جستجوی مقالات مرتبط با کلیدواژه « transportation times » در نشریات گروه « فنی و مهندسی »

This paper aims at introducing a novel bi-objective model for a Job Shop Scheduling Problem (JSSP) in order to minimize makespan and maximum tardiness simultaneously. Some realistic assumptions, i.e. Fuzzy processing times and due dates involving triangular possibility distributions, transportation times, availability constraints, modified position-based learning effects on processing times, and sum-of-processing-time based learning effects on duration of maintenance activities have been considered, to provide a more general and practical model for the JSSP. Based on the learning effects, Processing times decrease as a machine performs an operation frequently, and workers gain working skills and experiences. In this paper based on DeJong’s learning effect a novel and modified formulation has been proposed for this effect. According to the above-mentioned assumptions, a novel mixed-integer linear programming (MILP) model for the JSSP is suggested. The proposed model is first converted to an auxiliary crisp model, given that model is a possibilistic programming, it is then solved by the TH and ε-constraint methods for small instances, and the results are compared. For medium and large instances, five metaheuristic algorithms, including NSGA-ΙΙΙ, PESA-ΙΙ, SPEA-ΙΙ, NSGA-ΙΙ, and MOEA/D are utilized, and the results are finally compared on the basis of three performance metrics.

Sequence dependent set-up times scheduling problems (SDSTs), availability constraint and transportation times are interesting and important issues in production management, which are often addressed separately. In this paper, the SDSTs job shop scheduling problem with position-based learning effects, job-dependent transportation times and multiple preventive maintenance activities is studied. Due to learning effects, jobs processing times are not fixed during plan horizon and each machine has predetermined number of preventive maintenance activities. A novel mixed integer linear programming model is proposed to formulate the problem for minimizing Make Span. Owing to the high complexity of the problem; we applied Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) to find nearly optimal solutions for medium and large instances. Finally, the computational Results are provided for evaluating the performance and effectiveness of the proposed solution approaches.

Nowadays, </strong>scholars do their best to study more practical aspects of classical problems. Job shop Scheduling Problem (JSSP) is an important and interesting problem in scheduling literature which has been studied from different aspects so far. Considering assumptions like learning effects, flexible maintenance activities and transportation times can make this problem more close to the real life, however these assumptions have rarely been studied in this problem. This paper aims to provide a mathematical model of JSSP which covers these assumptions. MILP model is suggested, Three different sizes of instances are generated randomly,  and this model has been solved for small-sized problems exactly by GAMS software and the effects of learning on reducing the value of objective function is shown. Due to the complexity of the problem, in order to obtain near optimal solutions, medium and large instances are solve by applying Ant Colony Optimization for continuous domains(ACOR) and Invasive weed Optimization (IWO) algorithm, finally results are compared.