Unit Transportation Cost Research Articles

APPLICATION OF A SYSTEM DYNAMICS MODEL TO IMPROVE THE PERFORMANCE OF MAKE-TO-ORDER PRODUCTION

This study provides a system dynamics (SD) model of make-to-order (MTO) production and discusses the key factors of production improvement. The proposed system can be divided into three subsystems: income/cost, order/production, and human resources (HR). The time delay between customer demand, production demand, order quantity, material demand, and inventory is considered in a practical application. In addition, this paper considers how the cycle time is affected by the total input of HR; how unit transportation cost is influenced by the delivery quantity; and how unit penalty (shortage) cost is affected by the amount of shortage. The production capacity, yield, and holding cost needed to satisfy practical demands are all considered. A simulation approach to MTO production for meeting contract requests is presented in this study. Simulation results reveal that the amount of shortage will be the most important factor affecting the policy for the replenishment of material. Although the rise in production capacity leads to a reduced amount of shortage, it does not play a significant role. A sensitivity analysis of the replenishment of material policy is conducted to find out the best suggested policy. The SD model is also shown to quickly simulate changes in system behaviour, which allows an organisation enough time to respond to and conquer any unpredictable situation that might occur.

A fixed-charge transportation problem in two-stage supply chain network in Gaussian type-2 fuzzy environments

This paper presents two mathematical models representing imprecise capacitated fixed-charge transportation problems for a two-stage supply chain network in Gaussian fuzzy type-2 environment. It is a two-stage transportation process from a manufacturing center to m potential distribution centers (DCs) and then from DCs to business centers of n retailers with particular demands. Retailers are situated at some distances apart. Here unit transportation costs, fixed charges, availabilities, and demands are imprecise and represented by Gaussian type-2 fuzzy numbers. The proposed models are formulated as profit maximization problems in such a way that some DCs are selected in order to satisfy the demands at all retailers. The type-2 fuzziness has been removed by using generalized credibility measure developed with the help of CV-based reduction method and hence the models are reduced to chance constrained programming problems with different credibility labels. The deterministic models are then solved using both genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover with uniform mutation and modified particle swarm optimization (PSO), where the position of each particle is adjusted according to its own experience and that of its neighbors. Finally models are illustrated with some numerical data. Some sensitivity analyses on the proposed models are presented.

The multiple container loading problem with preference

An international audio equipment manufacturer would like to help its customers reduce unit shipping costs by adjusting order quantity according to product preference. We introduce the problem faced by the manufacturer as the Multiple Container Loading Problem with Preference (MCLPP) and propose a combinatorial formulation for the MCLPP. We develop a two-phase algorithm to solve the problem. In phase one, we estimate the most promising region of the solution space based on performance statistics of the sub-problem solver. In phase two, we find a feasible solution in the promising region by solving a series of 3D orthogonal packing problems. A unique feature of our approach is that we try to estimate the average capability of the sub-routine algorithm for the single container loading problem in phase one and take it into account in the overall planning. To obtain a useful estimate, we randomly generate a large set of single container loading problem instances that are statistically similar to the manufacturer’s historical order data. We generate a large set of test instances based on the historical data provided by the manufacturer and conduct extensive computational experiments to demonstrate the effectiveness of our approach.

Agility and Proximity Considerations in Supply Chain Design

Strategic supply chain design decisions are critical to the long-term success of a business. Traditional facility location models for supply chain design focus on the trade-offs between the costs and benefits of proximity, i.e., the distance between facilities and customers. These strategic-focused models do not consider the supply chain’s agility, i.e., its ability to quickly respond to unexpected fluctuations in customer needs. In this paper, we study the problem of designing a supply chain distribution network under demand uncertainty and analyze how the optimal design characteristics of proximity and agility depend on various input parameters. We are able to draw managerial insights on how agility considerations may invalidate well-established and widely accepted qualitative results derived from traditional models. In particular, we show that it is optimal to increase the density of distribution centers (DCs) when the shortage penalty cost increases, and to decrease the density of DCs when a certain unit transportation cost parameter increases. Through these findings, our work conveys the message that traditional, proximity-based facility location models can be inadequate for designing modern responsive supply chains, and calls for the need to develop a new class of models for the task. This paper was accepted by Yossi Aviv, operations management.

