Evolutionary Simulated Annealing Algorithm Research Articles

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.

A transportation network model with intelligent probabilistic travel times and two hybrid algorithms

A main component of a transportation network is travel time or distance. Due to stochastic events such as accidents and failures in roads, a deterministic estimation of the travel time between two cities or regions is impossible. Also, on special occasions like holidays, with increase in the traveling population in the transportation network, probabilities of occurrence of these events increase. The increase in traveling population has a direct effect on the estimation of travel times and subsequently on the decision-making process. Therefore, provision of an appropriate model for the intelligent probabilistic travel times to distribute the population in a transportation network is a practical necessity. Here, we study a capacitated location-multi allocation-routing problem with intelligent probabilistic travel times. In our study, the concept of intelligent probabilistic travel times incurs two issues: (1) consideration of some random factors in computing the travel times and (2) impact of the traveling population on these random factors simultaneously. Here, we consider three random factors of the time spent in traffic, the number of accidents and the number of road failures. It is assumed that server nodes and arcs have limited capacities for accepting the population. After proposing a function for computing the intelligent probabilistic travel time, we first formulate the problem as a mixed-integer nonlinear programming model, and then suitably transform it into a mixed-integer linear programming one. Our aim is to determine appropriate server locations among the candidate locations, allocate the existing population in each demand node to server locations, and find the movement path of each member to reach its corresponding server with respect to the simultaneous change of the probabilistic travel times so that the expected total transportation time is minimized. For small problems, the model is efficiently solvable by the CPLEX software package. For large problems, two solution approaches, a heuristic algorithm incorporating genetic algorithm and local search, and an evolutionary simulated annealing algorithm, are proposed. Comparative numerical results demonstrate the effectiveness of the proposed probabilistic model and the proposed algorithms.

A stochastic user equilibrium assignment problem in discrete network design problem

We formulate the transportation discrete network design problem (DNDP) as a mixed-integer bi-level mathematical problem, based on the concept reserve capacity. The upper level goal programme maximises the reserve capacity by designing the direction of street and increasing the street capacity through lane addition. The lower level problem is stochastic user equilibrium traffic assignment problem within a probit-based path choice decision framework which generates user equilibrium flow patterns. Because of non-convexity nature of the model, meta-heuristic methods used to solve the problem and we used Monte Carlo simulation approach to compute path choice probabilities and stochastic user equilibrium solved by method of successive averages. A hybrid genetic algorithm with simulated annealing and an evolutionary simulated annealing algorithm are proposed. Numerical examples are presented to verify the proposed model and algorithm.

Optimizing reserve capacity of urban road networks in a discrete Network Design Problem

This paper addresses the problem of designing of street directions and lane additions in urban road networks, based on the concept of reserve capacity. Reserve capacity is identified by the largest multiplier applied to a given existing demand matrix, that can be allocated to a network without violating the arc capacities. Having a two-way streets base network and the allowable street lane additions, the problem is to find the optimum configuration of street directions and two-way street lane allocations, and the optimum selection of street lane addition projects, in a way that the reserve capacity of the network is maximized. The problem is considered in two variations; in the first variation no restriction is imposed on the symmetricity of lane allocations for two-way streets, and in the second variation, two-way street lane allocations are restricted to be symmetric. The proposed problems are modeled as mixed-integer bi-level mathematical problems. A hybrid genetic algorithm and an evolutionary simulated annealing algorithm are proposed to solve the models. Computational results for both problem variations are presented.

Sequential process planning: A hybrid optimal macro-level approach

Increased global competition and unpredictable market changes are challenges facing manufacturing enterprises. Changes of part design and engineering specifications trigger frequent and costly changes in process plans, setups, and machinery. The paradigm shift in manufacturing systems and their increased changeability also require corresponding responsiveness in support functions; process planning is a key logical enabler that should be further developed to cope with changes encountered at the system level and to support new manufacturing paradigms and continuously evolving products. Retrieval-based planning, predicated on rigid predefined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. On the other hand, pure generative planning is not yet a reality. Therefore, a sequential hybrid approach at the macro level is proposed where, initially, the part family’s master plan is retrieved, followed by application of modeling tools and solution algorithms to arrive at the plans of the new parts, whose features could exceed its respective original family boundaries. Two distinct generative methods, namely reconfigurable process planning and process replanning, are presented and compared. A genuine reconfiguration of process plans to optimize the scope, extent, and cost of reconfiguration is achieved using a 0–1 integer programming model. Also, because the problem is combinatorial in nature, a random-based evolutionary simulated annealing algorithm has been tailored for replanning. The developed methods are, conceptually and computationally, analyzed and validated using an industrial case study.