Supply chain management optimization using meta-heuristics approaches applied to a case in the automobile industry

This thesis presents supply chain management optimization with meta-heuristics approaches, specifically on issues regarding the configuration of a generic multi stage distribution network, and the determination of a milk-run delivery issue in lean supply chain management. Indeed, this issue can be represented as the routing of the supply or delivery vehicle to construct multiple pick-ups or drop-offs on a regularly scheduled basis and at different locations. The optimal model for this milk-run delivery issue must aim to improve vehicle load and minimize transportation distance (optimal delivery route) between facilities while optimizing the entire delivery of goods among the supply chain facilities. The set of meta-heuristics approaches and hybrid meta-heuristics approaches introduced in the present research aim to become a modeling system to find an optimal solution for the transportation distance as well as the optimal delivery frequency for managing the transportation of goods in highly complex logistic networks. In fact, the optimal transportation distance ensures that the total cost of the entire supply chain is minimized. In particular, this modeling system groups concepts about integrated supply chain management proposed by logistics experts, operations research practitioners, and strategists. Indeed, it refers to the functional coordination of operations within the firm itself, between the firm and its suppliers as well as between the firm and its customers. It also references the inter-temporal coordination of supply chain decisions as they relate to the firm’s operational, tactical and strategic plans. The milk-run delivery issue is studied two ways: with the Genetic Algorithm approach and with the Hybrid of Genetic Algorithm and the Ant Colony Optimization approach. Various frameworks, models, meta-heuristics approaches and hybrid meta-heuristics approaches are introduced and discussed in this thesis. Significant attention is given to a case study from the automobile industry to demonstrate the effectiveness of the proposed approaches. Finally, the objective of this thesis is to present the Genetic Algorithm approach as well as the Hybrid of Genetic Algorithm with Ant Colony Optimization approach to minimize the total cost in the supply chain. This proposed Hybrid of Genetic Algorithm along with the Ant Colony Optimization approach can efficiently and effectively find optimal solutions. The simulation results show that this hybrid approach is slightly better efficient than the genetic algorithm alone for the milk-run delivery issue which allows us to obtain the minimum total automobile industry supply chain cost

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Locating a bioenergy facility using a hybrid optimization method

In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved

Systemic design of multidisciplinary electrical energy devices: a pedagogical approach

In this paper, we present a complete educative project for illustrating the design and the analysis of hybrid electrical systems. It is based on the study of an ElectroHydrostatic Actuator for flight control application, fed by a power supply associating a PEM fuel cell with a ultracapacitor storage. This system is controlled to achieve a typical energy management strategy of this multi source structure. Step by step, student can faces typical issues relative to the design of heterogenous and multidisciplinary devices by achieving eight pedagogical objectives. These eight targets are focused on methodological approach for multi domain modelling (Bond Graphs), causal analysis, but also on simulation of complex heterogeneous systems. A typical hybrid system feeding an ElectroHydrostatic Actuator (EHA) for flight control application has to be designed which drives students towards other pedagogical objectives: system based device sizing (fuel cell and ultracapacitor), energy management, system analysis

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Performance modeling of e-procurement workflow using Generalised Stochastic Petri net (GSPN)

This paper proposes a Generalised Stochastic Petri net (GSPN) model representing a generic e-procurement workflow process. The model displays the dynamic behaviour of the system and shows the inter relationship of process activities. An analysis based on matrix equation approach enabled users to analyse the critical system's states, and thus justify the process performance. The results obtained allow users for better decision making in improving e-procurement workflow performance

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Modelling an End to End Supply Chain system Using Simulation

Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

Simulation study of a basic integrated inventory problem with quality improvement and lead time reduction

In this paper, we investigate the trade-off between investing in product quality improvement and lead time reduction in a B2B single-supplier, single-buyer environment. To this end, a discrete event simulation model is proposed, based on the integrated inventory model defined in Ouyang et al. (2006). Furthermore, the possibility of using alternative failure mechanisms and capital investment functions are studied. ity of using alternative failure mechanisms and capital investment functions are studied