Total Network Cost Research Articles

A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm

The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental-related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time.

Optimal design of inter-plant hydrogen networks with intermediate headers of purity and pressure

This paper addresses the optimal design of inter-plant hydrogen networks with intermediate headers of purity and pressure. A superstructure for inter-plant integration of hydrogen networks is presented, where all hydrogen sources and hydrogen sinks are indirectly matched with each other through intra-plant hydrogen headers and inter-plant hydrogen headers. The corresponding mathematical programming model is constructed and a tailored two-step strategy is proposed to solve the model. In this solving method, the topology of the inter-plant hydrogen network and the purity levels of the hydrogen headers are first determined to minimize the cost of hydrogen utilities. The placements of compressors and pressure levels of hydrogen headers are then optimized to reach the lowest total annual cost of the inter-plant hydrogen network. The application of the proposed method is illustrated via a practical industrial case of inter-plant hydrogen network in China.

Multi-Objective Optimization for Green Dual-Channel Supply Chain Network Design Considering Transportation Mode Selection

A well-designed supply chain network should not only meet the efficient cost but also realize the sustainable effect on environment. The purpose of this article is to develop a multi-objective model to capture the trade-off between total cost and environmental performance in the green dual-channel supply chain network. Moreover, the transportation mode has been considered as a decision variable. With regard to the complexity of such network, a new swarm intelligence algorithm known as a multi-objective particle swarm optimization (MOPSO) algorithm has been employed to tackle this problem. The effectiveness of the present model and approach is evaluated by a numerical experiment, and the results show that the added environmental performance is actually proportional with the increased cost. Additionally, the comparison between different mode decisions shows that a better trade-off between two objectives will be obtained when considering the transportation mode selection.

A multi-objective optimization model for designing resilient supply chain networks

Supply chains evolve over time: they expand via planned construction and/or corporate mergers and acquisitions, and contract due to required facility closures, partnership terminations, and/or other cost-cutting decisions. In addition, businesses also operate in an uncertain world wherein network and other design decisions must be made despite the reality of unforeseen future events that can and often do disrupt or damage corporate supply chains. We present a multi-objective network design model and accompanying optimization-based decision support methodology for supply chain architects. Our methodology helps to evaluate the trade-off between total network cost minimization and maximizing overall supply chain network connectivity. Decision makers can evaluate a collection of solutions with different cost and connectivity values using our methodology and choose the network configuration that best serves the needs of their organization. Though our multi-objective network design model is applicable for individual companies looking to expand or contract their internal supply chains, we demonstrate our model's efficacy through the lens of a practically-motivated corporate merger and acquisition activity.

Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation

Abstract The aim of this research is to consider the issue of sustainability in designing a blood supply chain network by presenting a multi objective mixed integer mathematical programming model that aims to simultaneously minimize the total cost of the supply chain network and the total environmental impacts of the activities of the supply chain network. As the nature of supplying the blood by the donors and also demand for the blood product are uncertain, a robust optimization approach is applied in the model to deal with this type of uncertainty. To convert the proposed multi objective model into a single objective one, the bounded objective function method is used. Then, as the presented mathematical model is a complicated mixed integer linear programming model, an algorithm based on the Lagrangian relaxation approach is proposed to solve the model. At the end, a computational study is done to present the competency of the proposed Lagrangian relaxation algorithm.

A graph-based cost model for supply chain reconfiguration

The emergence and growth of globalization require manufacturing enterprises to reconfigure their manufacturing systems and supply chains quickly and cost-effectively in response to market changes. In this paper, a supply chain reconfiguration problem for manufacturing enterprise systems was investigated. An optimization model was formulated to minimize the total cost of the supply chain network with the consideration of manufacturing operations and reconfiguration cost. To effectively model the reconfiguration cost of the supply chain, a graph-based method was proposed to characterize the similarity between supply chain configurations and to establish the relationship between the similarity index and reconfiguration cost. A case study on a supply chain for laptop computer manufacturing was presented to illustrate the proposed method and demonstrate its effectiveness.

