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Volume 23, Issue 2 (6-2012)
Abstract
The ever severe dynamic competitive environment has led to increasing complexity of strategic decision making in giant organizations. Strategy formulation is one of basic processes in achieving long range goals. Since, in ordinary methods considering all factors and their significance in accomplishing individual goals are almost impossible. Here, a new approach based on clustering method is proposed to assist the decision makers in formulating strategies. Having extracted the internal and external factors, after setting long range goals, the factor-goal matrices are generated according to the impact rate of factors on goals. According to created matrices, clusters including goals and factors are formed. By considering individual clusters the strategies are proposed according to the current state of clusters for the organization. By applying this new method the opportunity of considering the impact of all factors and its interactions on goals are not lost. Strategy-factor and strategy-goal matrices are utilized to validate the proposed method. To show the appropriateness and practicality of our approach, particularly in an environment with a large number of interacting goals and factors, we have implemented the approach in Mahmodabad Training Center (MTC) in Iran. The resulting goal-factor, current and dated states of clusters, also, strategy-goal and strategy-factor matrices for model validation and route branch indices for finding out how the organization achieved each goal are reported.
Ali Nadizadeh,
Volume 28, Issue 3 (9-2017)
Abstract
In this paper, the fuzzy multi-depot vehicle routing problem with simultaneous pickup and delivery (FMDVRP-SPD) is investigated. The FMDVRP-SPD is the problem of allocating customers to several depots, so that the optimal set of routes is determined simultaneously to serve the pickup and the delivery demands of each customer within scattered depots. In the problem, both pickup and delivery demands of customers are fuzzy variables. The objective of FMDVRP-SPD is to minimize the total cost of a distribution system including vehicle traveling cost and vehicle fixed cost. To model the problem, a fuzzy chance-constrained programming model is proposed based on the fuzzy credibility theory. A heuristic algorithm combining K-means clustering algorithm and ant colony optimization is developed for solving the problem. To achieve an appropriate threshold value of parameters of the model, named “vehicle indexes”, and to analyze their influences on the final solution, numerical experiments are carried out.
Md. Rafsan Islam, Md. Azizur Rahman, Kazi Mohammad Nazib, Lasker Ershad Ali,
Volume 36, Issue 3 (9-2025)
Abstract
The Capacitated Vehicle Routing Problem (CVRP) is a significant variant of the vehicle routing problem that incorporates constraints related to customer demand and vehicle capacity. Owing to its extensive applications in logistics and transportation, CVRP has attracted substantial research attention, with numerous algorithms proposed from the perspective of intelligent search. A common solution strategy involves two phases: first, assigning customers to different vehicles to form feasible routes, and second, optimizing these routes. This paper presents a two-phase CVRP solution framework through the clustering concept with intelligent search to improve route planning. In the first phase, a set of clustering methods - fuzzy c-means, k-means, and k-medoids - combined with a nearest neighbor heuristic search, are applied to generate feasible routes for each vehicle. In the second phase, these routes are iteratively optimized using the Simulated Annealing (SA) algorithm. The process yields three distinct solution pathways: fuzzy c-means with SA, k-means with SA, and k-medoids with SA. For performance evaluation, 46 benchmark CVRP datasets from a publicly available library are used. Simulation results demonstrate that k-means with SA performs the best, surpassing the other two approaches and outperforming other clustering-based two-phase state-of-the-art algorithms in terms of solution quality.
