An advanced optimization system for Vehicle Routing and Scheduling Problems (VRSP), which is one of the Supply Chain Planning modules, is introduced. An object oriented system, Computer Aided Routing and Scheduling (CARS) can handle complicated distribution models using advanced heuristic optimization algorithms. To classify various types of routing and scheduling problems in a structured manner, a classification scheme is introduced based on the main objects of VRSP. Also, the modeling and solution approach in the CARS optimization engine has been elaborated. Main static and dynamic objects of the system as well as their relationships and interactions have been explained. The user interface in addition to the planning and operational features of the system is described in detail.

This paper is an endeavor to quantify the concept of sustainable transportation. The prevailing idea in the context of sustainable development (SD) emphasizes on the reduction of transportation demand in order to reduce the environmental and social consequences of it. Nevertheless, in the current paper using a measure for SD, and based on the conformity of the growths of all sectors with transportation supply and demand, the countries are comparatively studied. The measure, elasticity, for each two variables indicates the extent to which those two variables have been changing consistently. Indeed the elasticity values are measures of a “harmonic development” representing sustainability. The database consisted of national variables in transportation, economic, social and environmental categories for 128 countries in the period of 1980 to 1995. The study shed some light on the SD of transportation supply and demand, reflecting the harmony of social, environmental and economic development with respect to transportation supply and demand development. Using individual elasticity, composite sustainability indices were suggested. For comparative appraisal, country groupings were developed. The sustainability appraisal showed interesting patterns of within and between group similarities and differences. The study confirmed the significance of transportation supply and demand balancing and sustainability challenges of the 21st century. The methodology may be applied to any other time and geographic scope for addressing pertinent issues for balancing and SD of transportation systems.

This paper is focused on the tactical design of steel supply chain (SSC). A general mathematical model is proposed to integrate production and transportation planning in multi-commodity SSC. The main purpose is to prepare a countrywide production and distribution plan in an SSC with three layers consisting of iron ore mines as suppliers, steel companies as producers, and subsidiary steel companies as customers. An internal supply chain consisting of melting furnaces and casting lines in each producer is also taken into account. Demand is assumed to be deterministic and known at the beginning of planning horizon. Each mine supplies iron ore with specific chemical compounds (CCs) and each producer needs a given volume of iron ore with a pre-determined range of CCs which can be provided by mixing several kinds of iron ores. Finally, a real test case in SSC is described and solved using the proposed model. Sensitivity analysis supports the decision makers with noteworthy information about tactical decisions.

Reverse supply chain is a process dealing with the backward flows of used/damaged products or materials. Reverse supply chain includes activities such as collection, inspection, reprocess, disposal and redistribution. A well-organized reverse supply chain can provide important advantages such as economic and environmental ones. In this study, we propose a general framework and formulate a mathematical model for product return in reverse supply chain. We consider a multilayer, multi-product for the model. The main objective of the paper is minimizing the total costs of reverse supply chain including transportation, fixed opening, operation, maintenance and remanufacturing costs of centers. To validate the mathematical model, a numerical example is solved using Lingo 9 software and the results are reported.