Analysis of Relay-Assisted OFDMA Cellular Systems with Different Frequency Reuse Techniques

Co-channel interference (CCI) degrades performance of cellular networks. Therefore, this paper addresses CCI problem in relay-assisted (R-A) orthogonal frequency division multiple access cellular systems. Analytical treatments are conducted. The network performance improvements through reducing CCI effects are evaluated using different four interference mitigation models. These models represent R-A strict fractional frequency reuse (FFR) technique with frequency reuse factor (FRF) \(=\) 3, R-A strict FFR technique with FRF \(=\) 4, R-A sectored FFR technique and R-A soft frequency reuse (SFR) technique. Each model contains two different configurations for further network performance improvement. The first configuration assumes three relay stations (RSs) per cell (RPC) while the other one proposes 6 RPC. The best RSs locations in terms of SIR are proposed. Moreover, closed-form expressions for worst cases CEU’s SIR, cell centre user’s SIR and inner radius are implemented. These expressions are used to compare different models using various performance evaluation metrics. The results demonstrate that the two configurations in R-A sectored FFR and R-A strict FFR models provide equal CEU’s SIR. The reason of this result can be attributed to applying equal power transmission strategy. Moreover, the two configurations of R-A SFR network attain different CEU’s SIR values. This result can be accredited to the SFR power control factor. The work outcomes attain much higher CEU’s SIR improvement. Therefore, the outage probability is decreased. Thus, more users can be covered by the network. Accordingly, the total network cost is reduced. So, this treatment improves the network performance while keeping lower network cost.

Distributed extremum-seeking control over networks of dynamically coupled unstable dynamic agents

In this paper, a distributed extremum seeking control technique is proposed to solve a class of real-time optimization problems over a network of dynamic agents with unknown unstable dynamics. Each dynamic agent measures a cost that is shared over a network. A dynamic average consensus approach is used to provide each agent with an estimate of the total network cost. The extremum seeking controller operates at each agent to allow each agent to contribute to the optimization of the total cost, in a cooperative fashion. The extremum seeking control technique is based on a proportional–integral approach that provides improvements in transient performance over standard techniques. The contribution of the proposed technique is to solve the simultaneous stabilization and real-time optimization. A dynamic network simulation example is presented to demonstrate the effectiveness of the technique.

T-Trail: Link Failure Monitoring in Software-Defined Optical Networks

Monitoring trail (m-trail) provides a striking mechanism for fast and unambiguous link failure localization in all-optical networks. However, allocating dedicated supervisory lightpaths (m-trail) undoubtedly increases total network cost. Accordingly, how to maximally reduce monitoring cost in an optical network is an important issue. To this end, we propose a concept of traffic trail (t-trail) that uses traffic lightpaths, instead of dedicated supervisory lightpaths, to localize a single link failure in the context of a software-defined optical network (SDON). The central controller of an SDON collects routing information of all t-trails in the network. Thus, any link failure can be localized according to the ON–OFF status of the traversing t-trails. We first formulate the problem as an integer linear programming (ILP) model. Since the ILP is not feasible for solving the problem in large-size networks, an efficient heuristic algorithm t-trail allocation (TTA) is proposed to address it. We conduct extensive simulations to evaluate the performance of TTA. The results show that compared with the existing m-trail schemes, TTA can reduce total costs by 20.91% on average.

Modular recycling supply chain under uncertainty: a robust optimisation approach

It is estimated that recycling can avert approximately 50% annual landfill cost, while simultaneously recovering lost materials valued at 4 to 9.5% of the total logistics network cost. This study proposes a robust integrated reverse logistics supply chain planning model with a modular product design at different quality levels. A mixed-integer programming (MIP) model is formulated to maximise the profit by considering the collection of returned products, the recovery of modules and the proportion of the product mix at different quality levels. This paper proposes the collection of returnable items (end-of-life, defective and under-warranty products) through retail outlets and the appropriate recovery of modules to manage these using a network of recovery service providers. The modular product design approach is adopted to create design criteria that provide an improved recovery process at a lower cost. This robust model seeks solutions close to the mathematically optimal solutions for a set of alternative scenarios identified by a decision-maker. The efficacy of the proposed model is evaluated by a given set of variously sized numerical expressions and sensitivity analyses. A robust solution is found that appraises the impact of two major sources of uncertainty, demand rate and the volume of returned products of a key recycled material.

Interplant heat exchanger network synthesis using nanofluids for interplant heat exchange

Heat transfer between different processes or inter-plant heat integration can be seen as an efficient way to cost-efficiently improve the energy efficiency of a system of different processes. Nanofluids are a new type of heat transfer fluids, in which particles with size of 1–100 nm are suspended in a liquid. Nanosized particles can cause considerable enhancement in convective heat transfer performance of the base fluid, although at the same time they increase the viscosity of the fluid, thus enhancing the needed pumping power. In this work we study the effect of using nanofluids in streams transferring heat from different processes by optimizing the total annual cost of a heat exchanger network. These costs include the cost of hot and cold utilities, heat exchanger investment costs and pumping costs. A modified version of the well-known Synheat superstructure is used as the optimization model in comparing the different fluids (water and five nanofluids) in two examples. Some key parameters (electricity price and annuity factor) are varied in these two examples. The results show that nanofluids can in some cases save total annual costs and especially if electricity prices are low compared to other factors. This is true especially for MgO1.0% which outperformed water and the other nanofluids in normal price conditions. But altogether it is evident that most, and in some cases all, of the benefits provided by nanofluids to improved heat transfer is canceled out by the increased pressure drops.

