An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain

This paper proposes a multi-objective possibilistic programming model to design a sustainable medical supply chain network under uncertainty considering conflicting economic, environmental and social objectives. Effective social and environmental life cycle assessment-based methods are incorporated in the model to estimate the relevant environmental and social impacts. An accelerated Benders decomposition algorithm utilizing three efficient acceleration mechanisms is devised to cope with computational complexity of solving the proposed model. Computational analysis is also provided by using a medical industrial case study to present the significance of the proposed model as well as the efficiency of the accelerated Benders decomposition algorithm.     

A supply chain network design model for biomass co-firing in coal-fired power plants

We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.     

Multistage optimization of the supply chains of biofuels

In this study, a mathematical model that integrates spatial and temporal dimensions is developed for strategic planning of future bioethanol supply chain systems. The planning objective is to minimize the cost of the entire supply chain of biofuel from biowaste feedstock fields to end users over the entire planning horizon, simultaneously satisfying demand, resource, and technology constraints. This model is used to evaluate the economic potential and infrastructure requirements for bioethanol production from eight waste biomass resources in California as a case study. It is found that, through careful supply chain design, biowaste-based ethanol production can be sustained at a compatible cost around $1.1 per gallon.     

Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical analysis show that the model selects to use intermodal hubs located in areas with low disruption probabilities. In case of a disaster, the reliable solution results in 6.21% savings over the minimum cost solution.     

Supplier hoarding,government intervention,and timing for post-disaster crop supply chain recovery

This work presents a multi-methodological approach to address the issue of post-disaster crop supply chain recovery under the influences of government intervention and supplier hoarding intention. A conceptual model which characterizes the antecedents of supplier hoarding intention is proposed, and validated using survey data. Grounded in the empirical study, an analytical model is then proposed for decision analysis of a two-tier crop supply chain recovering from post-disaster supply disruptions. Analytical results indicate that hoarding behavior reduces the time taken by supply chain members for supply recovery; and however, contributes to mixed effects on the expected profits of supply chain members.     

Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study

A mixed-integer, non-linear model is developed for designing robust global supply chain networks under uncertainty. Six resilience strategies are proposed to mitigate the risk of correlated disruptions. In addition, an efficient parallel Taguchi-based memetic algorithm is developed that incorporates a customized hybrid parallel adaptive large neighborhood search. Fitness landscape analysis is used to determine an effective selection of neighborhood structures, while the upper bound found by Lagrangian relaxation heuristic is used to evaluate quality of solutions and effectiveness of the proposed metaheuristic. The model is solved for a real-life case of a global medical device manufacturer to extract managerial insights.     

A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints

This paper presents a location-inventory-pricing model for designing the distribution network of a supply chain with price-sensitive demands and inventory-capacity constraints. The supply chain has market power and uses markup pricing. An efficient Lagrangian relaxation algorithm is proposed to solve the model. Our numerical study shows that by moderately increasing the number of possible values for pricing decisions, the model can be used to find near-optimal solutions of a similar location-inventory-pricing problem with continuous pricing decisions. The approach used here to incorporate pricing decisions can be applied to other supply-chain design and planning problems with price-sensitive demands.     

Bioethanol supply chain system planning under supply and demand uncertainties

A mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment. The two-stage stochastic programming model, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production. The model results show that biowaste-based ethanol can be a viable part of sustainable energy solution for the future.     

Stochastic optimization of sustainable hybrid generation bioethanol supply chains

This paper focuses on designing a hybrid generation bioethanol supply chain (HGBSC) that will account for economic, environmental and social aspects of sustainability under various uncertainties. A stochastic mixed integer linear programming model is proposed to design an optimal HGBSC. A case study set in the state of North Dakota in the United States is used as an application of the proposed model. The results suggest that the designs of optimal HGBSC change when different sustainability standards are applied. In addition, sensitivity analysis is conducted to provide deeper understanding of the proposed model.     

模糊随机环境下再利用逆向物流网络设计

考虑模糊随机环境下,再利用逆向物流网络在数量方面的不确定性,并考虑了回收点与回收中心的回收周期不同步等复杂因素,提出了第三方构建再利用逆向物流网络的数学模型。该模型结合遗传算法、模糊随机模拟及数学规划的混合智能算法,并通过算例验证模型和算法的可行性,得到模糊随机环境下的最优选址结果和回收点最优回收周期。     

Synchromodal hinterland freight transport: Model study for the port of Rotterdam

This paper develops a model that enables comparative analysis of intermodal and synchromodal operations from economic, societal, and environmental perspectives. The model captures relevant (day-to-day and within-day) dynamics in freight transport demand and supply, flexible multimodal routing with transfers and transhipments. The capacitated schedule-based assignment algorithm operating specifically at path level allows strategic modelling and evaluation accounting for the freight transport system at operational level. The Rotterdam hinterland container transport case study shows that synchromodal system is likely to improve transport service level, capacity utilization, and modal shift, but not to reduce delivery costs.     

