Journal of Optimization in Industrial Engineering
Volume:2 Issue: 3, Winter and Spring 2009

In this paper the real-world occurrence of the multiple-product multiple-constraint single period newsboy problem with two objectives, in which there is incremental discounts on the purchasing prices, is investigated. The constraints are the warehouse capacity and the batch forms of the order placements. The first objective of this problem is to find the order quantities such that the expected profit is maximized and the second objective is maximizing the service rate. It is assumed that holding and shortage costs, modeled by a quadratic function, occur at the end of the period, and that the decision variables are integer. A formulation to the problem is presented and shown to be an integer nonlinear programming model. Finally, an efficient hybrid algorithm of harmony search, goal programming, and fuzzy simulation is provided to solve the model. The results are illustrated by a numerical example.

There are some problems with estimating the time required for the manufacturing process of products, especially when there is a variable serving time, like control stage. These problems will cause overestimation of process time. Layout constraints, reworking constraints and inflexible product schedule in multi product lines need a precise planning to reduce volume in particular situation of line stock. In this article, a hybrid model has been presented by analyzing real queue systems with layout constraints as well as by using concepts and principles of Markov chain in queue theory. This model can serve as benchmark to assess queue systems with probable parameters of service. Here, the proposed model will be described drawing on the findings of a case study. Thus, production lines of a home application manufacturer will be analyzed.

In this paper, a new approach to overlapping operations in job shop scheduling is presented. In many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. In each job, embedded operations can be performed due to overlapping considerations in which each operation may be overlapped with the others because of its nature. The effects of the new approach on job shop scheduling problems are evaluated. Since the problem is well known as NP-Hard class, a simulated annealing algorithm is developed to solve large scale problems. Moreover, a mixed integer linear programming (MILP) method is applied to validate the proposed algorithm. The approach is tested on a set of random data to evaluate and study the behavior of the proposed algorithm. Computational experiments confirmed superiority of the proposed approach. To evaluate the effect of overlapping considerations on the job shop scheduling problem, the results of classical job shop scheduling with the new approach (job shop scheduling problem with overlapping operations) are compared. It is concluded that the proposed approach can improve the criteria and machines utilization measures in job shop scheduling. The proposed approach can be applied easily in real factory conditions and for large size problems. It should thus be useful to both practitioners and researchers.

This paper explores the flexile flow lines where setup times are sequence- dependent. The optimization criterion is the minimization of total weighted completion time. We propose an iterated greedy algorithm (IGA) to tackle the problem. An experimental evaluation is conducted to evaluate the proposed algorithm and, then, the obtained results of IGA are compared against those of some other existing algorithms. The effectiveness of IGA is demonstrated through comparison.

This paper presents a mathematical model for a flow shop scheduling problem consisting of m machine and n jobs with fuzzy processing times that can be estimated as independent stochastic or fuzzy numbers. In the traditional flow shop scheduling problem, the typical objective is to minimize the makespan). However,, two significant criteria for each schedule in stochastic models are: expectable makespan and the probability of minimizing the makespan. These criteria can be considered for fuzzy problems as well. In this paper, we propose a solution for the fuzzy model by the use of fuzzy logic based on developing the model presented by MacCahon [18].

In order to study the relationship between random Boolean sets and some explanatory variables, this paper introduces a Propagation model. This model can be applied when corresponding Poisson process of the Boolean model is related to explanatory variables and the random grains are not affected by these variables. An approximation for the likelihood is used to find pseudo-maximum likelihood estimates of propagation model parameters when the grains are nonrandom circle with unknown radii.

Among numerous NP-hard problems, the Traveling Salesman Problem (TSP) has been one of the most explored, yet unknown one. Even a minor modification changes the problem’s status, calling for a different solution. The Generalized Traveling Salesman Problem (GTSP)expands the TSP to a much more complicated form, replacing single nodes with a group or cluster of nodes, where the objective is to find a minimum-length tour containing exactly one node from each cluster. In this paper, a new heuristic method is presented for solving singlevehicle single-depot GTSP with the ability of controlling the search strategy from conservative to greedy and vice versa. A variant algorithm is then developed to accommodate the multi-vehicle single-depot condition, which is modified afterwards to accommodate the multi-vehicle multi-depot GTSP.

This paper considers the problem of scheduling hybrid flowshops with machine availability constraints (MAC) to minimize makespan. The paper deals with a specific case of MAC caused by preventive maintenance (PM) operations. Contrary to previous papers considering fixed or/and conservative policies, we explore a case in which PM activities might be postponed or expedited while necessary. Regarding this flexibility in PM activities, we expect to obtain more efficient schedule. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the problem, we present a high performing metaheuristic based on memetic algorithm incorporating some advanced features. To evaluate the proposed algorithm, the paper compares the proposed algorithm with several wellknown algorithms taken from the literature. Finally, we conclude that the proposed algorithm outperforms other algorithms.