Robust reliable humanitarian relief network design: an integration of shelter and supply facility location

The human societies are threatened by natural disasters. Thus, preparedness and response planning is necessary to eliminate or mitigate their negative effects. Relief network design plays an important role in the efficient response to the affected people. This paper addresses the problem of relief logistics network design under interval uncertainty and the risk of facility disruption. A mixed-integer linear programming model is proposed (1) to consider distribution center (DC) disruption (2) to support the disrupted DC by backup plan (3) to take in to the account both supply and evacuation issues (4) and finally, to mitigate disruption impact by investment. Moreover, robust optimization methodology is applied to hedge against uncertain environments. We conduct computational experiments by using generated instances and a real-world case to perform sensitivity analysis and provide managerial insights. The results show that the total cost of relief network increases by increasing the conservatism level. Moreover, the result show that the total cost of the network can be decrease by reducing the interval of uncertain parameters. As a result, providing more information and better estimation about uncertain parameters can reduce network costs. Disruption probability effect is also investigated and the result indicates that the network tries to establish more reliable facilities as the disruption probability increases. To demonstrate superiority of reliable network described in this paper over the classic network, a Monte Carlo procedure is used to compare two networks and results confirmed superiority of reliable network.

Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network

In the last few decades, production and procurement of food grain in India have steadily increased, however, storage capacity has not increased proportionally. The government of India (GOI) is establishing the various capacitated silos across the country to bridge this storage capacity gap. This paper presents a novel integrated multi-objective, multi-modal and multi-period mathematical model for grain silo location-allocation problem with Dwell time to support the decision-making process of GOI. Two conflicting objectives-minimization of total supply chain network cost and total lead time (transit and dwell time) are simultaneously optimized using two Pareto based multi-objective algorithms with calibrated parameters.

An evaluation of public truck terminal implementation and truck ban policy in a medium-size city of Thailand: a case study of Khon Kaen city

This study aims at investigation the potential impacts of a public truck terminal implementation along with truck ban policy adaptation in Khon Kaen City, Thailand. A four-step model is developed and used for examining traffic condition changes due to the terminal relocation. Three different terminal locations are set. Truck traffic of each scenario are estimated and converted to total network cost, and then compared with the base-case scenario. The most suitable truck terminal location is suggested. Also, several concerns regarding the public truck terminal and truck ban policy in the study area are given.

A Simple Congestion-Aware Algorithm for Load Balancing in Datacenter Networks

We study the problem of load balancing in datacenter networks, namely, assigning the end-to-end data flows among the available paths in order to efficiently balance the load in the network. The solutions used today rely typically on an equal-cost multi path (ECMP) mechanism, which essentially attempts to balance the load in the network by hashing the flows to the available shortest paths. However, it is well-known that the ECMP performs poorly when there is asymmetry either in the network topology or the flow sizes, and thus, there has been much interest recently in alternative mechanisms to address these shortcomings. In this paper, we consider a general network topology where each link has a cost, which is a convex function of the link congestions. Flows among the various source–destination pairs are generated dynamically over time, each with a size (bandwidth requirement) and a duration. Once a flow is assigned to a path in the network, it consumes bandwidth equal to its size from all the links along its path for its duration. We consider low-complexity congestion-aware algorithms that assign the flows to the available paths in an online fashion and without splitting . Specifically, we propose a myopic algorithm that assigns every arriving flow to an available path with the minimum marginal cost (i.e., the path which yields the minimum increase in the network cost after assignment) and prove that it asymptotically minimizes the total network cost. Extensive simulation results are presented to verify the performance of the myopic algorithm under a wide range of traffic conditions and under different datacenter architectures. Furthermore, we propose randomized versions of our myopic algorithm, which have much lower complexity and empirically show that they can still perform very well in symmetric network topologies.

