Design and Planning of Maintenance Logistics Networks

The system up-time and availability play an essential role in industrial, maritime, aeronautical, and health care sectors. These sectors utilize, in general, several advanced capital systems, such as gas turbines, radars, airplane engines, and MRI-scanners. Most of these technical devices contain expensive and low-demand repairable parts. The management of maintenance logistics networks deal with decisions both in the design and planning phases. In the design phase, goals such as the allocation of users to maintenance centers and spare-part provisioning are pursued. On the other hand, the planning phase deals with decisions in terms of workforce, capacity, and aggregate planning. Maintenance planning is a hard task due to several conflicting constraints, such as sporadic demand, uncertain repair and inspection time, limited capacity, and availability of resources (inventory and certified operators). Our problem of interest is mainly motivated by maintenance logistics networks in the context of gas turbine engines. The maintenance service providers to these devices are confronted with the interaction of workforce training and operations planning along with demand and repair time uncertainty, that introduce new challenges to the management of these logistics networks. In the first part of this thesis, we devise a decision model to obtain the optimal size of workforce, training schedule, repair quantity, as well as number of repair jobs to outsource so as to minimize the cost of repair operations, spare part stock, training, outsourcing, and penalties incurred for the delayed delivery of repaired equipment over a planning horizon. Then, we evaluate the impact of integrating workforce training with operational planning decisions in maintenance facilities. Besides, we analyze the role of risk mitigation strategies such as outsourcing of repair jobs to other maintenance centers and borrowing of certified operators in the presence of demand fluctuations by formulating a two-stage stochastic programming model. The second part of the thesis is an effort to incorporate the repair time uncertainty into the decision model developed in the first contribution. We propose a multi-stage stochastic programming model for integrated production and workforce planning under independent random repair times of faulty components. Then, we develop an approximate decomposition algorithm, based on Lagrangian relaxation approach, to efficiently solve the problem for real-size instances. This algorithm relies on decomposing the MSP model into sub-models corresponding to component scenario trees and coordinating them via a sub-gradient algorithm to obtain a high-quality feasible solution. In the final part of this thesis, given an MLN that provides maintenance/repair services to geographically dispersed equipment users, we propose a two-stage robust optimization model for collaborative design and planning of maintenance networks under demand uncertainty. The goal of this model is to determine the optimal allocation of customers to each maintenance center along with the initial stock level of different spare parts in each facility so as to minimize the cost of late deliveries under worst-case demand scenarios. We consider component and operator sharing strategies as the recourse actions in this model to hedge against the demand surge. The proposed approach is compared with a deterministic model by the aid of Monte-Carlo simulation on several test instances inspired by a real case study

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;

"The Shift from Belt Conveyor Line to Work-cell Based Assembly Systems to Cope with Increasing Demand Variation and Fluctuation in The Japanese Electronics Industries"

As consumption patterns become increasingly sophisticated and manufacturers strive to improve their competitiveness, not only offering higher quality at competitive costs, but also by providing broader mix of products, and keeping it attractive by launching successively new products, the turbulence in the markets has intensified. This has impelled leading manufacturers to search the development of alternative production systems supposed to enable them operate more responsively. This paper discusses the trend of abandoning the strategy of relying on factory automation technologies and conveyor-based assembly lines, and shifting towards more human-centered production systems based on autonomous work-cells, observed in some industries in Japan (e.g. consumer electronics, computers, printers) since mid-1990s. The purpose of this study is to investigate this trend which is seemingly uneconomic to manufacturers established in a country where labor costs are among the highest in the world, so as to contribute in the elucidation of its background and rationality. This work starts with a theoretical review linking the need to cope with nowadays' market turbulence with the issue of nurturing more agile organizations. Then, a general view of the diffusion trend of work-cell based assembly systems in Japanese electronics industries is presented, and some empirical facts gathered in field studies conducted in Japan are discussed. It is worthy mentioning that the abandonment of short cycle-time tasks performed along conveyor lines and the organization of workforce around work-cells do not imply a rejection of the lean production paradigm and its distinctive process improvement approach. High man-hour productivity is realized as a key goal to justify the implementation of work-cells usually devised to run in longer cycle-time, and the moves towards this direction has been strikingly influenced by the kaizen philosophy and techniques that underline typical initiatives of lean production system implementation. Finally, it speculates that even though the subject trend is finding wide diffusion in the considered industries, it should not be regarded as a panacea. In industries such as manufacturing of autoparts, despite the notable product diversification observed in the automobile market, its circumstances have still allowed the firms to rely on capital-intensive process, and this has sustained the development of advanced manufacturing technologies that enable the agile implementation and re-configuration of highly automated assembly lines.

