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The purpose of this paper is to attempt to expand the traditional economic effect analysis of export subsidy, which has previously ignored the incentive of export subsidies in terms of competition from re‐importation.

The paper performs a comparative static analysis based on the traditional welfare analysis of export subsidies by introducing different transportation costs and using small country model and large country model, respectively.

Compared with the traditional analysis, exporting countries that implement export subsidies suffer less welfare loss and induce intra‐industry trade of homogeneous products. Due to export subsidy policy incentives, transportation costs heavily influence trade patterns, trade volumes and welfare. Trade patterns evolve from unidirectional export to intra‐industry trade as transportation costs are reduced, with the main source of welfare loss coming from transportation costs. The distribution of export subsidies is biased when domestic transportation costs are high. Under low domestic transportation costs, inefficient intra‐industry trade would emerge as a result of export subsidy incentive.

The findings could be helpful to understand the impact of export subsidy policy on trade pattern, trade volumes and welfare when considering international and domestic transportation cost.

The paper emphasizes the incentive of export subsidy on re‐importation, and links it with transportation costs, which expand the traditional export subsidy analysis.

Consolidation, the grouping of several small shipments into one at a designated location, can reduce total logistics cost. Total logistics cost includes consolidation, transportation and inventory costs. Identifying where cost‐saving opportunities exist is often confused by the interrelated nature of these various costs.

It has been suggested that much of the potential inefficiencies associated with supply chain management (SCM) costs can be traced to wasteful practices such as inefficient, unnecessary, or redundant stocking practices, or inefficient transportation. The purpose of this paper is to develop a model which reconciles many of these inefficiencies by integrating production factors, purchasing, inventory, and trucking decisions for optimizing supply chain costs between first‐, and second‐tier suppliers and subsequent OEM customers.

The modeling technique is mathematical programming tested in a simulation model. In an effort to determine the significance of the transportation component of the proffered model, the fully developed model is differentially tested, including standard production variables varying transportation costs, paired with similar instances of the model in which the transportation costs are fixed.

Significant differences are found in the predictive abilities of the respective models, and this supplies pragmatic evidence of the important role that transportation issues play in the consideration of integrated SCM costs.

The key limitation to this finding lies in the validation process. As suggested by Sargent, Monte‐Carlo studies are useful for validation purposes, and the supply chain optimization model (MHSCM) is certainly confirmed through this particular simulation.

The managerial focus on transportation management and cost control in SCM can be highlighted as a critical implication of the study.

The structure of the MHSCM is robust, and may be useful for cost‐control planning purposes in a dynamic environment, subject to certain limitations accruing to the methodology.

Most OECD countries will have a considerable challenge ahead with an ageing population and necessary health care produced for retired people. Healthcare costs have increased continuously from the mid-1990’s in Finland, and growth is likely to continue in the future, as the amount of older inhabitants is increasing. Furthermore, transportation of patients and their visitors between homes and hospitals is a large component of the total health carehealth care related costs. This paper aims to estimate transport-related costs and develop ways to decrease these costs.

A system dynamics simulation model was developed to examine different scenarios for patients and their visitor transportation to hospitals until the year 2040. Model is driven by age distribution of the region and likely by development of the total population. All parameter values were defined based on real-life observations.

Patients’ need to travel to hospitals is likely to continue to grow. In addition, quality of travel will change as older retired people are not willing or able to use their own transportation equipment or public transportation modes – this is the main reason for higher transportation costs of patients. Transportation is typically conducted via taxis, private cars and ambulances. Therefore, it is critical that people from the region are able to access hospital services with short proximity.

Simulation study is limited to one hospital investment decision in Finland. Distances and population densities as well as transportation mode alternatives differ from more populous regions in the world.

Research findings stressed the importance of keeping their own hospital operations within the region and placing them in a better location. In an alternative case, where a hospital decision would have been abandoned, total transportation costs during 2012-2040 would have increased by at least the same amount that a new hospital is assumed to cost.

This research is one of the first from the health care sector, where patient transportation modes and ageing is being dealt with in the context of new investments. Patient transportation is often an overlooked issue, which bears significant costs, especially as people age.

The purpose of this paper is to help shippers determine a negotiation yardstick for transportation price and formulate wise transportation outsourcing strategies by examining the presence of freight rate differentials for shippers and identifying their main causes. This paper also develops a framework for benchmarking freight rates based on the actual data.

This paper proposes an additive dummy regression model to determine a statistical significance in shipping charges between different shippers. Unlike the traditional least square analysis, the proposed model is designed to avoid a biased assessment of the impact of an explanatory variable.

