جستجوی مقالات مرتبط با کلیدواژه « budget constraint » در نشریات گروه « صنایع »

Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fixed interval preventive maintenance (PM) and budget constraint are considered.PM activity is a crucial task to reduce the production efficiency. In the current research we focused on a scheduling problem which a job is processed at the upstream stage and all the downstream machines get busy or alternatively PM cost is significant, consequently the job waits inside the buffers and increases the associated holding cost. This paper proposes a new more realistic mathematical model which considers both the PM and holding cost of jobs inside the buffers in the stochastic flexible flow shop scheduling problem. The holding cost is controlled in the model via the budget constraint. In order to solve the proposedmodel, three hybrid metaheuristic algorithms are introduced. They include a couple of well-known metaheuristic algorithms which have efficient quality solutions in the literature. The two algorithms of them constructed byincorporationof the particle swarm optimization algorithm (PSO) and parallel simulated annealing (PSA) methods under different random generation policies. The third one enriched based on genetic algorithm (GA) with PSA. To evaluate the performance of the proposed algorithms, different numerical examples are presented. Computational experiments revealed that the proposed algorithms embedboth desirable accuracy and CPU time. Among them, the PSO-PSAП outperforms than other algorithms in terms of makespan and CPU time especially for large size problems.

The supply chain network design has a crucial role in decreasing total transportation cost. On the other hand, the value of some effective parameters, such as established facilities cost and demand, often is uncertain. In this regard, a multi-objective multi-commodity scenario-based supply chain model in the presence of disaster is proposed. Minimizing the probability of travel time exceeded at a pre-specific threshold value in different scenarios is defined as the objective function. In addition, failure probability and budget constraint can be considered as other innovations of this paper. A multi-objective vibration damping optimization (MOVDO) algorithm is developed to solve large-scale instances of the presented problem. The obtained results show that a 75-node network can be solved.

A facility layout problem is concerned with determining the best position of departments, cells, or machines on the plant. An efficient layout contributes to the overall efficiency of operations. It’s been proved that, when system characteristics change, it can cause a significant increase in material handling cost. Consequently, the efficiency of the current layout decreases or is lost and it does necessitate rearrangement. On the other hand, the rearrangement of the workstations may burden a lot of expenses on the system. The problem that considers balance between material handling cost and the rearrangement cost is known as the Dynamic Facility Layout Problem (DFLP). The objective of a DFLP is to find the best layout for the company facilities in each period of planning horizon considering the rearrangement costs. Due to the complex structure of the problem, there are few researches in the literature which tried to find near optimum solutions for DFLP with budget constraint. In this paper, a new heuristic approach has been developed by combining Genetic Algorithm (GA) and Parallel Simulated Annealing Algorithm (PSAA) which is the main contribution of the current study. The results of applying the proposed algorithm were tested over a wide range of test problems taken from the literature. The results show efficiency of the hybrid algorithm GA- to solve the Dynamic Facility Layout Problem with Budget Constraint (DFLPBC).

In today’s competitive transportation systems, passengers search to find traveling agencies that are able to serve them efficiently considering both traveling time and transportation costs. In this paper, we present a new model for the traveling salesman problem with multiple transporters (TSPMT). In the proposed model, which is more applicable than the traditional versions, each city has different transporting vehicles and the cost of travel through each city is dependent on the transporting vehicles type. The aim is to determine an optimal sequence of visited cities with minimum traveling times by available transporting vehicles within a limited budget. First, the mathematical model of TSPMT is presented. Next, since the problem is NP-hard, a new hybrid parallel simulated annealing algorithm with a new coding scheme is proposed. To analyze the performance of the proposed algorithm, 50 numerical examples with different budget types are examined and solved using the algorithm. The computational results of these comparisons show that the algorithm is an excellent approach in speed and solution quality.