Scheduling in the manufacture of evaporative air conditioners

The MISG examined the problem of scheduling production of air conditioners at Seeley International. Seeley's objective was to meet their demand in a more cost-effective way. Two models are proposed to achieve this objective. A long term master production schedule with a yearly planning horizon was formulated to give Seeley a broad based schedule for planning production to meet forecast demands and production constraints. Output from this model is designed to provide the appropriate number of units of each product type or sub-assembly item to be produced in the following week. This output is then designed to be the input to a more detailed short term model for scheduling production at the machine level. The short term model is formulated to handle a mixture of both finished products and sub-assemblies. The objective of the short term model is to minimise total production time to free up the use of resources in order to allow for external orders. Directions for further work are discussed

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

Market forces, strategic management, HRM practices and organizational performance, a model based in european sample

This study uses structural equation modeling to test a model of the impact of human resources management practices on perceived organizational performance, on a large sample of European companies. The influences of competitive intensity, industry attractiveness and strategic management are considered in the model, and their direct and indirect influence on organizational performance is assessed. The model produced an adequate fit and results show that strategic management does influence human resource practices. Human resource flexibility practices and performance management have a positive impact on organizational performance, while training was not found to have a significant impact. A direct positive impact of competitive intensity and industry attractiveness on strategic management was supported by the data, as well as a direct positive effect of industry attractiveness on perceived organizational performance.

System Dynamics Simulation to Test Operational Policies in the Milk-Cheese Supply Chain Case study: Piar Municipality, Bolivar State, Venezuela.

With the purpose of detecting the impact that variations of demand cause in the milk-cheese supply chain, and determining how the operational policies of capacity, inventories or labor force can mitigate this impact, a system dynamics simulation model has been designed based on a survey conducted on a sample of cheese manufacturers and their links with milk farms, transportation companies and cheese distributors. This supply chain will be consolidated when a milk center that will collect the raw milk is completed. From this center, and after adequate treatment, milk will be distributed to the different cheese manufacturers in the supply chain. Managing adequately the milk-cheese supply chain represents an important challenge due to the short life of these products. Although this study was done in a region in Latin America, its results can be applicable to food supply chains by introducing some modifications. The milk-cheese supply chain in this case study contemplates three milk producers, one milk center, five cheese producers and several distributing agents. These companies operate individually under normal conditions, but they have understood that their integration in a supply chain improves the competitiveness of all its members. That is to say, the sum is greater than the parts. For its initial design a simulation software model is used in which the resources of the supply chain are optimized. Later the product of this optimization facilitates some initial values to be used in the system dynamics model in which causeeffect or influence relationships have been previously established considering the most representative variables. Finally, changes in operational policies that can reduce the level of pending orders in the supply chain are tested using other simulation software. The main contribution of this research is that it can serve as support or contribute to reduce the uncertainty in the decision making process of the supply chain management due to the speed with which individual or combined policies can be analyzed. In response to a variation of demand the most adequate policy may be selected and that can be done before the policy is implemented

Partnership in practice

This paper examines human resource management practices adopted in a group of eight case study firms and their tendencies towards versus away from partnership. The analysis is based on data collected during interviews with 124 employees (75 in organisations tending towards partnership and 49 in organisations tending away from partnership) and senior managers, conducted in 1997-1998 for the Job Insecurity and Work Intensification Survey (JIWIS). Drawing on the perspectives of senior managers and employees, we examine the tendency of firms towards and away from partnership in employment relations; and in keeping with the JIWIS methodology (Burchell et.al., 2001) we combine quantitative and qualitative evidence in our analysis. Specifically, we are interested in what partnership looks like in these different contexts, the reasons it is pursued (or not), the degree to which companies have been successful in achieving their partnership objectives (from the perspective of both management and employees), and the conditions that have either facilitated or impeded partnership in relationships with employees