Through a series of empirical data analysis and hypothesis tests, the authors discovered that the fixed portion (minimum base charge) of shipping charges differed depending on the shipper’ individual contract, while the variable portion (fuel or accessorial charge) of shipping charges remained the same regardless of the shipper’s individual contract. As such, shippers who are unaware of flexible but unpredictable transportation pricing practices and unprepared for freight rate negotiation can suffer from higher shipping costs as compared to their peers. Thus, the authors conclude that the success of transportation outsourcing, carrier selection, and freight rate negotiation strategies tends to rest on the shipper’s ability to understand transportation cost structures and then determine the benchmark freight rate considered “fair” and “reasonable” for a given service.

This paper is one of the first to examine shipping cost differentials between different shippers and determine what causes such differentials. In doing so, this paper attempted to assess the potential impact of freight rate negotiation and carrier selection strategies on shippers’ transportation costs in current deregulatory environments where shippers were given a greater freedom to negotiate freight rates with carriers and an increased opportunity to save their transportation costs.

Construction is possibly one of the most cost orientated industries in any economy. The primary mode of supplier selection has always tended to be on the basis of lowest material or service cost at point of consumption. Indeed, this remains the case even in the post‐Latham (1994) and Egan (1998) world in which we live. In general, construction cost estimates are based on a straight ‘take off’ of the quantities required. All further ‘other’ costs in the form of overhead, profit, labour and wastage are consolidated into the cost of the materials. Construction is unique within the various industries making up a modern economy in that the bulk of the materials and components that it uses are of relatively low value while being of high volume. Consequently, a significant proportion of the ‘other’ costs associated with materials purchases must be in the form of transportation from the point of extraction and / or production to the point of consumption. This paper provides an overview of the hidden costs associated with the transportation of construction materials within the industry and proposes improved methods of managing the logistics of the construction process e.g. reverse logistics, in order to reduce costs and increase the basic sustainability of the construction process.

Lignite coal-fired power plants are the main electricity generators in the province of Saskatchewan, Canada. Although burning lignite coal to generate power is economical, it produces significant greenhouse gases making it a big challenge to Canada’s international commitment on emission reduction. However, abundant agricultural crops and sawdust produced in Saskatchewan put the province in a good position to produce and use agri-pellets as an alternative fuel to generate electricity. This study aims to conduct an economic and environmental analysis of the replacement of lignite coal by agri-pellets as the fuel for Saskatchewan’s coal-fired power plants.

The study estimates the economic and environmental costs and benefits of two alternative fuels for power plants. The economic analysis is based on the pellet production and transportation costs from farms to production sites and from the production sites to power plants. In the production process, biomass precursors are densified with and without additives to produce fuel agri-pellets with appropriate mechanical durability and high heating value per volume unit. The environmental analysis involves estimation of greenhouse gas emissions and their social costs for lignite coal and different types of agri-pellets under different scenarios for pellet production and transportation.

The results show that although the total cost of electricity is lower for coal than agri-pellets, the gap shrinks when social costs and specifically a carbon price of $50/tonne are included in the model. The cost of electricity in lignite coal-fired power plants would also be on par with agri-pellets-fired power plants if the carbon price is between U$68 and $78 per tonne depending on the power plant locations. Therefore, a transition from coal to agri-pellet fuels is feasible if a high-enough price is assigned to carbon. The method and the results can be generalized to other places with similar conditions.

There are a few caveats in this study as follows. First, the fixed costs associated with the transformation of the existing coal-fired power plants to pellet-fired plants are not considered. Second, the technological progress in the transportation sector, which would favor the net benefits of using pellets versus coal, is not included in the analysis. Finally, the study does not address the possible political challenges facing the transition in the context of the Canadian federal system.

The study results indicate that the current carbon price of $50 per tonne is not sufficient to make the agri-pellets a feasible source of alternative energy in Saskatchewan. However, if carbon pricing continues to rise by $15 annually starting in 2022, as announced, a transition from coal to agri-pellets will be economically feasible.

Canada is committed to reduce its emission according to the Paris agreement, and therefore, needs to have a concrete policy to find alternative energy sources for its coal-fired power plants. This study examines the challenges and benefits of such transition using the existing agri-pellet resources in Saskatchewan, a province with abundant agricultural residues and coal-fired power plants. The findings indicate that a significant emission reduction can be achieved by using agri-pellets instead of coal to produce electricity. The study also implies that the transition to renewable energy is economical when social costs of carbon (carbon tax) is included in the analysis.

As far as the authors know, this is the first study providing a socio-economic analysis for a possible transition from the coal-fired power plants to a more clean and sustainable renewable energy source in one of the highest carbon dioxide (CO₂) producer provinces in Canada: Saskatchewan. The study builds upon the technical production of three agri-pellets (oat hull, canola hull and sawdust) and estimates the economic and environmental costs of alternative fuels under different scenarios.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.