Pezhman Ghadimi

Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

An integrated approach for sustainable supplier selection using fuzzy logic and fuzzy AHP

Supplier selection is one of the important processes in supply chain management. Regarding the emergence of sustainability issues in recent decades, companies have incorporated these issues in conventional supplier selection in order to meet governmental legislations and market demands. These issues have been noticed by various researchers. However, there are limited research activities which considered all aspects of sustainability for supplier selection problem as an integrated assessment. In this paper, an integrated approach of Fuzzy Analytical Hierarchy Process and fuzzy logic has been proposed in order to solve sustainable supplier selection problem. Fuzzy analytical hierarchy process has been used to calculate the weight of sustainable criteria and sub criteria. Then, fuzzy logic was utilized in order to assess the suppliers based on the weights acquired by Fuzzy analytical hierarchy process. Finally, a case study of petroleum industry has been carried out in order to show the validity of proposed approach.

A review on the buyer–supplier dyad relationships in sustainable procurement context: past, present and future

Sustainable development is currently being applied in most fields of research. Procurement, focused on the buyer–supplier dyad, is one such discipline where sustainability is being widely applied. This paper provides a review of these research studies, conducting a systematic content analysis in order to present the state of the art in this domain. The paper carries out a detailed review of articles in international scientific journals and well-known international conferences related to green and sustainable supplier selection published between 2008 and 2014 inclusive. Seven designed research questions are proposed and answered based on this bibliography. Interesting results are reported in each section and gaps in the current body of literature are identified. The purpose of this review is to provide important future directions and limitations in this research topic.

Supplier Selection: A Hybrid Approach Using ELECTRE and Fuzzy Clustering

Vendor selection is a strategic issue in supply-chain management for any organization to identify the right supplier. Such selection in most cases is based on the analysis of some specific criteria. Most of the researches so far concentrate on multi-criteria decision making (MCDM) analysis. However, it incurs a huge computational complexity when a large number of suppliers are considered. So, data mining approaches would be required to convert raw data into useful information and knowledge. Hence, a new hybrid model of MCDM and data mining approaches was proposed in this research to address the supplier selection problem. In this paper, Fuzzy C-Means (FCM) clustering as a data mining model has been used to cluster suppliers into groups. Then, Elimination and Choice Expressing Reality (ELECTRE) method has been employed to rank the suppliers. The efficiency of this method was revealed by conducting a case study in an automotive industry.

A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain

Recently, incorporating sustainability into the buyer-supplier sourcing decisions has achieved a considerable amount of attentions among researchers and industrial enterprises who are attempting to move towards sustainable production. Moreover, by investigating further in the buyer-supplier relationships, the literature suggests that proper communication and structured information exchange are important components in establishing a long-term partnership and maintaining such a relationship. Toward this end, a Multi-Agent Systems (MASs) approach is proposed as a mean of automating and facilitating the process of sustainable supplier selection and order allocation (SSS&OA) resulting in a more co-operative partnership. This research shows that financial performance of manufacturing companies adopting environmental and social sustainability in their operations strategy enhanced their competitive advantage that can lead to long-term sourcing relationships for the buyer-supplier dyad. Additionally, it was also shown that applying MASs to the SSS&OA problem can be utilized as an approach to facilitate communications and automate information exchange processes in Supply Chains (SCs) where suppliers and manufacturer are looking to maintain a long-term SC partnership. The applicability of the developed MAS approach and its incorporated sustainable supplier evaluation and order allocation models is demonstrated using an adopted practical scenario from an industrial case study operating in the electronics sector in medical device industry.