Designing a multi-echelon reverse logistics operation and network: A case study of office paper in Beijing

To develop a circular and low-carbon economy, it is important to recycle and reuse office supplies, particularly office paper. We have analyzed the characteristics of office paper reverse logistics and propose an appropriate operation mode: a strategic alliance and third-party reverse logistics union led by a government or non-profit organizations and including office paper producers. We propose a nonlinear integer programming model for determining the locations and numbers of recycling stations and plants, such that the total cost is minimized. A case study of selected sites along the Xueyuan Road in Beijing is used to illustrate the proposed model. Additionally, our sensitivity analyses investigated how the amount of waste office paper, the throughput capacity of recycling stations, and the unit transportation cost affected the optimized results. Our findings provide useful insights for various stakeholders and suggest avenues for further research.

Profit Maximization Solid Transportation Problem with Trapezoidal Interval Type-2 Fuzzy Numbers

This paper proposes a new concept on transportation problem in which, we maximize the profit and minimize the transportation time while transporting an amount of quantity from a source to the destination. Here, we design two transportation models, in both the models; we maximize the profit and minimize the time of transportation. Here model-I having the unit purchase cost, unit selling price, unit transportation cost and transportation time as trapezoidal interval type-2 fuzzy number, while in model-II all the parameters are trapezoidal interval type-2 fuzzy number. To reduce these model-I and model-II into crisp equivalent, we use the expected value of a trapezoidal interval type-2 fuzzy number. Then the crisp equivalent problems are solved by employing the Interactive fuzzy satisficing method and LINGO 13.0 software to get the optimal solution. A numerical example is provided to demonstrate the models.

THE GENERALIZED ALCHIAN-ALLEN THEOREM: A SLUTSKY EQUATION FOR RELATIVE DEMAND

We generalize the Alchian-Allen theorem so as to account for income and endowment effects and provide two versions of a Generalized Alchian-Allen theorem: one for a unit cost component and one for a proportional cost component. Both versions provide a decomposition of an uncompensated change in the demand ratio of two goods into a substitution effect and an income-endowment effect-and may thus be regarded as extensions of the familiar Slutsky equation for relative demand. Finally, we apply our results to the choice of real estates and to parental time allocation decisions, the latter providing implications for child care policies. UEL Dll, H21, J22, R21) I. INTRODUCTION The Alchian-Allen theorem (see Alchian and Allen 1964, 74-75) suggests that an increase in the prices of two goods by the same amount leads to a decrease in the relative price of the more expensive good, and hence to a relative increase in the compensated demand for that good. As this pure substitution effect applies to any pair of goods with a common but abstract unit cost component, the Alchian-Allen theorem has a broad range of applications: for example, the common unit cost component may be interpreted as a specific tax, as a fixed transportation cost, as a flat transaction cost, or as the wage forgone in favor of different leisure time activities. Accordingly, the Alchian-Allen theorem has been discussed in various fields of both theoretical and empirical economics. (1) Above all, Borcherding and Silberberg (1978) formulated the Alchian-Allen theorem for compensated demand in the standard consumer model with three goods. Then, Hummels and Skiba (2004) extended the analysis of Borcherding and Silberberg by introducing an ad valorem tariff on the two goods under consideration and showed that the effect of a change in the ad valorem tariff is basically opposite to the effect of a change in the unit cost. In this paper, we generalize the two formulas for substitution effects-one for demand changes resulting from the unit cost components and one resulting from the proportional cost components (2)-within the Alchian-Allen framework so as to incorporate income and endowment effects. We believe that this generalization constitutes an important result, as these effects are arguably significant for a broad variety of goods. In this way, we arrive at two versions of the generalized Alchian-Allen theorem (GAAT), which essentially provides a decomposition of an uncompensated change in the demand ratio of two goods into a substitution effect (compensated effect) and an income-endowment effect. Because of the apparent similarity of our decomposition of the change in the demand ratio with the renowned Slutsky equation, the two versions of the GAAT we provide here may be regarded as the proper versions of the Slutsky equation for relative demand. The Alchian-Allen result (i.e., an increase in the demand for the more expensive good relative to the demand for the less expensive good resulting from an increase of a common unit cost component of both goods) has been examined empirically. For example, Bertonazzi, Maloney, and McCormick (1993) interpreted the Alchian-Allen theorem within a household production framework (3) and found that football fans traveling farther tend to purchase higher quality tickets. Hummels and Skiba (2004) presented the hypothesis that an increase in unit transport costs increases the share of the higher quality goods and therefore the share weighted average of prices, and confirmed this using international trade data. Moreover, some researchers have indirectly provided findings that are consistent with the Alchian-Allen result. Sobel and Garrett (1997) found that an increase in specific taxes is estimated to increase the market share of premium-brand cigarettes, and Nesbit (2007) found that an increase in specific taxes is estimated to increase the market share of premium-grade gasoline. …