A spatially explicit techno-economic model of bioenergy and biofuels production in California

This study presents a spatially explicit techno-economic Bioenergy Siting Model (BSM) of the bioenergy production system in California. The model describes the bioenergy system in terms of facility siting and size, conversion technology, feedstock profile, and feedstock supply chain configuration for the year 2015. The BSM expands upon previous bioenergy siting work by optimizing the system using spatially explicit feedstock supply curves, multiple potential conversion technologies and geographically determined bioenergy demand. We present sensitivity analysis demonstrating the effect of market and policy change scenarios. The model couples transportation network analysis using a Geographic Information System (GIS) with a mixed integer-linear programming (MIP) optimization model. Scenario results show total biomass resource utilization between 18 and 25 million dry tons annually at biofuel prices from $2.20 to $4.00/gallon of gasoline equivalent.     

An inventory-based simulation model for annual-to-daily temporal freight assignment

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF³ data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.     

Sailing speed optimization for container ships in a liner shipping network

This paper first calibrates the bunker consumption - sailing speed relation for container ships using historical operating data from a global liner shipping company. It proceeds to investigate the optimal sailing speed of container ships on each leg of each ship route in a liner shipping network while considering transshipment and container routing. This problem is formulated as a mixed-integer nonlinear programming model. In view of the convexity, non-negativity, and univariate properties of the bunker consumption function, an efficient outer-approximation method is proposed to obtain an ε-optimal solution with a predetermined optimality tolerance level ε. The proposed model and algorithm is applied to a real case study for a global liner shipping company.     

Reliable evacuation planning via demand inflation and supply deflation

This paper presents an evacuation route planning model that both accounts for demand uncertainty (i.e. the number of evacuees) as well as capacity uncertainty (i.e. the road capacities). To ensure reliability, the model plans for more evacuees (i.e. demand inflation) and less road capacity (i.e. supply deflation). A major contribution is that we provide a framework to determine the amount of demand inflation/supply deflation necessary to ensure a user-specified reliability level. The model is shown to be a natural generalization of previously proposed evacuation models. A small numerical case study reveals the key characteristics of the model.     

Responses of air cargo carriers to industrial changes

This study examines how product characteristics, values, inventory cost, shipping charges, shipping distance, and time affect an international firm's choice of air carrier. An individual choice model is constructed by assuming that the shipper in a specific industry chooses the optimal air cargo carrier with the minimal logistics cost. The study further aggregates air cargo demands on different routes for the carriers by considering the spatial distribution of the origin-destination pattern and any temporal changes in the industrial structure. A case study is used to illustrate the application of the proposed model using data from Taiwan Taoyuan International Airport and the industrial economics database in Taiwan. The results show that shippers with high product value and short delivery distance focus on the shipping charge and prefer choosing the air cargo carrier that offers more flights. Further a carrier may achieve a larger market share if its supply attributes match the industrial structure and the product characteristics of the market on the route. Finally, because dynamic changes in the industrial structure and product value have been captured, the results are more accurate than that from the Grey model.     

Reliable design of an integrated supply chain with expedited shipments under disruption risks

This paper studied the design of a two-echelon supply chain where a set of suppliers serve a set of terminals that receive uncertain customer demands. In particular, we considered probabilistic transportation disruptions that may halt product supply from certain suppliers. We formulated this problem into an integer nonlinear program to determine the optimal system design that minimizes the expected total cost. A customized solution algorithm based on Lagrangian relaxation was developed to efficiently solve this model. Several numerical examples were conducted to test the proposed model and draw managerial insights into how the key parameters affect the optimal system design.     

Comparison of royalty methods for build–operate–transfer projects from a negotiation perspective

This study constructs a royalty negotiation model for the bi-level programming (BLP) problem and develops a heuristic algorithm for solving the BLP problem. Concession rate, learning effect, and the time value discount rate are integrated into the proposed algorithm to reflect an authentic negotiation process. A case study is employed to simulate the negotiation behavior of two parties and alternative royalty strategies are discussed. Analytical results indicate that the two parties acquire the best negotiation result during the fifth negotiation. The operational revenue-based royalty model is more preferred by governments, while concessionaires favor more the operational output-based royalty model.     

The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan

Recently, many companies have recognized the concepts of green supply chain management or supply chain environmental management. However, relatively little research attention has been devoted to the consideration of relations between greening the supply chain, green innovation, environmental performance and competitive advantage. Hence, this paper aims to bridge this gap by providing empirical evidence to encourage companies to implement green supply chain and green innovation in order to improve their environmental performance, and to enhance their competitive advantage in the global market. A model is constructed to link the aforementioned constructs. Data were collected through a questionnaire-based survey across 124 companies from eight industry sectors in Taiwan. The data are analyzed using Structural Equation Modeling and the results from the final measurement model are used to evaluate the structural model that verifies the significance of the proposed relationships. A prominent result of this study is that greening the supplier through green innovation contributes significant benefits to the environmental performance and competitive advantage of the firm.     

A model for taxi pooling with stochastic vehicle travel times

Taxi pooling means the sharing of a taxi by more than one passenger with at least a semi-common route. Currently, a trial-and-error process is adopted, taking into account expected travel times, for taxi pooling, a method which is neither effective nor efficient. This is because stochastic disturbances arising from variations in the taxi travel times that occur in actual traveling are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule can be disturbed enough to lose its optimality. Therefore, in this study, a network flow technique is used to construct a stochastic taxi pooling model incorporating stochastic vehicle travel times. A solution algorithm is also proposed to efficiently solve the problem. The performance of the proposed models and the use of the solution method in practice are evaluated by executing numerical tests using real data with suitable assumptions. The test results show that the stochastic model and the solution method could be useful references for practice.