Optimising installation (R,Q) policies in distribution networks with stochastic lead times: a comparative analysis of guaranteed- and stochastic service models

This paper studies two modelling approaches to the multi-echelon inventory optimisation problem in a distribution network with stochastic demands and lead times. It compares the performance of a novel guaranteed-service model (GSM), using an installation (R, Q) inventory control policy, with a stochastic service model (SSM) considering ordering, holding and flexibility costs. From both cycle service level and fill rate perspectives, our numerical analysis of the 1-warehouse 2-retailer network shows that cost difference between both models is driven by the internal service level at the warehouse. The GSM outperforms the SSM for over 80% of the simulated instances and realises an average total cost improvement of approximately 10%. This analysis goes against earlier results that showed a relatively low-cost difference between the two approaches, and demonstrates that it is worthwhile to evaluate competing models for multi-echelon inventory optimisation in real-world supply chains with batch ordering and variable lead times.     

Optimising part feeding in the automotive assembly industry: deciding between kitting and line stocking

In a synchronous and fast-paced assembly line operation, it is crucial that the right parts are being supplied at the right time and at the right place. In automotive assembly, the need for efficient material handling part delivery is particularly great because of extensive product customisation and the lack of space to stock all the required parts at the assembly line. This paper introduces a mathematical cost model for evaluating the assignment of parts to one of two possible material supply systems: kitting or line stocking. Case data from an automotive company in Belgium is used to test the model. The results demonstrate that hybrid policies, where some parts will be kitted while others will be stocked in bulk at the line, are preferred to the exclusive use of either material delivery system. The factors influencing the preferred delivery method for individual parts are explored. Numerical results are presented.     

A robust hierarchical production planning for a capacitated two-stage production system

In this paper, we propose a robust hierarchical production planning approach for a two-stage real world capacitated production system operating in an uncertain environment. The first stage of the system produces a set of semi-finished products having relatively stable annual demands, and the second finishing stage produces finished products having highly variable weekly demands. The fixed production setup costs incurred at the first stage are considerably high. Fixed production setup costs incurred at the second stage are fairly small compared to those of the first stage. We propose an integrated hierarchical planning model, where semi-finished products from the first stage (i.e. the aggregate level) are disaggregated into finished products to be produced in the second stage (i.e. the operational level). As a result of the relatively stable demands and the high setup costs experienced at the first stage, a cyclical aggregate planning model is proposed for production planning at the upper level of the hierarchical plan. Based on this aggregate plan, a modified periodic review policy is then proposed for production planning at the lower level. Finally, a coupling plan, linking the two planning levels, is proposed to ensure the feasibility of the disaggregation process at every period.     

An integrated model for inventory and production planning in a two-stage hybrid production system

A two-stage hybrid flow-shop production system is considered. The first stage is a process production system and the second stage is a job-shop production system. The two stages are separated by an intermediate warehouse to introduce flexibility (some independence) in the planning of production at both stages. The inventory level at the warehouse should be optimized to provide a trade-off between the cost of carrying the inventory of the semi-finished products, the minimum batch size requirement in the first stage, and the required service level at the second stage. An integrated model for planning the production in these hybrid flow-shop production systems types is developed. The objectives of optimizing the production and inventory costs at the two stages of the system, including the warehouse, while satisfying customer demands, are considered. An algorithm to solve the suggested model is described in detail, and a solution is provided for a real world case, which has inspired the study. A computational study to measure the performance of the approach was also carried out and the results are reported.     

A constrained EPSAC approach to inventory control for a benchmark supply chain system

The design of an appropriate inventory control policy for a supply chain (SC) plays an essential role in tempering inventory instability and bullwhip effect. Several constraints are commonly encountered in actual operations so managers are required to take these physical restrictions into account when designing the inventory control policy. Model predictive control (MPC) appears as a promising solution to this issue, due to its capability of finding optimal control actions for a constrained SC system. Therefore, the inventory control problem for a benchmark SC is solved using the extended prediction self-adaptive control approach to MPC. To extend methodologies in our previous work, the control framework relies on generic process model and incorporates the physical constraints arising from practical operations to form the general constrained optimisation problems. The managers can choose from decentralised and centralised control structures according to specific informational and organisational factors of their SCs. The proposed control schemes in this study may be appropriate for industrial practice because the designed policy can bring a reduction of over 30% in operating cost and a significant increase of customer satisfaction level compared with that of the conventional policy.     

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.     

Migrating the fair share algorithm from a distribution to a production planning environment

This paper investigates the ability to migrate the fair share algorithm from a distribution to a production planning environment. In a semi-process-based production system, such as that of the photographic film producer Agfa, the availability of the intermediate product is then the limiting constraint steering the fair share algorithm for the end-product lotsizing decision process. The manufacturing model of Agfa is typically semi-process, where a first stage produces a limited number of intermediate products. The second stage is flow oriented and converts the intermediates into many distinct end-products. The planning method currently implemented within Agfa is a two-level scheduling approach. First, it establishes a cyclical volume plan at the intermediate product level, which is then used as an input constraint for the secondary problem of determining end-product lotsizes. As an alternative to the traditional model, where the end-product lotsizes are determined based on the standard EOQ formula, this investigation suggests the end-product mix decisions to be governed by a tuned fair share algorithm. The paper discusses this algorithm with its parameter settings, the impact on stock values, on service levels, and on set-up and inventory holding costs. The results of both algorithms are compared. This investigation proves that the combination of a cyclical volume plan, at the intermediate product level, combined with fair share mix decisions for the end-product lotsizes, delivers the needed service level with lower inventory levels and reduced operational costs. The main benefit of the model integrating volume planning and mix decisions is its ability to reduce demand amplifications, prohibiting market demand nervousness (amplified by the Forrester effect) from entering into upstream operations. The reduced nervousness allows a major reduction in needed safety stock at the intermediate product level.     

Aggregate planning in hybrid flowshops

We present a linear programming based heuristic for the solution of a class of aggregate level planning problems in hybrid flowshops (flowshops with several machines per stage). First, the general planning problem is modelled as multi-level with parallel processors, multi-item, capacitated, lot-sizing with set up times. We suggest a hierarchical approach which sequentially loads the stages; each stage is constrained by the solution of its preceding stage and each stage is treated as a multi-item, capacitated, lot-sizing problem with setup times on parallel processors. We show how this latter problem may be reformulated and solved heuristically as a sequence of network problems (trans-shipment problems) in which the amount of capacity lost in setups is fixed for each period and each processor. The model is within the computing reach of a PC.     

Enhancing the order picking process through a new storage assignment strategy in forward-reserve area

This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and returns. Implementing effective storage assignment strategies combined with efficient batching, in this context, is very fundamental to keep the warehouse’s responsiveness and order completion times up to the standards. This paper investigates a new storage assignment strategy, initially implemented by a large shoes and footwear wholesaler, to enhance the performance of the order picking process in its warehouses. The impact of this new storage assignment strategy on the performance of the order picking process is analysed via simulation. The performance of the system, measured in terms of total picking time as well as order lead time while taking congestion into account, is compared to the cases where conventional storage assignment strategies are implemented. A full factorial design is set up and the simulation output is statistically analysed. The results of this analysis are reported and thoroughly discussed. Attending to the results, the proposed strategy presents a remarkable potential to shorten total picking travel distances and order completion time and consequently customer satisfaction.