Staged Improvement of Delivery-Oriented Production

This study proposes a system dynamics (SD) model to deal with the staged improvement of delivery-orientated production plan. The situations, such as the cycle time affected by total input of human resource, unit transportation cost influenced by amount of delivery quantity, unit penalty (shortage) cost affected by amount of shortages, and the time delay among customer demand, production demand, order quantity, material demand, and inventory are discussed. A dynamic approach to adjusting enterprise’s policy for conforming customers’ satisfactions is a practical problem, but past studies lack discussions. Therefore, this work aims to construct a dynamic approach for conforming the customers’ satisfactions. Research results reveal that the increment of production capacity and replenishment material policy could lead the shortage reduction, but do not achieve the complete improvement “none shortage”. Decreasing the affection of time delay would bring the production reaching the complete improvement. (Received in November 2013, accepted in August 2014. This paper was with the author 1 month for 1 revision.)

Interval transportation problem in urban region

The unit transportation cost is not fixed in urban region but there are available certain types of vehicles in prepaid system, depending on the distance from source to destination. In this paper we represent a transportation problem with interval source and interval demand but interval of unit transportation cost is determined depending on the prepaid costs of single trip of N-number of vehicles. The limits of the transportation costs are determined by our proposed algorithm. Then an uncertain multi-level programming model is developed for such a type of transportation problem. By another proposed algorithm convert it a multi-objective crisp model. Then solve this model by fuzzy programming technique. A numerical example is presented.

Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller

This paper proposes an adaptive genetic algorithm (FLC-aGA) approach based on fuzzy logic controller (FLC) for evaluating the reverse logistics (RL) networks with centralized centers. For the FLC-aGA approach, an adaptive scheme using a fuzzy logic controller is applied to GA loop. Five components which are composed of customers, collection centers, recovery centers, redistribution centers, and secondary markets are used to design the RL networks. For the RL with centralized centers (RLCC), collection center, recovery center, redistribution center and secondary market will be opened alone. The RLCC will be formulated as a mixed integer programming (MIP) model and its objective function is to minimize the total cost of unit transportation costs, fixed costs, and variable costs under considering various constraints. The MIP model for the RLCC is solved by using the FLC-aGA approach. Three test problems with various sizes of collection centers, recovery centers, redistribution centers, and secondary markets are considered and they are compared the FLC-aGA approach with other competing approaches. Finally, the optimal solutions by the FLC-aGA and other competing approaches are demonstrated each other using some measures of performance.

