Diversification strategy with random yield suppliers for a mean–variance risk-sensitive manufacturer

We consider the diversification strategy for a mean–variance risk-sensitive manufacturer with unreliable suppliers. We first analyze the linear model and find that the suppliers are selected according to the descending order of their contributed marginal expected profit, and increasing the manufacturer’s risk-averseness leads to a more even allocation of demand across the suppliers. Then, we study the general newsvendor model. By approximating the leftover inventory with a normal distribution, we establish the general properties of the active supplier set and show that the supplier selection rule is similar to that under the risk-neutral setting when the demand uncertainty is large. Moreover, we conjecture that the selection rule also applies when the demand uncertainty is low, which we verify with an extensive numerical study. Our paper makes two contributions: First, we establish the properties of the optimal diversification strategy and develop corresponding insights into the trade off between cost and reliability under the mean–variance framework. Second, we perform comparative statics on the optimal solution, with a particular emphasis on investigating how changes in the supplier’s cost or reliability affect the risk-averse manufacturer’s ordering decisions and customer service level.     

A three-level supply chain network design model with risk-pooling and lead times

This paper considers a single-sourcing network design problem for a three-level supply chain consisting of suppliers, distribution centers (DC’s) and retailers, where risk-pooling strategy and DC-to-supplier dependent lead times are considered. The objective is to determine the number and locations of suppliers and DC’s, the assignment of each location-fixed DC to a supplier and that of each retailer to a DC, which minimizes the system-wide location, transportation, and inventory costs. The problem is formulated as a nonlinear integer programming model, for which a two-phase heuristic solution algorithm is derived based on the Lagrangian relaxation approach. Numerical experiments show that the proposed heuristic is effective and also efficient.     

Multi-objective supplier selection and order allocation under disruption risk

We formulate a multi-objective MILP model to find the optimal choice of suppliers and their order quantity allocation under disruption risk. Suppliers are evaluated and ranked, based on the preference values obtained using a hybrid fuzzy AHP-fuzzy PROMETHEE. Multi-objective Particle Swarm Optimization is then applied to yield a set of Pareto-optimal solutions for the choice of suppliers and their order allocation. Numerical experimentation suggests that the supplier failure probability affects the expected total cost more than supplier flexibility and loss cost. Sensitivity analysis is performed on the failure probability, the output flexibility, and loss cost of the suppliers.     

Sourcing decisions under risks of catastrophic event disruptions

In this paper, a supplier selection problem is studied under risks of supplier failure due to the catastrophic events disruption. An analytical model is developed to determine the optimal number of suppliers considering different failure probability, capacity, and compensation. An algorithm is designed to find the optimal solution and numerical study is carried out to illustrate the model. Results of numerical study and sensitivity analysis provide useful guidelines for managers to select the optimal number of suppliers under the risks of supply disruption.     

The capacitated plant location problem with customers and suppliers matching

This paper introduces a new problem called the capacitated plant location problem with customer and supplier matching (CLCSM). The product distribution from plants to customers and the material supply from suppliers to plants are considered together. We merge a distribution trip and a supply trip into one triangular trip for saving allocation cost. Vehicles from plants visit a customer and a supplier for each trip. We provide a heuristic solution procedure based on Lagrangian relaxation. Computational results indicate that the proposed heuristic solution procedure is shown to be efficient yielding optimal or near-optimal solutions for randomly generated instances.     

Integrating the warehousing and transportation functions of the supply chain

Electronic commerce and associated business-to-business transaction capabilities have changed the way in which supply chains operate. The Internet has enabled information exchange on an unprecedented scale, often at a pace too fast for normal consumption. Companies are not equipped to make effective use of data from warehouse management systems––which contain information on supplier/customer warehouse inventory levels and key customer ordering patterns––and transportation management systems––within which information pertaining to the location of important supply chain assets such as products or vehicles is typically stored. These systems are key factors in integrating the physical flow of goods along the supply chain. The integration of these systems leads to global inventory visibility, which, in turn, leads to reduced costs and improved customer service by decreasing shipping and receiving cycle times, increasing shipment and inventory accuracy, and decreasing lead-time variability. This paper examines the total cost benefits that can be achieved by suppliers and warehouses through the increased global visibility provided by an integrated system. We develop a discrete event simulation model of a multi-product supply chain to examine the potential benefits to be gained from global inventory visibility and trailer yard dispatching and sequencing techniques. Experimental results demonstrate the potential for this integrated paradigm to improve customer service through improved efficiencies, reduced costs, and reduced lead-time variability.     

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.     

