Hansuk Sohn

A rough set based approach to distributor selection in supply chain management

Distributors selection is an important issue in Supply chain management, particularly in the current competitive environment. The current research works provide only conceptual, descriptive, and simulation results, focusing mainly on firm resources and general marketing factors. The selection and evaluation of distributors generally incorporate qualitative information; however, analyzing qualitative information is difficult by standard statistical techniques. Consequently, a more suitable approach is desired. In this paper, a method based on Rough set theory, which has been recognized as a powerful tool in dealing with qualitative data in the literature, is introduced and modified for preferred distributor selection. We derived certain decision rules which are able to facilitate distributor selection and identified several significant features based on an empirical study conducted in China.

A Dynamic Programming Algorithm for Scheduling In-Vehicle Messages

In-vehicle information systems (IVISs) can enhance or compromise driving safety. Such systems present an array of messages that range from collision warnings and navigation instructions to tire pressure and e-mail alerts. If these messages are not properly managed, the IVIS might fail to provide the driver with critical information, which could undermine safety. In addition, if the IVIS simultaneously presents multiple messages, the driver may fail to attend to the most critical information. To date, only simple algorithms that use priority-based filters have been developed to address this problem. This paper presents a dynamic programming model that goes beyond the immediate relevance and urgency parameters of the current Society of Automotive Engineers (SAE) message scheduling algorithm. The resulting algorithm considers the variation of message value over time, which extends the planning horizon and creates a more valuable stream of messages than that based only on the instantaneous message priority. This method has the potential to improve road safety because the most relevant information is displayed to drivers across time and not just the highest priority at any given instant. Applying this algorithm to message sets shows that scheduling that considers the time-based message value, in addition to priority, results in substantially different and potentially better message sequences compared with those based only on message priority. This method can be extended to manage driver workload by adjusting message timing relative to demanding driving maneuvers.

Optimal sequences of trials for balancing practice and repetition effects

This paper describes procedures for generating trial sequences to balance out practice effects and intertrial repetition effects in experiments consisting of repeated trials. In the sequences presented, each stimulus appears an equal number of times, is preceded equally often by itself and by each other stimulus, and is distributed in a “balanced” manner throughout the block of trials. Two criteria for balance are employed. One criterion aims to equalize the average position of each stimulus in the sequence. The second criterion maintains, as much as possible, a uniform interval between appearances of each stimulus in the sequence. For each criterion, optimal or near-optimal sequences are presented for experiments involving from three to nine different stimulus conditions. Suggestions are included for extending (e.g., doubling or tripling) the length of the sequences.

Using a Genetic Algorithm to Solve the Benders’ Master Problem for Capacitated Plant Location

The capacitated plant location problem (CPL) consists of locating a set of potential plants with capacities, and assigning a set of customers to these plants. The objective is to minimize the total fixed and shipping costs while at the same time demand of all the customers can be satisfied without violating the capacity restrictions of the plants. The CPL is a well-known combinatorial optimization problem and a number of decision problems can be obtained as special cases of CPL. There are substantial numbers of heuristic solution algorithms proposed in the literature (See Rolland et al., 1996; Holmberg & Ling, 1997; Delmaire et al., 1999; Kratica et al., 2001; He et al., 2003; Uno et al., 2005). As well, exact solution methods have been studied by many authors. These include branch-and-bound procedures, typically with linear programming relaxation (Van Roy & Erlenkotter, 1982; Geoffrion & Graves, 1974) or Lagrangiran relaxation (Cortinhal & Captivo, 2003). Van Roy (1986) used the Cross decomposition which is a hybrid of primal and dual decomposition algorithm, and Geoffrion & Graves (1974) considered Benders’ decomposition to solve CPL problem. Unlike many other mixed-integer linear programming applications, however, Benders decomposition algorithm was not successful in this problem domain because of the difficulty of solving the master system. In mixed-integer linear programming problems, where Benders’ algorithm is most often applied, the master problem selects values for the integer variables (the more difficult decisions) and the subproblem is a linear programming problem which selects values for the continuous variables (the easier decisions). If the constraints are explicit only in the subproblem, then the master problem is free of explicit constraints, making it more amenable to solution by genetic algorithm (GA). The fitness function of the GA is, in this case, evaluated quickly and simply by evaluating a set of linear functions. In this chapter, therefore, we discuss about a hybrid algorithm (Lai et al., 2010) and its implementation to overcome the difficulty of Benders’ decomposition. The hybrid algorithm is based on the solution framework of Benders’ decomposition algorithm, together with the use of GA to effectively reduce the computational difficulty. The rest of

A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem

Abstract This paper presents a new variant of Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP). ACO has been successfully used in many combinatorial optimization problems. However, ACO has a problem in reaching the global optimal solutions for TSPs, and the algorithmic performance of ACO tends to deteriorate significantly as the problem size increases. In the proposed modification, adaptive tour construction and pheromone updating strategies are embedded into the conventional Ant System (AS), to achieve better balance between intensification and diversification in the search process. The performance of the proposed algorithm is tested on randomly generated data and well-known existing data. The computational results indicate the proposed modification is effective and efficient for the TSP and competitive with Ant Colony System (ACS), Max-Min Ant System (MMAS), and Artificial Bee Colony (ABC) Meta-Heuristic.

Using DEA in conjunction with designs of experiments: an approach to assess simulated futures in the Afghan educational system

This research presents an assessment of the simulated futures of the Afghan educational system. We use a hybrid analytical process combining simulation, design of experiments, and data envelopment analysis (DEA) that is capable of studying the dynamic behaviour of the Afghan educational system. By using the process, we are able to determine the critical relationships, potential futures, and unforeseen consequences of potential policies regarding primary and secondary education in Afghanistan. This research also provides insight into the use of DEA when applied to a large-scale discrete event social simulation.

A simulation model with verification and validation for time-phased education planning in Afghanistan

Decision-makers have rightly come to expect thorough, relevant analysis prior to allocating resources. The field of education planning is no different in its requirement for such analysis. In this research, we present a discrete event simulation model in which individual students and teachers flow through the Afghanistan education system across a variable time horizon. We explain the simulation and its premises through some verification and validation processes that allow the user to glean insight into the implications of security, policy, and infrastructure in this complex and capricious environment.