A multi-objective bi-level location planning problem for stone industrial parks

This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, conflicts between the local authority and the stone enterprises are considered. The local government, being the leader in the hierarchy, aims to minimize both total pollution emissions and total development and operating costs. The stone enterprises, as the followers in the hierarchy, only aim to minimize total costs. In addition, unit production cost and unit transportation cost are considered random variables. This complicated multi-objective bi-level optimization problem poses several challenges, including randomness, two-level decision making, conflicting objectives, and difficulty in searching for the optimal solutions. Various approaches are employed to tackle these challenges. In order to make the model trackable, expected value operator is used to deal with the random variables in the objective functions and a chance constraint-checking method is employed to deal with such variables in the constraints. The problem is solved using a bi-level interactive method based on a satisfactory solution and Adaptive Chaotic Particle Swarm Optimization (ACPSO). Finally, a case study is conducted to demonstrate the practicality and efficiency of the proposed model and solution algorithm. The performance of the proposed bi-level model and ACPSO algorithm was highlighted by comparing to a single-level model and basic PSO and GA algorithms.

Research of Container Transport Organization Model Based on the Theory of Hub and Spoke

In order to realize scale economy of shipping and promote development of container transportation system of the Yangtze River, this paper established the model of transport organization based on theory of hub and spoke, and applied the model to container transportation system of the Yangtze River.The results show that if hub k is Wuhan Port, so the model can play the advantages of scale economy, reduce unit transportation costs and achieve the purpose of reducing the total cost and the social cost of logistics network.

Port privatization in an international oligopoly

We investigate the effects of port privatization on port usage fees, firm profits, and welfare. Our model consists of an international duopoly with two ports and two markets. When the unit transport cost is high, port privatization reduces port usage fees, although neither government has an incentive to privatize its port. The equilibrium governmental decisions are inconsistent with the desirable outcome if the unit transport cost is not high enough. The government of the smaller country, in terms of market size, is more likely to privatize its port, and the government of the larger country is more likely to nationalize its port to protect its domestic market.

An interval valued solid transportation problem with budget constraint in different interval approaches

In this paper we investigate on solid transportation problem with interval environment. In this manuscript we develop STP under interval unit transportation cost, availability, requirement, conveyances capacity etc. Also, separately from source, demand and capacity constraints, an additional constraint on the total budget at each destination is imposed. We employ two different approaches (Hu and Wang's Approach and Mahato and Bhunia's Approach) based on interval ranking. After converting the interval transportation models into its crisp equivalent we apply the Weighted Tchebycheff method to solve. The models are illustrated with numerical examples and solved using the LINGO.13 optimization software.

Breakable Solid Transportation Problem with Hybrid and Fuzzy Safety Factors using LINGO and Genetic Algorithm

nvironments. If we carrying the produce from sources to destination by the means of unlike conveyances then due to insurgency, land slide and bad road, there are some risks or difficulties to transport the items. By this motive we initiate “Safety Factors” in transportation problem. Due to this reason desired total safety factor is being introduced. Also our objective is to evaluate the solution of STP using expected value model. Here we develop six models where first three models are formulated taking crisp unit transportation cost but the remaining three models are formulated taking hybrid unit transportation cost. To build up the different models we consider breakability and safety factor which is taken as crisp, fuzzy and hybrid for assorted models. All the fuzzy and hybrid models are reduced into its crisp equivalent using expected value modeling. Finally by Generalized Reduced Gradient (GRG) method using LINGO.13 optimization software and Genetic Algorithm we solve the mathematical models and put a enlarge discussion on it.

Allocating Freight Empty Cars in Railway Networks with Dynamic Demands

This paper investigates the freight empty cars allocation problem in railway networks with dynamic demands, in which the storage cost, unit transportation cost, and demand in each stage are taken into consideration. Under the constraints of capacity and demand, a stage-based optimization model for allocating freight empty cars in railway networks is formulated. The objective of this model is to minimize the total cost incurred by transferring and storing empty cars in different stages. Moreover, a genetic algorithm is designed to obtain the optimal empty cars distribution strategies in railway networks. Finally, numerical experiments are given to show the effectiveness of the proposed model and algorithm.

