Determining the prices of remanufactured products, capacity of internal workstations and the contracting strategy within queuing framework
Graphical abstract
Section snippets
Introduction and literature review
Recently, due to environmental regulations, legal pressures as well as potential economic incentives, manufacturers are more interested in product recovery processes. It is worth mentioning that a significant factor of product recovery is remanufacturing that has been applied in several industries such as cameras, automobile engine, computers, medical equipment, aircrafts, among others. According to Stock et al. [1], remanufacturing processes are profitable. For example, some well-known
Problem definition
Consider the remanufacturing facility as it is shown in Fig. 1, where all nonconforming products enter to a testing center at an arrival rate of λ are first screened 100% to be classified into m groups based on the severities of their nonconformities. Based on the limited capacity of the workstation and time constraint, each group of nonconforming products are either processed at internal workstation or they are sent to a contractor. Furthermore, each group of nonconforming products is
Problem formulation
In this section, the assumptions of the problem defined in Section 2 are first explicitly stated in Subsection 3.1. Then, the notation is introduced in Subsection 3.2. Finally, the mathematical model is derived in Subsection 3.3.
Solving methods
In this section, a genetic algorithm (GA) is developed to obtain a near-optimum solution of the complex mixed-integer nonlinear mathematical programming formulation developed in Section 3. Before employing this algorithm, six MODM methods are investigated in the next subsection to convert the bi-objective optimization problem into a single-objective optimization problem. Converting a regular bi-objective problem to a single one is a common practice in the literature and it is just an
Numerical examples
This section considers a remanufacturing facility with ten groups of nonconforming products and ten independent workstations. The initial data for the numerical example are randomly generated as shown in Table 1. Besides, the total arrival rate (λ), the total budget to increase the capacity of workstations (C), the total budget for outsourcing (B), and the test station cost (CT) are determined 100, 25000, 1000 and 1000, respectively. Furthermore, L, U and h are considered 3, 8 workstations and
Conclusion
This paper investigates a bi-objective remanufacturing problem within queuing framework in which an outsourcing strategy is considered. The objective functions are to maximize the total profit received by selling the remanufactured products and to minimize the average number of products in the queues at the workstations, simultaneously. The aim is to determine the capacity of each internal workstation, the contracting strategy, and the selling prices of remanufactured products. The problem is
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