In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer’s capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer’s skill-level. This study intends to social contribution through attempts to optimize enterprise’s goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource’s capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz’s MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.

As supply chains are globalized, multinational companies are trying to optimize distribution networks using a hub and spoke structure. In this hub and spoke network structure, multinational companies locate regional distribution centers at hub airports, which serve demands in their corresponding regions. Especially when customers put higher priority on the service lead-time, hinterlands of international hub airports become ideal candidate locations for the regional hub distribution centers. By utilizing excellent airport and logistics services from hub airports, regional distribution centers in the hub airports can match supply with demand efficiently. In addition, regional hub distribution centers may increase air cargo volume of each airport, which is helpful in the current extremely competitive airport industry. In this paper, we classified locational preferences into three primary categories including demand, service and risk and applied the analytic hierarchy process methodology to prioritize factors of locational preferences. Primary preference factors include secondary factors. Demand factor contains access to current and prospect markets. Service factor comprises airport and logistics perspectives. Service factor in terms of airport operations includes secondary factors such as airport service and connectivity. Service factor in terms of logistics operations contains infrastructure and logistics operations efficiency. Risk factor consists of country and business risks. We also evaluated competitiveness of Asian hub airports in terms of candidate location for regional hub distribution centers. The candidate hub airports include Singapore, Hong Kong, Shanghai, Narita and Incheon. Based on the analytic hierarchy process analysis, we derived strategic implications for hub airports to attract multinational companies’ regional hub distribution centers.

District heating is a system of distributing heated water from centralized facilities to local homes and buildings. In this paper, we model the distribution planning problem as a supply chain planning problem and propose an explicit column generation algorithm to handle large scale data as well as nonlinear constraints. The algorithm has been successfully applied to a Korean district heating company and the computational experiments show that the integrated operation of district heating network improves the total profit compared to the previous isolated network operation.

Lot-order assignment is the process of assigning items in lots being processed in a production facility to orders to meet due-dates of the orders. In this study, we consider the lot-order assignment problem (LOAP) with the objective of minimizing total tardiness of the orders with distinct due dates. We address similarity relationships between the LOAP and the single machine total tardiness scheduling problem (SMTTSP) and suggest priority rules for the LOAP based on those for the SMTTSP. Performances of the priority rules are compared with each other and with that of the commercial optimization software package in computational experiments.