Forecast accuracy, information technologies and the performance of inventory policies under multi-level rolling schedule environments

Our incentive is to study the behaviour of lot-sizing rules in a multi-level context when forecast demand is subject to changes within the forecast window. To our knowledges, only Bookbinder and Heath (1988) have proposed a lot-sizing study in a multi-echelon rolling schedule with probabilistic demands. But their simulation study was limited to two arborescent structures with 6 nodes. By means of an extensive simulation study we show that it is always worth decreasing the error magnitude. This should encourage companies to develop Electronic Data Interchange to ameliorate demand forecast.Although the presence or absence of forecast errors matters more than the error level, we show that lot-sizing rules exhibit significant differences in their behaviour as the level of error is augmented. This paper also provides a clear description of the rolling procedure when applied to general product structures, probabilistic demand within the forecast window and positive lead times.economics of technology ;

Demand forecast accuracy and performance of inventory policies for multi-level rolling schedule MRP problems.

Our incentive is to assess the impact of demand forecast errors on the cost performance of several lot-sizing techniques in a multi-level context. Unpredicted changes in demand keep on plaguing consumer product companies. However, efforts to improve demand forecast accuracy may not be rewarded if lot-sizing techniques perform equally badly as soon as forecast errors affect the demand. With an extensive simulation study we show that it is always worth decreasing the error magnitude: the performance of all techniques improves when the error level is decreased. But the relationship between cost improvement and error level is not linear as bigger cost reductions are obtained when the error decrease is applied to an initial value of error that is moderate. This means that increasing forecast accuracy is more profitable for companies that already have more accurate forecasts than for those who face inadequate forecasts. Although the presence or absence of forecast errors tends to matter more than the error level itself, we show that lot-sizing rules exhibit significant differences in their behavior as the level of error is augmented. This paper also provides a clear description of the rolling procedure when applied to general product structures, demand with forecast errors within the forecast window and positive lead times.Forecast errors; Rolling schedule; Multi-level lot-sizing; Material requirements planning; Heuristics;

Budget Allocation for Permanent and Contingent Capacity under Stochastic Demand.

We develop a model of budget allocation for permanent and contingent workforce under stochastic demand. The level of permanent capacity is determined at the beginning of the horizon and is kept constant throughout, whereas the number of temporary workers to be hired must be decided in each period. Compared to existing budgeting models, this paper explicitly considers a budget constraint. Under the assumption of a restricted budget, the objective is to minimize capacity shortages. When over-expenditures are allowed, both budget deviations and shortage costs are to be minimized. The capacity shortage cost function is assumed to be either linear or quadratic with the amount of shortage, which corresponds to different market structures or different types of services. We thus examine four variants of the problem that we model and solve either approximately or to optimality when possible. A comprehensive experimental design is designed to analyze the behavior of our models when several levels of demand variability and parameter values are considered. The parameters consist of the initial budget level, the unit cost of temporary workers and the budget deviation penalty/reward rates. Varying these parameters produce several trade-offs between permanent and temporary workforce levels, and between capacity shortages and budget deviations. Numerical results also show that the quadratic cost function leads to smooth and moderate capacity shortages over the time periods, whereas all shortages are either avoided or accepted when the cost function is linear.Stochastic; Capacity planning; Contingent workers; Budget allocation; Non-linear stochastic dynamic programming; Optimization;

Controlling multi-level production in a rolling-schedule environment

We consider the multi-level lot-sizing problem in a rolling-schedule environment as it occurs in Material Requirements Planning systems, with no capacity constraints and a time-invariant cost structure. We show that the performance of fixed-horizon methods can be improved drastically in a rolling-schedule environment. We consider several improvement methods, such as the use of discounted costs and implementing a make-to-stock policy for the basic components.ou

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.ou

Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line

This paper presents an integer programming formulation for the sequencing problem in mixed-model assembly lines where the number of temporarily hired utility workers and the number of sequence-dependent setups are to be optimized simultaneously through a cost function. The resultant model offers an operational way to implement the utility work needed to avoid line stoppages, unlike previous papers addressing the goal of smoothing the workload. The present research has an immediate application to the automotive industry, namely to the car-sequencing problem. Simulation results show that the proposed formulation leads to the optimum in a reasonable time for instances up to 15 items and to satisfactory feasible solutions for some of the larger problems we considered within a moderate time limit.ou

An alternative to safety stock policies for multi-level rolling schedule 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.ou

Randomized heuristics for multi-level lot-sizing problems

This paper proposes two cost-modification procedures designed to improve the synchronization of lot-sizing decisions among levels, in any product structure. One of these cost-modification procedures includes a random variable since ordering a given item does not imply a new order for this item's components with certainty. Simulation results confirm the superiority of the randomized cumulative Wagner-Whitin algorithm over the existing techniques included in this study.</p

The discrete time break scheduling problem under fatigue and no preemption: solution methods and impact of work regulations

We address the discrete time break scheduling problem with no preemption when workers’ fatigueimpacts their productivity. We propose a Mixed Integer Linear Programming model to solve theone break problem to optimality, using a lexicographic approach where the production amount ismaximisedfirst,andthenthebreaklengthoveradiscretetimehorizon.WedevelopaVariableNeigh-bourhood Search algorithm to solve the multiple break problem. In addition to proposing efficientsolution methods to the problem, our incentive is to assess the impact on the production amountand on workers’ welfare of rest break regulations laid down in legislation or collective agreements.Weconductedanextensivesimulationstudytorepresentawiderangeofworkers’profilesdefinedinterms of fatigability and recovery speed. Simulation results show that regulations slightly affect theproduction amount whereas they allow for large improvements of workers’ welfare as long as breaksare optimised as a second objective. The production amount is also shown to be quite sensitive tothe break timing. Finally, multiple breaks can improve the production amount and workers’ welfarein many situations, which questions the widespread belief that endowing workers with a single shortbreak would optimise the production amou