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Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study. IntroductionWith the globalisation and the emergence of large-scale enterprise of interdependent organisations in the twenty-first century, there has been an increasing trend in outsourcing of raw materials, parts and services (Aissaoui, Haouari, and Hassini 2007). This trend has forced companies to give more attention to purchasing operations and their associated decisions. Under the pressure of global competition, companies strive to achieve excellence in delivering high-quality and low-cost products and services to their customers on time and rely on the efficiency of their supply chain to gain competitive advantage. Supply chain management involves suppliers, manufacturers, distribution centres and retailers to ensure the efficient flow of raw materials, work-in-process inventory, finished products, information and funds among different facilities. Effective supply chain management involves managing supply chain assets and products, information and fund flows to maximise total supply chain surplus (Chopra and Meindl 2013). A growth in supply chain surplus increases the size of the total share and allows contributing members of the supply chain to benefit. One of the important decisions that influences the entire company's performance and competitiveness is the supplier selection and order allocation to the selected suppliers.A closed-loop supply chain system is defined as the process of planning, implementing and controlling the inbound flow and storage of secondary goods and related information opposite to the traditional supply chain directions for the purpose of recovering value and proper disposal operations (Fleischmann et al. 1997). In addition to selecting the best candidate suppliers and allocating optimal orders to them, closed-loop supply chain systems consist of reuse, resale, repair, refurbishing, remanufacturing and recycling operations. In the remanufacturing process, used/returned products are disassembled in disassembly sites and then usable parts are cleaned, refurbished and transmitted to part inventory. In the next stage, the new ...

PurposeThis paper seeks to develop and present a new mathematical formulation to determine the optimal preventive maintenance and replacement schedule of a system.Design/methodology/approachThe paper divides the maintenance‐planning horizon into discrete and equally‐sized intervals and in each period decide on one of three possible actions: maintain the system, replace the system, or do nothing. Each decision carries a specific cost and affects the failure pattern of the system. The paper models the cases of minimizing total cost subject to a constraint on system reliability, and maximizing the system reliability subject to a budgetary constraint on total cost. The paper presents a new mathematical function to model an improvement factor based on the ratio of maintenance and repair costs, and show how it outperforms fixed improvement factor models by analyzing the effectiveness in terms of cost and reliability of the system.FindingsOptimal decisions in each period over a planning horizon are sought such that the objectives and the requirements of the system can be achieved.Practical implicationsThe developed mathematical models for this improvement factor can be used in theoretical and practical situations.Originality/valueThe presented models are effective decision tools that find the optimal solution of the preventive maintenance and replacement scheduling problem.

A maintenance scheduling optimization model considering equipment risk, total maintenance cost, system reliability and availability is proposed. This work is motivated by gas processing operator's concern of high maintenance cost, poor availability and reliability caused by inefficient maintenance scheduling. The approach presented in this article addresses the optimization of maintenance cost by efficiently scheduling maintenance task subject to reliability and availability constraints. Four maintenance actions are considered for each equipment, namely, corrective, replacement, maintenance and inspection. The proposed solution develops maintenance schedules for complex repairable system with equipment operating in series. Two single-objective, nonlinear mathematical models are presented to find the optimal maintenance cost subject to reliability and system reliability subject to availability constraint. A goal programming model is also proposed to simultaneously deal with multiple criteria based on their importance and defined goals. A gas absorption system of a hydrocarbon processing facility is used to ensure the practicality of the proposed formulation to real industry problems. A comparison of existing and proposed formulation is carried out to show that the proposed optimization approach is an efficient method for optimizing maintenance schedules and flexibility to adjust schedules in a complex operating system.