3,062 research outputs found
Modeling and Controlling of an Integrated Distribution Supply Chain: Simulation Based Shipment Consolidation Heuristics
Increasing competition due to market globalization, product diversity and technological breakthroughs stimulates independent firms to collaborate in a supply chain that allows them to gain mutual benefits. This requires collective knowledge of the coordination and integration mode, including the ability to synchronize interdependent processes, to integrate information systems and to cope with distributed learning.
The Integrated Supply Chain Problem (ISCP) is concerned with coordinating the supply chain tires from supplier, production, inventory and distribution delivery operations to meet customer demand with an objective to minimize the cost and maximize the supply chain service levels. In order to achieve high performance, supply chain functions must operate in an integrated and coordinated manner. Several challenging problems associated with integrated supply chain design are: (1) how to model and coordinate the supply chain business processes; (2) how to analyze the performance of an integrated supply chain network; and (3) how to evaluate the dynamic of the supply chain to obtain a comprehensive understanding of decision-making issues related to supply network configurations. These problems are most representative in the supply chain theory’s research and applications.
A particular real life supply chain considered in this study involves multi echelon and multi level distribution supply chains, each echelon with its own inventory capacities and multi product types and classes. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially in a real life situation where a multitude of aspects and functions should be taken into consideration.
In this dissertation, the simulation based heuristics solution method was implemented to effectively solve this integrated problem. A complex real life simulation model for managing the flow of material, transportation, and information considering multi products multi echelon inventory levels and capacities in upstream and downstream supply chain locations supported by an efficient Distribution Requirements Planning model (DRP) was modeled and developed named (LDNST) involving several sequential optimization phases. In calibration phase (0), the allocation of facilities to customers in the supply chain utilizing Add / Drop heuristics were implemented, that results in minimizing total distance traveled and maximizing the covering percentage. Several essential distribution strategies such as order fulfillment policy and order picking principle were defined in this phase. The results obtained in this phase were considered in further optimization solutions.
The transportation function was modelled on pair to pair shipments in which no vehicle routing decision was considered, such an assumption generates two types of transportation trips, the first being Full Truck Load trips (FTL) and the second type being Less Truck Load trips (LTL). Three integrated shipment consolidation heuristics were developed and integrated into the developed simulation model to handle the potential inefficiency of low utilization and high transportation cost incurred by the LTL.
The first consolidation heuristic considers a pure pull replenishment algorithm, the second is based on product clustering replenishments with a vendor managed inventory concept, and the last heuristic integrates the vendor managed inventory with advanced demand information to generate a new hybrid replenishment strategy. The main advantage of the latter strategy, over other approaches, is its ability to simultaneously optimize a lot of integrated and interrelated decisions for example, on the inventory and transportation operations without considering additional safety stock to improve the supply chain service levels.
Eight product inventory allocation and distribution strategies considering different safety stock levels were designed and established to be considered as main benchmark experiments examined against the above developed replenishment strategies; appropriate selected supply chain performance measures were collected from the simulation results to distinguish any trading off between the proposed distribution strategies.
Three supply chain network configurations were proposed: the first was a multi-echelon distribution system with an installation stock reorder policy; the second proposed configuration was Transshipment Point (TP) with a modified (s,S) inventory; and the last considered configuration was a Sub-TP, a special case from the second configuration. The results show that, depending on the structure of multi-echelon distribution systems and the service levels targets, both the echelon location with installation stock policy and advanced demand information replenishment strategy may be advantageous, and the impressive results and service level improvements bear this out.
