This paper presents a Pareto-based bi-objective optimization of hazardous materials vehicle routing and scheduling problem with time windows and shows its application to a realistic hazardous material logistics instance. A meta-heuristic solution algorithm is also proposed, which returns a set of routing solutions that approximate the frontier of the Pareto optimal solutions based on total sche...

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distrib...

efficient and on-time maintenance plays a crucial role inreducing cost and increasing the market share of an industrial unit. preventivemaintenance is a broad term that encompasses a set of activitiesaimed at improving the overall reliability and availability of a systembefore machinery breakdown. the previous studies have addressed thescheduling of preventive maintenance. these studies have co...

In this chapter, we present a new method for interactive multiobjective optimization, which is based on application of a logical preference model built using the Dominance-based Rough Set Approach (DRSA). The method is composed of two main stages that alternate in an interactive procedure. In the first stage, a sample of solutions from the Pareto optimal set (or from its approximation) is gener...

This article reports an experimental analysis on stochastic local search for approximating the Pareto set of bi-objective unconstrained binary quadratic programming problems. First, we investigate two scalarizing strategies that iteratively identify a high-quality solution for a sequence of sub-problems. Each sub-problem is based on a static or adaptive definition of weighted-sum aggregation co...

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algori...

This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new Strength Pareto Evolutionary Algorithm (SPEA) based approach is proposed to handle the problem as a true multiobjective problem with competing and non-commensurable objectives....

In this paper, we study an integrated logistic system where the optimal location of depots and vehicles routing are considered simultaneously. This paper presents a new mathematical model for a multi-objective capacitated location-routing problem with a new set of objectives consisting of the summation of economic costs, summation of social risks and demand satisfaction score. A new multi-objec...

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation s...

Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. The crucial task of choosing a single preferred solution from the obta...