Aslı Aksoy

Supplier selection and performance evaluation in just-in-time production environments

Research highlights? Neural network is used for supplier selection and evaluation in JIT production. ? The system can assist manufacturers in two ways: selecting and evaluating suppliers. ? The proposed systems are tested with data taken from an automotive factory. ? The results show that the proposed systems can be used effectively and simply. The purpose of this paper is to aid just-in-time (JIT) manufacturers in selecting the most appropriate suppliers and in evaluating supplier performance. Many manufacturers employ the JIT philosophy in order to be more competitive in todays global market. The success of JIT on the production floor has led many firms to expand the JIT philosophy to the entire supply chain. The procurement of parts and materials is a very important issue in the successful and effective implementation of JIT; thus, supplier selection and performance evaluation in long-term relationships have became more critical in JIT production environments. The proposed systems can assist manufacturers in handling these issues. In this research, neural network based supplier selection and supplier performance evaluation systems are presented. The proposed approach is not limited to JIT supply. It can assist manufacturers in selecting the most appropriate suppliers and in evaluating supplier performance. The proposed neural network based systems are tested with data taken from an automotive factory, and the results show that the proposed systems can be used effectively.

A memory structure adapted simulated annealing algorithm for a green vehicle routing problem

Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.

Simulated annealing approach in scheduling of virtual cellular manufacturing in the automotive industry

An approach based on simulated annealing algorithm and heuristic method is presented as an efficient means of scheduling the manufacturing operations of virtual cellular manufacturing systems in the automotive industry. The objectives are to minimise the total weighted tardiness of the production schedule and to minimise the total materials travelling distance. A two-stage approach is employed for scheduling the manufacturing operations. In the first stage, the simulated annealing algorithm is applied to get the optimal schedule. In the second stage, a heuristic approach that was presented by Mak et al. (2007) is employed with some adjustments to minimise the total materials travelling distance. Examples are introduced to evaluate the performance of the present approach and to illustrate how the approach is employed to tackle scheduling problems. The results show that the approach is quite successful and can be used for scheduling the virtual manufacturing cells for the production of parts in case of frequently changing demands.

Design of an intelligent decision support system for global outsourcing decisions in the apparel industry

Global outsourcing can be defined as assigning a specific business component to a global supplier. While global outsourcing enhances the capability of the enterprise, it also facilitates flexible enterprise structures. Many factors need to be considered simultaneously for global outsourcing decisions, and purchasing managers need a structured method of using these criteria in their decision-making. The scope of this study was to develop a fuzzy logic-based global outsourcing decision support system to evaluate the “make or buy” decision in the apparel industry. A fuzzy logic-based decision support system is constructed due to the similarity between fuzzy logic and human decision-making systems. The proposed approach decreases the dependency of the decision-making process on the decision-maker, establishing an effective system that can easily be applicable to most of the apparel industry.

The Fundamentals of Global Outsourcing for Manufacturers

Today, international competition is growing rapidly, and enterprises must always remain ahead of the competition to ensure their survival. Therefore, firms must keep pace with dynamic conditions and rapid changes, be innovative, and adapt to new systems, techniques and technologies. In a competitive market environment, customers are becoming more conscious and tend to demand a particular number of customised products at a particular speed. Furthermore, fluctuations in national economies and in the global economy create significant risks. Because of all of these factors in todays competitive environment, firms have begun to make radical changes in their management and production structures. They must also reduce costs to maintain their current position in the market.

A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet

In this study, the green vehicle routing problem (GVRP) with a heterogeneous fleet is presented for both capacity and time-windows constraints to reduce fuel consumption and consequently to minimise CO2 emissions. A hybrid metaheuristic algorithm (HMA) is developed to solve this problem to analyse the effect of a heterogeneous fleet on reducing the fuel consumption for the specified variants of GVRP, such as GVRP with capacity constraints and GVRP with time-windows constraints. The proposed HMA is validated using well-known instances with different numbers of customers and fleet configurations. The computational results indicated that the HMA is capable of obtaining good results for GVRP variants within a reasonable amount of time by providing remarkable reductions in fuel consumption and greener fleet configurations.

Integrated model for renewable energy planning in Turkey

ABSTRACT All economic sectors are associated with energy use; therefore, government organizations aim to supply sustainable energy for human needs and economic growth. In particular, increased environmental concerns of the public in Turkey have impacted policymaking for renewable energy (RE) management in Turkey. The primary objective for RE sources of the Turkish Ministry of Energy is to ensure that 30% of the share of electricity production is from RE resources in 2023. In this paper, the integrated multi-objective, multi-period linear programming model is presented to determine effective allocation of RE supply for seven different geographical regions in Turkey for the period of 2017 to 2024. The integrated model consists of two different stages. The first stage involves qualitative evaluations of RE sources for seven geographical regions. Analytical Hierarchy Process (AHP) is applied to determine criteria priorities and overall ratings of geographical regions across determined criteria for RE sources are computed. The second stage of the integrated model consists of a multi-objective, multi-period linear programming model. The proposed multi-objective linear programming model is coded in MPL (Mathematical Programming Language) and solved using the GUROBI 5.1.0 solver. The output of the integrated model presents the total supply amount of RE sources for geographical regions in planning period. The ε-constraints method is applied to compute the total supply amount of RE from geographical regions for the period of 2017 to 2024. In this study, a systematic decision-making model is generated to allocate renewable energy sources to the geographical regions. The presented model integrates qualitative evaluations and quantitative parameters of different geographical regions to determine the optimal supply amount of RE. The obtained results are consistent with the potential quantities of RE alternatives in geographical regions, regional specifications, and social requirements.