A Reverse Logistics Network Model for Handling Returned Products

Due to the emergence of e-commerce and the proliferation of liberal return policies, product returns have become daily routines for many companies. Considering the significant impact of product returns on the company's bottom line, a growing number of companies have attempted to streamline the reverse logistics process. Products are usually returned to initial collection points (ICPs) in small quantities and thus increase the unit shipping cost due to lack of freight discount opportunities. One way to address this issue is to aggregate the returned products into a larger shipment. However, such aggregation increases the holding time at the ICP, which in turn increases the inventory carrying costs. Considering this logistics dilemma, the main objectives of this research are to minimize the total cost by determining the optimal location and collection period of holding time of ICPs; determining the optimal location of a centralized return centre; transforming the nonlinear objective function of the proposed model formulation by Min et al. (2006a) into a linear form; and conducting a sensitivity analysis to the model solutions according to varying parameters such as shipping volume. Existing models and solution procedures are too complicated to solve real-world problems. Through a series of computational experiments, we discovered that the linearization model obtained the optimal solution at a fraction of the time used by the traditional nonlinear model and solution procedure, as well as the ability to handle up to 150 customers as compared to 30 in the conventional nonlinear model. As such, the proposed linear model is more suitable for actual industry applications than the existing models.

Unilateral Climate Policy and Foreign Direct Investment with Firm and Country Heterogeneity

We contribute to the debate on the impact of unilateral climate policy with a two-country two-firm international oligopoly model accounting for endogenous plant location and heterogeneity in both country size and firm’s emissions technology. Our results suggest that, if the carbon price differential is moderate as compared to unit transport costs and the relative size of the highly regulated country is big enough, a no relocation equilibrium may prevail also in the long run. A large market asymmetry coupled with a small technology gap emerges as the only configuration in which unilateral climate policy leads to a fall in world emissions irrespective of the optimal location choice. Thus for being effective and not leading to production relocation, unilateral climate policy should be moderate, implemented by a sufficiently large area and complemented by mechanisms for promoting the international transfer of clean technologies. Welfare implications are also discussed.

The last mile(s) in modern input distribution: Pricing, profitability, and adoption

AbstractIncreasing adoption of modern inputs remains one of the best hopes for greater agricultural production in developing countries. Based on unique data from northwestern Ethiopia, this study examines the “last mile(s)”—from the input distribution center to the farmer—in the chemical fertilizer and improved seed distribution system. We find that increasing transaction and transportation costs over a 35 km distance, along a route mainly accessible to only foot traffic, lead to a 50% increase of the price of chemical fertilizer and to a 75% reduction in its use. Farmers who live about 10 km from the distribution center face per unit transaction and transportation costs as high as the costs needed to bring the fertilizer from the international port to the input distribution center (about 1,000 km). Tackling the “last mile(s)” costs should thus be a priority to improve modern input adoption and use in these settings.

Entropy based solid transportation problems with discounted unit costs under fuzzy random environment

This paper investigates some multi-objective fixed charge solid transportation problems (FCSTPs) under fuzzy-random environment with fuzzy-random fixed charges and transportation times. Here objectives are total transportation cost and time which are minimized. Unit transportation costs are crisp and three types of discount i.e. All Unit Discount (AUD), Incremental Quantity Discount (IQD), IQD within AUD are applied upon these costs. In the first model (Model-I) supplies, demands and capacities of conveyances are assumed to be fuzzy-random in nature. These quantities are fuzzy in the second model (Model-II). Imprecise objectives of the models are transformed into equivalent crisp ones using random expectation and fuzzy possibility/necessity measures on fuzzy event. Imprecise constraints are reduced to equivalent crisp constraints using two different fuzzy-random chance constraint methods. Similarly constraints of Model-II are reduced to equivalent crisp constraints with the use of possibility and necessity measures on fuzzy events. Models are also formulated with and without entropy function. Finally transformed constrained multi-objective deterministic problems are solved using a multi-objective genetic algorithm (MOGA) based on arithmetic crossover and boundary mutation. Moreover, Model-IA1 is converted to a single objective non-linear programming (SONLP) problem following fuzzy non-linear programming (FNLP) technique and the reduced Model-IA1S is solved using generalized reduced gradient (GRG) method (using LINGO software). Numerical examples are used for illustration and comparison of the models.