Ashkan Memari

Supplier Selection: A Fuzzy-ANP Approach

The main goal of this paper is to develop a framework to support the supplier selection process in an Iranian automotive industry. Although numerous criteria are being used for the selection of suitable supplier, selection of the critical factors in conformance to the specification of the automotive industries is less investigated. In order to fill this gap, this research was carried out to systematically propose a framework comprising of the most critical factors for the aim of supplier selection. A literature survey was conducted and measures for assessing the suppliers were extracted. Nominated Group Technique (NGT) was deployed to extract the most critical performance measures from the initial list. Seven measures were found to be proper for the supplier selection process. A Fuzzy Analytical Network Process (FANP) was then proposed to weight the extracted measures and determine their importance level. The model was then implemented to assist an automotive company for the aim of its supplier selection.

A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network

Distribution network planning has attracted the attention of many studies during last decades. Just-in-time (JIT) distribution has a key role in efficient delivery of products within distribution networks. In modeling of JIT distribution networks, the most frequently applied objectives are related to cost and service level. However, evaluating the impact of simultaneously minimizing total costs and balance between distribution network entities in different echelons still rarely complies with the current literature. To remedy this shortcoming and model reality more accurately, this paper develops a multi-objective mixed-integer nonlinear optimization model for a JIT distribution in three-echelon distribution network. The aims are minimization of total logistics cost along with maximization of capacity utilization balance for distribution centers and manufacturing plants. A non-dominated sorting genetic algorithm-II (NSGA-II) with three different mutation operators namely swap, reversion and insertion is employed to provide a set of near-optimal Pareto solutions. Then, the provided solutions are verified with non-dominated ranked genetic algorithm (NRGA) as well. The Taguchi method in design of experiments tunes the parameters of both algorithms, and their performances are then compared in terms of some multi-objective performance measures. In addition, a genetic algorithm is used to assess Pareto optimal solutions of NSGA-II. Different problems with different sizes are considered to compare the performance of the suggested algorithms. The results show that the proposed solution approach performs efficiently. Finally, the conclusion and some directions for future research are proposed.

Carbon-capped Distribution Planning

We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution network.We investigate different carbon constraints namely periodic, cumulative and global.We present the complexity analysis for the proposed model.We analyze the solution approach as well as identify some managerial and policy insights. Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

Production Planning and Inventory Control in Automotive Supply Chain Networks

This paper addresses a non-linear optimization model by integrating production planning and inventory control in the automotive industry at the strategic and operational level. In order to provide an effective modeling, we developed a framework to integrate manufacturing system and suppliers within an automotive supply chain network. The numerical experiments demonstrate the efficiency of the proposed model on minimization of total delivery cost and due date delivery.

A literature review on green supply chain modelling for optimising CO 2 emission

Global warming impacts are becoming more visible in our daily life. Supply chain activities and many logistics activities are the leading sources of carbon dioxide (CO2) emission and environmental pollutions. These issues have raised concerns to reduce CO2 emissions amount through design and planning of supply chain networks. Operations research has been recognised by many studies as an effective tool to deal with CO2 emission in design and planning of green supply chains. To date, a number of literature reviews have highlighted the contribution of operations research to green supply chain management with broader areas of focus. In this paper, we present a review which highlights the operations research contribution to recent green supply chain and logistics literature which specifically focuses on planning and control of supply chain activities with respect to CO2 emission. Finally, we propose some possible areas for further developments of current studies and directions for future research.

Multi-objective genetic algorithm in green just-in-time logistics

This paper addresses a mixed-integer linear programming model by integrating just-in-time delivery along with green objectives in a logistics network. Multi-objective genetic algorithm optimization has been applied in order to minimize the number of delivery and lead-time as well as environmental impact of logistic network. This evolutionary based algorithm incorporates non-dominated sorting genetic algorithm, so as to allow heuristic for parallel optimization of the objective functions. Computational results demonstrate efficiency of the proposed model for minimizing the objective functions. Finally, the conclusion and some areas of further research are proposed.

Optimizing a Just-In-Time logistics network problem under fuzzy supply and demand: two parameter-tuned metaheuristics algorithms

Abstract Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed.