent workforce for various lead-time situations. We compare the cost ingredients of the optimal strategy with the standard inventory approach that neglects capacity restrictions in the decision. The experimental study shows that co-ordination of both decisions in the optimal strategy leads to cost reductions of around 10%.We also derive characteristics of the optimal strategy that we expect to provide a thorough basis for operational decision making.