Makespan In Job Shop Scheduling Problem Research Articles

Optimization of Makespan in Job Shop Scheduling Problem by Golden Ball Algorithm

<em>Job shop scheduling problem (JSSP) is considered to belong to the class of NP-hard combinatorial optimization problem. Finding a solution to this problem is equivalent to solving different problems of various fields such as industry and logistics. The objective of this work is to optimize the makespan in JSSP using Golden Ball algorithm. In this paper we propose an efficient adaptation of Golden Ball algorithm to the JSSP. Numerical results are presented for 36 instances of OR-Library. The computational results show that the proposed adaptation is competitive when compared with other existing methods in the literature; it can solve the most of the benchmark instances.</em>

A hybrid computer simulation-adaptive neuro-fuzzy inference system algorithm for optimization of dispatching rule selection in job shop scheduling problems under uncertainty

Unstable environment of industrial systems is a source of various uncertainties in production features such as processing times. Moreover, selecting appropriate dispatching rules is a complex and significant issue in practical problems under uncertainty. Most previous studies have pointed out that using a single dispatching rule does not necessarily result in an optimal schedule. This study proposes a novel hybrid algorithm based on computer simulation and adaptive neuro-fuzzy inference system (ANFIS) to select optimal dispatching rule for each machine in job shop scheduling problems (JSSPs) under uncertain conditions so that makespan is minimized. It captures uncertainty using fuzzy set theory and assumes that processing times are in the form of fuzzy numbers. This algorithm contributes to the previous works in two important ways. First, the inherent uncertainty of JSSPs is reflected in fuzzy processing times. Second, this is the first study that develops an approach based on computer simulation and ANFIS for selecting the optimal dispatching rules and minimizing the makespan in JSSPs under uncertainty. The computational results demonstrate the superiority of this algorithm over the previous studies in the literature.

A Hybrid Particle Swarm Optimization Algorithm for Job-Shop Scheduling Problem

In this paper, a hybrid particle swarm algorithm is proposed to minimize the makespan of job-shop scheduling problem which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO as an evolutionary algorithm, it combines coarse global search capability (by neighboring experience) and local search ability. Simulated annealing (SA) as a neighborhood search algorithms, it has strong local search ability and can employ certain probability and can to avoid becoming trapped in a local optimum. Three neighborhood SA algorithms is designed and combined with PSO(called HPSO), for each best solution that particle find, SA is performed on it to find it’s best neighbor solution. The effectiveness and efficiency of HPSO are demonstrated by applying it to 43 benchmark job-shop scheduling problems. Comparison with other researcher’s results indicates that HPSO is a viable and effective approach for the job-shop scheduling problem.