Hinterland operations of sea ports do matter: Dry port usage effects on transportation costs and CO2 emissions

Decreasing carbon dioxide (CO₂) emissions is one of the most important tasks for the society in the 21st century. One possibility to decrease emissions originating from transportation is to utilize more rails instead of relying simply on road transportation. In the dry port concept an inland intermodal terminal is connected to a sea port using railways. This study analyzes impacts of dry ports in a Finnish context. We compare two different configurations: In the first one shippers drive directly to a sea port, while in the second one they use dry ports. The systems are evaluated by using discrete-event simulation. In the systems we are interested in two issues: (1) Level of CO₂ emissions, and (2) Costs to transport the goods in different configurations. We use different scenarios for future energy prices and estimate both the costs and CO₂ emission development in these scenarios. We also compare the results to a situation, where emissions are minimized instead of costs. Implications on larger scale are also discussed, for example in the Baltic Sea and North Sea area, where strict sulfur emission restrictions are seen to harm sea transport and increase concentration on small number of sea ports.     

Coordinated inventory replenishment and outsourced transportation operations

We consider a one-warehouse N retailers supply chain with stochastic demand. Inventory is managed in-house whereas transportation is outsourced to a 3PL provider. We develop analytical expressions for the operating characteristics under both periodic and continuous joint replenishment policies. We identify the settings where a periodic review policy is comparable to a continuous review one. In our numerical test-bed, the periodic policy performed best in larger supply chains operating with larger trucks. We also observed that if the excess utilization charge is less than 25%, outsourcing becomes beneficial even if outsourcing cost is 25% more than the in-house fleet costs.     

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.     

A study on pricing and delivery strategy for e-retailing systems

This study investigates the dynamics between price and lead time for an e-retailing system in which one of its commodities is offered by two duopolistic suppliers. A Stackelberg game is formulated by considering the two suppliers as the leaders and the e-retailer as the follower. The proposed model assists the channel members in getting an equilibrium relationship in a competitive environment. The results suggest that when a supplier chooses a shorter lead time as the competitive strategy, the other supplier should choose a lower price for counteraction, and channel members should understand the characteristics of demand before promoting their commodities.     

Robust optimization for United States Department of Agriculture food aid bid allocations

The U.S. Department of Agriculture (USDA) currently uses a bidding system to determine carriers and suppliers that would partner in providing food aid annually in response to global emergencies and famine. We mimic the USDA approach via a robust optimization model featuring box and ellipsoid uncertainty frameworks to account for uncertainties in demand, supplier and carrier bid prices. Through a case study utilizing historical invoice data, we demonstrate our model applicability in improving ocean carrier and food supplier bid pricing strategy and similar supply chain network optimization problems. Through a validation algorithm we demonstrate the value of our robust models.     

Multi-level inventory management decisions with transportation cost consideration

In this article we address specific inventory management decisions with transportation cost consideration in a multi-level environment consisting of a supplier–warehouse–retailers. We develop two models – namely, decentralized ordering model and centralized ordering model to investigate the effect of collective ordering by retailers on the total inventory cost of the system. A numerical study shows that the proposed model is robust and generates reasonable cost savings. The models have potential in several multi-level applications such as fresh or frozen food delivery to stores of different supermarkets or the supply of medicine to a number of hospitals from a wholesaler.     

供应服务商资金支付结算方式的优化管理

为降低资金成本和提高资金使用效率,上海铁路局优化供应服务商资金支付结算方式管理。通过支付结算和资金融资结合的手段,利用商业汇票和现金折扣两种方式,提高资金筹集能力,节约资金成本,实现路企银合作共赢。     

Analysis of traffic congestion costs in a competitive supply chain

We study a competitive facility location problem with identical suppliers competing in multiple markets subject to distribution network congestion. We first analyze the solution of a symmetric competitive location game and discuss the firms’ facility location and equilibrium market supply decisions. Then, we utilize the model to provide analytical characterization of the effects of traffic congestion costs on equilibrium distribution flows. These analytical results permit qualitative characterization of how changes in congestion levels and costs affect equilibrium location and distribution decisions. The results of extensive numerical studies serve to further illustrate the effects of traffic congestion costs on location, market supply quantity, and distribution decisions.     

Truthful multi-unit multi-attribute double auctions for perishable supply chain trading

This paper aims to propose multi-attribute double auctions for perishable supply chain trading (PSCT). We first construct a multi-unit/single-unit multi-attribute double auction (MS-MDA) for PSCT where suppliers can submit bids on a single unit of one item (i.e., single output restriction). We then relax the single output restriction and propose a multi-unit multi-attribute double auction (M-MDA) for PSCT in which each supplier offers multiple units of one item. Both the MS-MDA and M-MDA mechanisms are incentive compatible, individually rational, budget balanced and computationally efficient. The computational study shows that all proposed mechanisms are of high allocation efficiency and practically implementable.     

Effect of variable transportation and carbon emission in a three-echelon supply chain model

Several industries controls carbon emission during transporting products due to increased transportation for obtaining the best transportation way with reduced cost. This study considers a three-echelon supply chain model where the supplier makes semi-finished products and transports to manufacturer for finished products. The manufacturer transports products by single-setup-multi-delivery policy to multi-retailer. The aim of the model is to reduce the supply chain cost by considering variable transportation and carbon emission costs are considered due to several shipments. An algebraic approach is employed to obtain the closed-form solution. Numerical example, sensitivity analysis, and graphical representations are given to illustrate the model.     

Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.     

Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system.