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Recently, the importance of the end-of-life (EOL) product recovery process has been rising since the return rate of products is increasing due to strict environmental regulations on products and economic reasons. In addition to this, the advent of emerging product identification technologies makes product lifecycle data visible at EOL phase. In this regard, the optimization of product recovery processes becomes highlighted as a challenging issue of EOL. At the inspection phase after disassembly, each part can have various EOL recovery options such as re-use, remanufacturing, and disposal. Depending on the selected EOL options of parts, the recovery value of an EOL product will be different. Hence, it is essential to develop a decision-making method that can select the best EOL options of parts for maximizing the recovery value of an EOL product. Although some previous works have focused on improving EOL operations, there has been a lack of research which dealt with EOL product recovery optimization in a quantitative manner. To cope with this limitation, in this study, we focus on a selection problem of EOL product recovery options for a turbocharger case, for maximizing its recovery value which includes both recovery cost and quality. To solve the problem efficiently, we develop a multi-objective evolutionary algorithm (MOEA). To show the effectiveness of our algorithm, we carry out computational experiments. [ABSTRACT FROM AUTHOR]

This paper considers the problem of selecting and sequencing operations in process planning for the objective of minimizing the sum of operation processing costs and machine, set-up and tool change costs. The main constraint is the precedence relations among operations. To represent the precedence relations and alternative operations, a tree-structured precedence graph is suggested. Based on the graph, the entire problem is decomposed into two subproblems: operation selection and operation sequencing. Then, three iteratively algorithms are suggested that solve the two subproblems iteratively until optimal and near-optimal solutions are obtained. The algorithms are illustrated using an example part, and to show the performances of the algorithms, computational experiments were done on randomly generated test problems. The results show that the algorithms suggested work well for the test problems. [ABSTRACT FROM AUTHOR]

The selection of the best compromise alternative for treating a product at its end of life (EOL) is presented. Each EOL alternative has its own consequences from an economical, environmental and social point of view. The criteria used to determine these consequences are often contradictory and not equally important. In the presence of multiple conflicting criteria, an optimal EOL alternative rarely exists. Hence, the decision-maker should seek the best compromise EOL alternative. The present paper proposes a multicriteria decision-aid (MCDA) approach to aid the decision-maker in selecting the best compromise EOL alternative on the basis of his/her preferences and the performances of EOL alternatives with respect to the relevant environmental, social and economic criteria. This approach is important because it allows the user to consider various conflicting criteria simultaneously and it takes into account his/her preferences. The paper analyses the most important aspects of this approach such as the constitution of a set of EOL alternatives, the selection of a list of relevant criteria to evaluate the EOL alternatives and the choice of an appropriate multicriteria decision-aid method. A case study is provided to illustrate how the proposed approach can be used for product EOL alternative selection in real-world applications. [ABSTRACT FROM AUTHOR]

The operation-sequencing problem in process planning is considered to produce a part with the objective of minimizing the sum of machine, setup and tool change costs. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem difficult to solve. Six local search heuristics have been developed based on simulated annealing and tabu search to obtain good solutions for practical-sized problems within a reasonable amount of computation time. Application of the algorithms is illustrated using an example part. Also, computational experiments were done on randomly generated problems and the results show that the tabu search-based algorithms are better than the simulated annealing-based algorithms on overall average. In particular, one of the tabu search algorithms suggested here gave optimal solutions for most small-sized test problems within very short computation times. [ABSTRACT FROM AUTHOR]

This paper focuses on the operation sequencing problem in process planning, which is the problem of determining the sequence of operations required for producing a part with the objective of minimizing the sum of machine, set-up and tool change costs. In general, operation sequencing in process planning has been a difficult optimization problem, since it has combinatorial characteristics and complex precedence relations. This paper first suggests a precedence-directed graph to describe the precedence relations among operations with a compact form. Then, using the directed graph, the authors develop optimal and heuristic branch and fathoming algorithms that can give optimal and near optimal solutions respectively. Application of the two algorithms is illustrated using an example part. Also, to show the performance of the suggested algorithms, computational experiments were done on randomly generated problems and the results show that the heuristic algorithm gives optimal solutions for all the generated problems with much shorter computation time. [ABSTRACT FROM AUTHOR]