This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX^® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.

Modern rail-road container terminals serve as important consolidation nodes in intermodal transportation networks where containers are transferred between freight trains and trucks. Among the most important decision problems in these yards is the crane scheduling problem, which decides on the sequences of container moves executed by each of the parallel gantry cranes. This paper treats a rich crane scheduling problem, which considers plenty relevant constraints and is directly applicable in Chinese intermodal terminals. For solving this problem, we propose a fix-and-optimise procedure, which is easy to implement and shown to successfully solve even large-sized instances with 100 container moves in reasonable time.

Two-sided assembly lines balancing problem has been extensively studied in recent years. However, only limited attention has been paid to balancing mixed-model two-sided assembly lines problem (MTALBP). Moreover, the majority of balancing research assumes the setup as negligible, although it is ubiquitous in the assembly process. As the non-increment activities, setups occur in two ways: forward and backward setups. According to our best knowledge, no published work in literature on MTALBP has simultaneously considered forward and backward setups. In this paper, the problem of balancing mixed-model two-sided assembly lines with setups (MTALBPS) is considered. The purpose of this paper is twofold. The primary objective is to develop a mixed-integer programming (MIP) mathematical model to formulate the type-I problem of MTALBPS. The secondary objective is to propose an effective variable neighbourhood search (VNS) algorithm to solve it, especially for the large-sized problems. In addition, to test the effectiveness of the proposed approaches, a number of test problems from the literature with up to 148 tasks are solved and compared with the lower bound. The results demonstrate that the proposed algorithm is effective and produces very close results to the lower bound in a reasonable time.

The standard artificial bee colony (ABC) algorithm as a relatively new swarm optimization method is often trapped in local optima in global optimization. In this paper, a novel search strategy of main three procedures of the ABC algorithm is presented. The solutions of the whole swarm are exploited based on the neighbor information by employed bees and onlookers in the ABC algorithm. According to incorporating all employed bees' historical best position information of food source into the solution search equation, the improved algorithm that is called global artificial bee colony search algorithm has great advantages of convergence property and solution quality. Some experiments are made on a set of benchmark problems, and the results demonstrate that the proposed algorithm is more effective than other population based optimization algorithms.