Considering the complexity of modeling the real life supply chain, the results obtained in this thesis reveal that there are significant differences in performance measures, such as activity based costs and network service levels. A supply chain network example is employed to substantiate the effectiveness of the proposed methodologies and algorithms
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
Development of Genetic Algorithm-based Stochastic Model to Study and Optimize Single-echelon vs Multi-echelon Inventory Systems
A Thesis Presented to the Faculty of the College of Science and Technology Morehead State University in Partial Fulfillment of the requirements for the Degree Master of Science by Nadeera Ekanayake on November 18, 2013
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
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Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain
In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method
The impact of freight transport capacity limitations on supply chain dynamics
We investigate how capacity limitations in the transportation system affect the dynamic behaviour of supply chains. We are interested in the more recently defined, 'backlash' effect. Using a system dynamics simulation approach, we replicate the well-known Beer Game supply chain for different transport capacity management scenarios. The results indicate that transport capacity limitations negatively impact on inventory and backlog costs, although there is a positive impact on the 'backlash' effect. We show that it is possible for both backlog and inventory to simultaneous occur, a situation which does not arise with the uncapacitated scenario. A vertical collaborative approach to transport provision is able to overcome such a trade-off. © 2013 Taylor & Francis
On returns and network configuration in supply chain dynamics
This research focuses on how two common modeling assumptions in the Bullwhip Effect (BWE) literature (i.e., assuming the return of the excess of goods and assuming a serial network) may distort the results obtained. We perform a robust design of experiments where the return condition (return vs. no return) and the configuration of the Supply Chain Network (SCN) (serial vs. divergent) are systematically analyzed. We find an important interaction between these assumptions: the impact of returns on the BWE strongly depends on the SCN configuration. This study highlights the importance of accurately modeling SCNs to properly assess SCNs managers.Junta de Andalucía P08-TEP-0363
Multiobjective Coordination Models For Maintenance And Service Parts Inventory Planning And Control
In many equipment-intensive organizations in the manufacturing, service and particularly the defense sectors, service parts inventories constitute a significant source of tactical and operational costs and consume a significant portion of capital investment. For instance, the Defense Logistics Agency manages about 4 million consumable service parts and provides about 93% of all consumable service parts used by the military services. These items required about US50 billion. The US Army Institute of Land Warfare reports that, at the beginning of the 2003 fiscal year, prior to Operation Iraqi Freedom the aviation service parts alone was in excess of US10.5 billion in appropriations spent on purchasing service parts in 2000, the United States Air Force (USAF) continues to report shortages of service parts. The USAF estimates that, if the investment on service parts decreases to about US$5.3 billion, weapons systems availability would range from 73 to 100 percent. Thus, better management of service parts inventories should create opportunities for cost savings caused by the efficient management of these inventories. Unfortunately, service parts belong to a class of inventory that continually makes them difficult to manage. Moreover, it can be said that the general function of service parts inventories is to support maintenance actions; therefore, service parts inventory policies are highly related to the resident maintenance policies. However, the interrelationship between service parts inventory management and maintenance policies is often overlooked, both in practice and in the academic literature, when it comes to optimizing maintenance and service parts inventory policies. Hence, there exists a great divide between maintenance and service parts inventory theory and practice. This research investigation specifically considers the aspect of joint maintenance and service part inventory optimization. We decompose the joint maintenance and service part inventory optimization problem into the supplier s problem and the customer s problem. Long-run expected cost functions for each problem that include the most common maintenance cost parameters and service parts inventory cost parameters are presented. Computational experiments are conducted for a single-supplier two-echelon service parts supply chain configuration varying the number of customers in the network. Lateral transshipments (LTs) of service parts between customers are not allowed. For this configuration, we optimize the cost functions using a traditional, or decoupled, approach, where each supply chain entity optimizes its cost individually, and a joint approach, where the cost objectives of both the supplier and customers are optimized simultaneously. We show that the multiple objective optimization approach outperforms the traditional decoupled optimization approach by generating lower system-wide supply chain network costs. The model formulations are extended by relaxing the assumption of no LTs between customers in the supply chain network. Similar to those for the no LTs configuration, the results for the LTs configuration show that the multiobjective optimization outperforms the decoupled optimization in terms of system-wide cost. Hence, it is economically beneficial to jointly consider all parties within the supply network. Further, we compare the model configurations LTs versus no LTs, and we show that using LTs improves the overall savings of the system. It is observed that the improvement is mostly derived from reduced shortage costs since the equipment downtime is reduced due to the proximity of the supply. The models and results of this research have significant practical implications as they can be used to assist decision-makers to determine when and where to pre-position parts inventories to maximize equipment availability. Furthermore, these models can assist in the preparation of the terms of long-term service agreements and maintenance contracts between original equipment manufacturers and their customers (i.e., equipment owners and/or operators), including determining the equitable allocation of all system-wide cost savings under the agreement
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