ABSTRACT
The shortest path problem is the problem of finding one-to-one path from the source to the destination. In real life, the problem involves conflicting and competing objectives, and many researchers solve the multiobjective shortest path optimization problem using a weighted linear combination of objectives. However, the solution of weighted sum is highly sensitive to the weights given, limiting the practicability of this method. Solving the problem by simultaneous optimization of the objective functions to generate a set of nondominated solutions can help to build a decision support system for travelers to choose their own path on the basis of their individual preferences. This chapter proposes a multiobjective mathematical model, solved using ε-approach to obtain (heuristically) nondominated solutions of shortest paths with respect to time and distance. In this study, we consider minimizing both distance and time as the objective functions with real-life constraints such as time-dependent dynamic and deterministic travel times, time-dependent dynamic and deterministic waiting times, and time-dependent one-way traffic along the roads. We implement the proposed model by using the real-life planning data (1658 nodes and 4224 links) of Chennai city network (a metropolitan city in India with highly interconnected network of roads), and present the discussion on the multiobjective routing problem with real-life considerations.
