No abstract

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this paper, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimization tool.

No abstract

Comprehensive long term maintenance contracts are a recent trend in equipment industries. At the same time increasing environmental concerns and high prices for raw materials stress the importance of remanufacturing activities. Hence, maintenance contracts often result in remanufacturing of some critical components. We consider the situation in which an industrial equipment manufacturer offers multi-year maintenance contracts with an uptime guarantee. To provide these service contracts profitably, the company needs to decide on the contract design: selling price, overhaul interval and uptime guarantee. Moreover, the company has to invest in the required logistics network. The logistics network is determined by the locations, number and capacity level of the remanufacturing facilities and the number of field technicians in each service region. The network design and the overhaul interval affect the level of service in terms of machine uptime, and consequently, the price that customers are willing to pay for the service contract. The lead times throughout the network are stochastic in nature

No abstract

An advanced resource planning model is presented to support optimal lot size decisions for overall performance improvement of real-life supply chain management systems in terms of either total delivery time or total setup costs. Based on a queueing network, a model is developed for a mix of products, which follow a sequence of operations taking place at multiple interdependent supply chain members. At the same time, various sources of uncertainty, both in demand and process characteristics, are taken into account. In addition, the model includes the impact of parallel servers for multiple resources with period dependent time schedules. The corrupting influence of variabilities from rework and breakdown is also explicitly modeled. This integer non-linear problem is solved by standard differential evolution algorithms. They are able to find each product's lot size that minimizes its total supply chain lead time. We show that this solution approach outperforms the steepest descent method, an approach commonly used in the search for optimal lot sizes. For problems of realistic size, we propose appropriate control parameters for an efficient differential evolutionary search process. Based on these results, we add a major conclusion on the debate concerning the convexity between lot size and lead time in a complex supply chain environment.

An advanced decision support system is presented to answer aggregate planning questions regarding the trade-off between demand (product-mix) and supply (capacity) in a multi period stochastic setting. This tool improves the effectiveness and efficiency of sales and operation planning meetings by accounting for both revenues and costs that are relevant at the intermediate planning horizon. We develop a multi product, multi routing model, where a routing consists of a sequence of operations on different resources. Given customer demand in each time period, the model obtains the optimal production quantities in every period for each alternative routing, while explicitly taking into account the stochastic nature of both demand patterns and production lead times. This is the key difference between our approach and traditional aggregate planning models. At the same time, an optimal capacity level for each resource is obtained. We include trade-offs between level and chase strategies by

Comprehensive long term maintenance contracts are a recent trend in equipment industries. At the same time increasing environmental concerns and high prices for raw materials stress the importance of remanufacturing activities. Hence, maintenance contracts often result in remanufacturing of some critical components. We consider the situation in which an industrial equipment manufacturer offers multi-year maintenance contracts with an uptime guarantee. To provide these service contracts profitably, the company needs to decide on the contract design: selling price, overhaul interval and uptime guarantee. Moreover, the company has to invest in the required logistics network. The logistics network is determined by the locations, number and capacity level of the remanufacturing facilities and the number of field technicians in each service region. The network design and the overhaul interval affect the level of service in terms of machine uptime, and consequently, the price that customers are willing to pay for the service contract. The lead times throughout the network are stochastic in nature