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The reliability-based design optimization (RBDO) is performed for mechanical design to compromise effectively between economy and safety requirements. In real mechanical applications, such RBDO problems are a highly complex task by involving computational difficulties and its resolution requires the use of appropriate optimization techniques. In this paper, a new RBDO solution approach is introduced for mechanical engineering problems. It is a combination of the reliable design space (RDS) technique with an efficient hybrid algorithm (AMDE-NM) based on the adaptive mixed differential evolution (AMDE) and Nelder–Mead local search (NM). First, the RDS strategy is used to turn the RBDO problem into a simple deterministic optimization (SDO) one, through converting the probabilistic constraints to approximate deterministic constraints, while the resolution is then carried out with the AMDE-NM algorithm. The new proposed integrated approach (RDS–AMDE-NM) is able to handle the mixed design variables with continuous, discrete, and integer types. Six mechanical problems with different features are studied to analyze the applicability and the efficiency of RDS–AMDE-NM. The obtained simulation results show the performance of the proposed approach, while new optimal solutions for two RBDO problems are presented. Furthermore, an industry case on a cylindrical spur gear is studied to investigate the reliability of the proposed method in solving real challenging mechanical RBDO problems. The obtained results reveal really that RDS–AMDE-NM is a promising RBDO approach with extensive applicability.

The two main objective functions for designing the manufacturing system are, improving the manufacturing system's productivity and production quality. Process planning is an integral part of manufacturing system design. In this work, the study of process plan evolution over the period has been reviewed, keeping in mind the design objectives. Based on the technological advancements, the process plan evolution has been classified into two periods. The first evolution relates to automation, in which efforts are made to automate the manual activities of conventional process plans, which leads to the development of Computer-Aided Process Planning (CAPP). As long as the systems are deterministic with fixed structures and capabilities, CAPP is a good solution. Due to the uncertainty in the market for product demand, the new manufacturing systems are becoming more and more dynamic to handle the product variety demand. In that case, just automation is not enough to achieve the objective functions of system design. Designers have to consider the integration of the manufacturing system life cycle. So the second evolution of the process plan relates to, the consideration of performance indicators defined due to the system integration. For the second evolution, the literature review of the reconfigurable process plan (RPP) is performed considering both automation and integration of the system.

This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer's company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.

In this paper, we focus on outsourcing activities optimization problem in single period setting. In some situations, capacity planning or outsourcing is a one-time event and can be modeled as a single period problem. The aim of this research is to balance the trade-off between two echelons of a supply chain consisting of a single outsourcer and a single subcontractor. Each part is composed of a failure-prone single machine that produces one product type to satisfy market requirements. The outsourcer’s manufacturing system is not able to satisfy the demand; in this case, outsourcing is allowed to recover the lack of capacity. We consider that the subcontractor can satisfy the demands of strategic clients and rent his machine for the outsourcer under a win–win partnership contract. We assume that the hazard failure rate depends on time and the adopted manufacture rate. When unforeseen failures occur, minimal repairs are implemented. Overhaul can be performed to reduce the degradation effects. Hence, we develop a mathematical model to define a profitability interval so that both parties of supply chain can be considered as winners. We seek to determine the contract parameters that suit both parties (duration, start and end dates, the production and outsourcing rates). Then, we develop an exact algorithm to solve the problem of single period optimization, which offers a better execution time through a series of test problems. Finally, we consider a sensitivity analysis based on outsourcing parameters (cost, periodicities, etc) to analyze their effects on partial costs and individual profit of each part, as well as the total profit generated by the system.

Reconfigurable manufacturing systems (RMS) are designed to overcome the shortcomings of conventional manufacturing systems in terms of effectiveness and responsiveness facing the increasingly influencing factors imposed on the manufacturing environment by unpredictable market condition and global competition. RMS is required to be more robust, flexible and reconfigurable, which makesmachine layout problem a key step to fulfill such design. Thus, any proposed layout needs to be changeable and easily redesigned. In this paper, we consider the machine layout problem in reconfigurable context. We develop an exhaustive search based heuristic to solve it. To propose the best respective machine layout, the heuristic considers various constraints from the manufacturing environment, and the initial generated process plan for the designed RMS under performance metrics. The proposed layout must at its best, respects both the constraints imposed by the generated process plan and those depicting the available location in the shop floor where machines are going to be placed. An illustrative numerical example is proposed to demonstrate the applicability of our approach.

This paper describes a multi-objective approach that assesses the evolution and layout transition efforts between products of a product family in reconfigurable manufacturing systems (RMS) design. The layout evolution effort is minimized and system performance metrics (i.e., average machine utilization and alternative replacement machines within the system) are maximized. The problem considers various manufacturing constraints as well as various metrics to ensure the high performance of the designed reconfigurable manufacturing system following the generated layouts. The effectiveness of the developed approach is evaluated using an illustrative numerical example. The results show the effectiveness of the multi-objective approach to help decision maker’s designing RMS. The approach can be used later to guarantee a better system performance when solving the problems of machine layout in the context of RMS.

In this paper, we consider a reconfigurable manufacturing systems RMS at the operational level, where we focus on the impact of machines unavailability on the efficiency of the system. More specifically, the problem consists of finding the best alternative solution (i.e.available machine) to ensure the system responsiveness if an unavailability of machine occurs. A hybrid heuristic is developed based on an adaptation of the well-known non-dominated sorting genetic algorithm (NSGA-II). Two objective functions are considered respectively the minimization of the total completion time and the minimization of the adapted robustness index. The two objectives guide the developed hybrid heuristic to the best alternative solutions by modifying the existing process plan. To illustrate the efficiency of the proposed heuristic, we present some experimental results and analyses.

In this paper, we propose to discuss profitability conditions of a subcontracting constraint for subcontractor companies. The subcontracting imposes unavailability periods of the production unit in order to periodically perform subcontracting tasks ( ST ). We study the subcontracting constraint under a joint maintenance and production approach of a production system composed of one machine M subject to random failures. This machine supplies a constant demand d via a buffer stock S . At each interval T , the machine is subject to preventive maintenance action. The corrective and preventive maintenance actions have random durations. We analytically show conditions under which subcontracting is profitable, based on a given strategies of maintenance, production control and assignment to subcontracting. In this paper, we study the profitability of two cases of subcontracting constraint: occasional and long-term relationship. In the first case, we integrate the ST in the subcontractor plan without changing the decision variables values (maintenance interval and buffer stock size). The second case imposes a new optimisation of decision variables.