Meta-heuristics for manufacturing scheduling and logistics problems

Nowadays, customer demand for order variety, short lead time, and fast delivery have created a great impact on manufacturing problems such as production scheduling, job sequencing, vehicle routing, bin-packing, and loading. The related problems of manufacturing scheduling and logistics support are even more crucial than ever. Traditional approaches are incapable of dealing with the challenges of such intractable problems. There is a need for the development of new solution methods to address these challenging issues. Meta-heuristics based on ant colony optimization (ACO), evolutionary algorithms (EA), particle swarms optimization (PSO), etc. provide a new problem-solving paradigm for academic researchers and industrial practitioners. Manufacturing scheduling and logistics problems are practical decision-making problems in organizations and factories, and they are notoriously difficult in the domains of production economics, operations research, optimization, and computer science. Due to their theoretical challenges and practical applications, scheduling and logistics problems have attracted a considerable amount of research attention in the literature. However, traditional approaches can only solve small-sized problems optimally as the computational efforts to solve large-sized problems are beyond the capability of contemporary computational resources. This limitation has prompted researchers and practitioners to develop problem-specific heuristics or use metaheuristics to solve such problems quickly with reasonable solution quality. Recently many researchers have proposed different kinds of meta-heuristics in solving the manufacturing scheduling and logistics problems. Chang et al. (2011) proposed a genetic algorithm (GA) enhanced by dominance properties for single machine scheduling problems to minimize the sum of the job’s setups and the cost of tardy or early jobs related to the common due date. Chang et al. (2010) also proposed a novel genetic algorithm which is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. The evaporation concept is also employed to reduce the effect of past experience and to explore new alternative solutions. Chen et al. (2009) developed a guided memetic algorithm (MA) with probabilistic models which serve as a fitness surrogate in estimating the fitness of the new solution generated by a local search. In terms of machine scheduling, Chang et al. (2009b) developed a hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties to minimize the weighted sum of earliness and tardiness costs. Chang et al. (2008b) also developed a mining gene structures to inject artificial chromosomes for genetic algorithm to solve the single machine scheduling problems. Prakash et al. (2012) proposed a constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem Liao and Liao (2008a) proposed an ant colony optimization combined with taboo search for the job shop scheduling problem. Liao and Liao (2008b) developed an