Network and Contract Optimization for Maintenance Services with Remanufacturing

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

Optimal design of uptime-guarantee contracts under IGFR valuations and convex costs

“…Drawing upon a non-linear deterministic demand function for approximating customers' sensitivity to contract price and delivery time, So and Song (1998) propose a model to analyze pricing, delivery time guarantee and capacity expansion decisions of a service provider. In a holistic approach to optimize contract prices and uptime levels, as well as the network of maintenance facilities and personnel, Lieckens et al (2015) use a multinomial logit model to estimate the probabilities of the customers choosing among full-service, on-call, or no-contract options. They obtain contract choice probabilities indirectly as functions of customer's price elasticity and downtime sensitivity.…”

Formulating and solving sustainable stochastic dynamic facility layout problem: a key to sustainable operations

“…Our interest in investigating the stochastic dynamic facility location problem was triggered by three gaps within facility layout design problem literature: firstly, the inherent uncertainties in demand and supply, which are widely noted in operations management literature (Balakrishnan and Cheng, 2007;Dubey et al, 2015); secondly, the lack of studies that look into the FLP from a sustainability point of view, apart from exceptions (Yang et al, 2013;Sacaluga and Frojan, 2014;Lieckens et al, 2015); and thirdly, the lack of studies in the stochastic FLP literature that use both quantitative and qualitative criteria apart from notable exceptions (Moslemipour and Lee, 2011;Garcia-Hernandez et al, 2013;2015;Yang et al, 2013;Tayal and Singh, 2014a). We are in agreement with Yang et al (2013) who suggest that simplifying practical FLP (and in our case, SSDFLP) in mathematical models or simulation models for objective optimization (Ertay et al, 2006;Yang and Hang, 2007) needs to be complemented by qualitative criteria.…”

“…SA, CSA, Hybrid FA/CSA to generate layouts followed by applying DEA to identify efficient layouts among the generated ones, and finally applying MADM approaches such as TOPSIS, IRP and AHP in association with aggregate ranking methods viz. Borda-Kendall and Integer Linear Programming (ILP) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 645 from a sustainability perspective (investigating economic, social, and environmental perspectives) (Yang et al, 2013;Sacaluga and Frojan, 2014;Lieckens et al, 2015) while incorporating both quantitative and qualitative criteria (Moslemipour and Lee, 2011;GarciaHernandez et al, 2013;2015;Yang et al, 2013;Tayal and Singh, 2014a). …”

“…Balakrishnan and Cheng (2009) have further argued to develop DFLP algorithms so that demand uncertainty does not influence the algorithms performance. Lieckens et al (2015) have argued the need for sustainable aspect, which includes moral hazards while locating the maintenance services with remanufacturing unit location and its layout design. Recently, Akash and Singh (2016) applied big data analytics to optimize stochastic dynamic facility layout problem.…”

Reinforcement learning-based flow management of gas turbine parts under stochastic failures

“…For example, methods for setting the optimal price of service contracts are proposed in [11,12,14], within the game theory framework. The same issue, i.e., contract pricing optimization, is investigated in [19] in combination with the optimization of logistics (i.e., facility locations, capacities and inventories with given service level), and in combination with the issue of optimally scheduling preventive maintenance in [16,20]. Other optimization objectives are the minimization of the warehouse costs through the reduction of the average number of parts sojourning therein (e.g., [16]), the identification of the optimal times for performing maintenance actions and ordering parts (e.g., [21,22]), the level of repair [13,23], the number of maintenance jobs that can be completed in each maintenance period [24], etc.…”