Flexible supply chain network design under uncertainty

Flexibility in supply chain networks dealing with uncertainty, has become a research challenge over the past years. This work proposes a flexible supply chain network design (SCND) model that uses generalized production/warehousing nodes instead of individual production plants and warehouses while conquers with demand uncertainty using a scenario-based approach. It also deals with inventory management and decisions on strategical and tactical level (facility location, production rate, warehouse capacity, demand allocation between generalized nodes, inventory levels, product flows, suppliers’ product availability and links between all facilities). The proposed Mixed-Integer Linear Programming (MILP) model allows intra-layer flows between generalized nodes and aims at minimizing total network cost. A case study is formed to test the applicability of the model for a medium sized European company. A comparison was made between a classic supply chain network and generalized network that deals with uncertainty. Results have revealed cost benefits for this model, making it not only applicable, but also cost effective for the company that will apply it. This decision support system, can help managers in taking strategic decisions such as facility location, with a higher level of accuracy.

Optimal design of hub-and-spoke networks with access to regional hub airports: a case for the Chinese regional airport system

ABSTRACTIn recent years, China has experienced unprecedented growth in air travel demand. However, existing hub airports have not been able to satisfy the increasing traffic, resulting in serious congestion and/or flight delays. On the other hand, regional hub airports located near hub airports have been increasingly utilized. This paper examines the factors that influence the emergence of regional hub airports, and determines the potential regional hubs base on an improved p-hub locating problem model. With the empirical investigation, one result is, for instance, that Nanjing Lukou International Airport is identified to be the optimal regional hub airport for Pudong International Airport, China's busiest hub airport. Results also show that utilizing a regional hub airport would enhance the network performance in terms of reduced total network cost and congestion at the hub airport. It is also found that Low-Cost Carriers (LCC) would benefit from the optimized network.

Optimal Synthesis of Water Networks for Addressing High-Concentration Wastewater in Coal-Based Chemical Plants

This paper outlines the development of an optimization-based method for synthesizing a water network, which incorporates various treatment technologies to address the high-concentration wastewater in coal-based chemical plants. One important feature of the proposed approach is that it associates a multistep wastewater treatment design within a source–regeneration–sink superstructure. This design can enforce certain design and structural specifications to tighten the model formulation and enhance solution convergence. A mixed integer nonlinear programming problem is formulated based on the proposed superstructure, which involves unit-specific shortcut models instead of the fixed impurities removal model to describe it accurately. The proposed method for water network synthesis is demonstrated using two case studies, which determine the effect of streams composition and wastewater treatment technologies on the total network cost, freshwater consumption, and water network design. The results highlight the ab...

Regional low-carbon timber logistics network design and management using multi-objective optimization

ABSTRACTWith the concern about environmental issues, supply chain designers and managers need to make trade-offs between the environmental impacts and costs of a logistics network. We develop a multi-objective optimization model that minimizes the total cost and CO2 emissions of a regional timber logistics network and seeks for the simultaneous optimization of logistics network design and operation. The generic model is applied to a case study in Jiangle County in China. The Pareto solution set, derived by applying the normalized normal constraint method, shows the relationship between carbon emissions and total cost. Our results reveal that both network structure and the allocations of timber flows have effects on the Pareto solutions, suggesting the importance of considering network design and management together in logistics network analysis. The model results also indicate that though the current carbon price is hard to trigger the change of the timber logistics network structure in Jiangle County, it can still be a driving force to push the decision-makers to implement lower carbon logistics plans. Additionally, sensitivity analysis shows that improving road conditions can remarkably reduce CO2 emissions in the logistics network and that maintaining an appropriate ratio of timber supply to demand can lead to some compromise between carbon emissions and total cost. These findings offer new insights into the trade-offs between the carbon emissions and total cost of the regional timber logistics network, which are of value to logistics network designers and managers as well as policy-makers in the study region and beyond.

After-sales services network design of a household appliances manufacturer

After-sales services have become high-margin businesses that account for larger portions of corporate profits. Delivering the after-sales services is challenging as after-sales services supply chains are significantly different than production–distribution supply chains. The literature provides little guidance on the use of quantitative methods for after-sales services network design. We present a mixed integer linear programming problem formulation to determine warehouse locations, assign repair vendors to facilities and choose mode of transportation while minimizing the total network cost. We transform the large-scale real-life problem of a household appliances manufacturer into a smaller scale to solve optimally in reasonable time. Through a scenario-based approach, we evaluate different configurations of a decentralized network with choices of transportation mode. We test the sensitivity of the solutions. The total cost decreases with additional choices of transportation mode and only slightly increases with the next-day delivery policy while the network solution may change significantly.