Applications of maintenance optimisation models: a review and analysis

In this paper we give an overview of applications of maintenance optimization models published so far. We analyze the role of these models in maintenance and discuss the factors which may have hampered applications. Finally, we discuss future prospects

Counter examples for compact action Markov decision chains with average reward criteria

In this note we present two examples of compact-action finite-state Markov decision chains in which a policy improvement procedure yields wrong or limited results. In the first example, which exhibits a multichain structure, there is no convergence of the average rewards of the successive policies to the maximal value. In the second example, which has a unichain structure, the lack of uniqueness of maximizing policies in each step of the algorithm means that there is no convergence of either bias vectors or maximizing policies. Accordingly, no solution to the average optimality equations can be obtained

On the use of operations research models for maintenance decision making

In this paper we analyse the role of operations research models in maintenance decision making. After an overview of these models and thei

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost

Cargo Revenue Management: Bid-Prices for a 0-1 Multi Knapsack Problem

Revenue management is the practice of selecting those customers that generate the maximum revenue from a fixed and perishable capacity. Cargo revenue management differs from the well-known passenger revenue management problem by the fact that its capacity constraint is 2-dimensional, i.e. weight and volume, and that the weight, volume and profit of each booking request are random and continuous variables. This leads to a multi-dimensional on-line knapsack problem. We show that a bid-price acceptance policy is asymptotically optimal if demand and capacity increase proportionally and the bid-prices are set correctly. We provide a heuristic to set the bid-prices based on a greedy algorithm for the multi-knapsack problem proposed by Rinnooy Kan et al. (1993). A test case shows that these bid-prices perform better than the traditional LP-based bid-prices that do not perform well at all for this problem

Designing liner shipping networks

In this paper the combined fleet-design, ship-scheduling and cargo-routing problem with limited availability of ships in liner shipping is considered. A genetic algorithm based solution method is proposed in which the ports are first aggregated into port cluster to reduce the problem size. When the cargo flows are disaggregated, a feeder service network is introduced to ship the cargo within a port cluster. The solution method is tested on a problem instance containing 58 ports on the Asia-Europe trade lane of Maersk. The best obtained profit gives an improvement of almost 20% compared to the reference network based on the Maersk network

Simultaneous Optimization of Block Replacement and Spare Part Ordering TIME for a Multi Component System with Separate Spare Part Ordering for Block and Failure Replacements

A block replacement schedule can be optimized simultaneously with a spare parts ordering schedule, since all items are replaced at a constant interval. The solution of joint optimization for spare parts ordering time and block replacement gives lower costs compared to separate optimization of ordering time and replacement time. The spare parts for replacement can be classified as stochastic demand for failure replacement and deterministic demand for block replacement. In this paper, we propose a simulation model for a separate spare parts ordering schedule. The solution was compared to the solution for a model with common spare parts for both failure and block replacement. The system has N identical components, each with a Weibull lifetime distribution. The costs of failure and block replacements, and also the costs of ordering, holding and shortage of spare parts are given. The proposed model was shown to perform better than the common order model. Also, compared to the age replacement model, the solution of the proposed model is relatively similar, yet the economies of scale would be an advantage for the block replacement over age replacement

Customer Differentiated End-of-Life Inventory Problem

This paper deals with the service parts end-of-life inventory problem in a circumstance that demands for service parts are differentiated. Customer differentiation might be due to criticality of the demand or based on various service contracts. In both cases, we model the problem as a finite horizon stochastic dynamic program and characterize the structure of the optimal policy. We show that when customers are differentiated based on the demand criticality then the optimal structure consists of time and state dependent threshold levels for inventory rationing. In case of differentiation based on service contracts, we show that in addition to rationing thresholds we also need contract extension thresholds by which the system decides whether to offer an extension to an expiring contract or not. By numerical experiments in both cases, we identify the value of incorporating such decisions in service parts end-of-life inventory management with customer differentiation. Moreover, we show that these decisions not only result in cost efficiency but also decrease the risk of part obsolescence drastically

Dynamic influences in multi-component maintenance

Most maintenance-optimisation models assume an infinite planning horizon and suppose that the failure process is stationary. Hence, information which is not known beforehand and which beocmes available in the short term only, must be ignored. We consider in this paper a multi-component system with economically dependent components, and we compare the costs of a stationary-planning method with the costs according to an approach which can adapt this long-term plan to dynamically changing information (such as a variable use of components and the occurrence of maintenance opportunities). With numerical experiments we show that incorporating short-term information can yield considerable cost savings

An overview of inventory systems with several demand classes

In this chapter we discuss inventory systems where several demand classes may be distinguished. In particular, we focus on single-location inventory systems and we analyse the use of a so-called critical level policy. With this policy some inventory is reserved for high-priority demand. A number of practical examples where several demand classes naturally arise are presented, and the implications and modelling of the critical level policy in distribution systems are discussed. Finally, an overview of the literature on inventory systems with several demand classes is given