Hossein Asefi

A novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times

In this paper, we contemplate the problem of scheduling a set of n jobs in a no-wait flexible flow shop manufacturing system with sequence dependent setup times to minimising the maximum completion time. With respect to NP-hardness of the considered problem, there seems to be no avoiding application of metaheuristic approaches to achieve near-optimal solutions for this problem. For this reason, three novel metaheuristic algorithms, namely population based simulated annealing (PBSA), adapted imperialist competitive algorithm (AICA) and hybridisation of adapted imperialist competitive algorithm and population based simulated annealing (AICA + PBSA), are developed to solve the addressed problem. Because of the sensitivity of our proposed algorithm to parameters values, we employed the Taguchi method as an optimisation technique to extensively tune different parameters of our algorithm to enhance solutions accuracy. These proposed algorithms were coded and tested on randomly generated instances, then to validate the effectiveness of them computational results are examined in terms of relative percentage deviation. Moreover, some sensitive analyses are carried out for appraising the behaviour of algorithms versus different conditions. The computational evaluations manifestly support the high performance of our proposed novel hybrid algorithm against other algorithms which were applied in literature for related production scheduling problems.

A biogeography-based optimisation algorithm for a realistic no-wait hybrid flow shop with unrelated parallel machines to minimise mean tardiness

This paper explores a no-wait hybrid flow shop scheduling problem (NWHFSSP) with realistic assumptions, including unrelated parallel machines at each stage, machine eligibility, sequence-dependent set-up times and different ready times, in order to minimise the mean tardiness. The largest position value rule is proposed to transmute continuous vectors of each solution into job permutations. Also, a novel biogeography-based optimisation (BBO) algorithm is developed to solve the aforementioned problem. To evaluate the effect of various parameters on the performance of the proposed BBO algorithm, response surface methodology (RSM) is employed. Production scenarios for small-scale and large-scale problems are created and tested for the validation purposes. Computational experiment results indicate that the proposed BBO outperforms all of the tested algorithms in terms of four measures, namely, mean relative percentage deviation (RPD), standard deviation of RPD, best RPD and worst RPD. It is shown that BBO produces the best solutions among the tested algorithms in terms of not only the four RPD measures but also computation time.

Adaptation of simulated annealing to an integrated municipal solid waste location-routing problem

This paper aims to propose an integrated municipal solid waste management network covering multiple types of wastes concurrently and utilise a location-routing problem framework to minimise the establishment cost of interrelated facilities (i.e., transfer stations; treatment, recycling and disposal centres) in the network and the transportation cost of wastes in the entire network. The defined problem consists of the concurrent site selection of the locations of the systems all facilities among the candidate locations and the determination of routes and amount of shipments among the selected facilities to minimise the total cost of transportation and facility establishment. As the addressed problem exhibits the non-deterministic polynomial-time hardness (NP-hardness), an adaptation of the simulated annealing algorithm is proposed in this paper. The experiment results, when compared with the exact solutions obtained by mixed-integer programming in terms of solution fitness and computing time, imply that the employed algorithm works effectively and efficiently.

Mathematical modelling and heuristic approaches to the location-routing problem of a cost-effective integrated solid waste management

Integrated solid waste management (ISWM) comprises activities and processes to collect, transport, treat, recycle and dispose municipal solid wastes. This paper addresses the ISWM location-routing problem in which different types of municipal solid wastes are factored concurrently into an integrated system with all interrelated facilities. To support a cost-effective ISWM system, the number of locations of the system’s components (i.e. transfer stations; recycling, treatment and disposal centres) and truck routing within the system’s components need to be optimized. A mixed-integer linear programming (MILP) model is presented to minimise the total cost of the ISWM system including transportation costs and facility establishment costs. To tackle the non-deterministic polynomial-time hardness of the problem, a stepwise heuristic method is proposed within the frames of two meta-heuristic approaches: (i) variable neighbourhood search (VNS) and (ii) a hybrid VNS and simulated annealing algorithm (VNS + SA). A real-life case study from an existing ISWM system in Tehran, Iran is utilized to apply the proposed model and algorithms. Then the presented MILP model is implemented in CPLEX environment to evaluate the effectiveness of the proposed algorithms for multiple test problems in different scales. The results show that, while both proposed algorithms can effectively solve the problem within practical computing time, the proposed hybrid method efficiently has produced near-optimal solutions with gaps of < 4%, compared to the exact results. In comparison with the current cost of the existing ISWM system in the study area, the presented MILP model and proposed heuristic methods effectively reduce the total costs by 20–22%.