Sustainable supplier performance scoring using audition check-list based fuzzy inference system

We propose an audition check-list based fuzzy inference system approach.The approach has been tested by a real-world case study in automotive spare part industry.Sustainability triple bottom line context is incorporated in supply chain practices.The approach helps to perform a fast and efficient sustainable supplier assessment.The approach provides precise decision making assistance regarding sustainability integration. With the global awareness of sustainability issues, sustainable development is being increasingly recognized by governments and industries. In addressing these issues, organizations worldwide have taken initiatives in adopting sustainability practices in their supply chain transferring it to sustainable supply chain management. In order to establish a responsible sustainable supply chain management, an effective way would be to make sure that the potential suppliers for procuring required components are precisely assessed and evaluated based on sustainable criteria. Therefore, this paper proposes a practical decision making approach to evaluate and select the most sustainable suppliers for an automotive spare part manufacturer licensed under a France-based automotive organization. Firstly, a requirement gathering approach, the audition check-list approach, is designed to facilitate the process of data gathering for supplier evaluation based on three pillars of sustainability. Next, the gathered data are processed using a proposed fuzzy inference system to remove impreciseness and vagueness in the gathered sustainability related data. The strength of this model falls into its applicability in data gathering phase which helps decision makers in manufacturing company to perform a fast audition of a typical supplier. Secondly, the final sustainable ranking of suppliers using the proposed fuzzy inference system provide a precise and less uncertain sustainability performance scoring which makes the developed approach a reliable system for making sustainable sourcing decisions. Comparison and sensitivity analysis are performed to evaluate the proficiency of the developed approach. Finally, theoretical and managerial implications together with conclusions of the study are presented.

An Engineering Prototype Workflow Management System

Abstract Engineering workflow management is a key focus for European manufacturing companies, however, issues such as time required to gather data, develop systems and integrate into current manufacturing environments presents obstacles for adoption. This paper presents a high level prototype of an engineering workflow system developed in conjunction with a medical device company to address this gap. The prototype data was used to construct a high level artifact which illustrates how the implementation of Advanced Platform for Manufacturing Engineering and Product Lifecycle Management (7th Programme, “amePLM”) engineering workflow management system can improve product design and development processes through increased productivity by capturing workflows which previously went unquantifiable.

Order Processing in Supply Chain Management with Developing an Information System Model: An Automotive Manufacturing Case Study

Nowadays, high competitive market needs fast, effective, high responsiveness, online interactive, 24 hours 7 days availability and easy to follow up order processing. Consequently, there is a need for a model in which interdisciplinary approaches for understanding the range of Supply Chain Management (SCM) Information System (IS) capabilities are provided. In this study, an integrated model of SCM IS was developed that is supported by empirical evidence specific to SCM IS implementations. The developed model integrates and enriches theories of competitive strategy, supply chain management and inter-organizational information systems. Then, a case study of an automotive manufacturing industry was conducted to demonstrate the proficiency of the proposed model. As a result, better understanding of capabilities of implemented supply chain management information systems and expected future capabilities could be identified by practitioners and decision makers. Finally, findings of this study are listed together with some future works.

Making sustainable sourcing decisions: practical evidence from the automotive industry

ABSTRACT Achieving environmental and sustainable performance within an organisation’s supply chain and manufacturing operations will be feasible if upstream supply partners have the same commitments in performing their operations in a sustainable manner. Given the debate above, we propose a comprehensive framework to address the sustainable supplier selection and order allocation (SSS&OA) problem. The framework developed is practical, that starts by using an audition check-list specific for each sustainability dimension (environmental, economic and social), from which the weighted values of its comprised criteria are obtained. The weighted scores of the selected sustainable suppliers are utilised by a proposed bi-objective order allocation model in order to make sourcing decisions. The strength of the proposed framework is its practical applicability to provide a solution for SSS&OA problems which is validated through a real-world application. Finally, research findings, theoretical and managerial insights and also directions for additional research are presented.

Masos: a multi-agent system simulation framework for sustainable supplier evaluation and order allocation

Purchasing activities consume more than half of manufacturing and trading organizations sales capitals. Effective procurement is tied with efficient and highly accurate collection of data needed for purchasing the right material with the acceptable quality from appropriate suppliers. Supply chain management (SCM) consists of complex networks of distributed actors in which the problem of identifying the appropriate suppliers and allocating optimal order quantities based on the Triple Bottom Line (TBL) attributes is strategically important. However, implementation of an autonomous and automated assessment that can incorporate dynamics and uncertainty of the whole supply chain during the assessment period is not addressed. In the current research paper, a novel framework is designed and proposed to narrow the aforementioned gap. Agent technology has been incorporated in the developed framework to decrease the supplier chain uncertainty by decreasing human interactions and automating the process of supplier evaluation and order allocation.