This article investigates a JIT Single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms’ parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.

A multi-agent Single machine scheduling problem with transportation constraints is studied. We assume that there are several independent agents placed in different geographical locations, each of them has several orders and each order includes different types of products. We use a simple and effective model to obtain maximum profit of the products. To have desired on-time deliveries, the minimization of the transportation costs and total tardiness costs are considered as objective functions. The main idea of this research is to develop a simple and integrated scheduling and transportation model which can be applied in many factories, chain stores, and so on. In order to solve this problem, a mixed integer linear programming (MILP) model is presented. Moreover, since solving large instances of the proposed MILP model is very time-consuming, a heuristic algorithm is presented. Implementing of two approaches on a variety of datasets show that the heuristic algorithm can provide good-quality solutions in very short time.

This paper considers the Single machine scheduling problem with precedence constraints and deteriorating jobs and a mathematical model based on binary integer programming (BIP) is developed. Since the precedence constraints exist, a job cannot start before completion of its all predecessors. The proposed model consists of two steps: in the first step, the earliest starting time of each job is computed, then the results are used in the second step in which an optimal sequence between jobs is determined with the aim of minimizing the total completion time. Finally, a numerical example is presented and solved using optimization software LINGO 8.0.

This research article proposes a Single machine scheduling problem subject to distinct due dates in fuzzy environment. The proposed problem is considered in fuzzy environment with an attempt to sequence of the 𝑛 − jobs. The total penalty cost is considered as the composition of all the total earliness and tardiness cost. The aim of this research work is to minimize the total penalty cost. A method to minimize the total penalty cost due to earliness or lateness of job in fuzzy environment is proposed. A numerical example is illustrated to support the proposed method in this study.

This paper employs an interactive possibility linear programming approach to solve a Single machine scheduling problem with imprecise processing times, due dates, as well as earliness and tardiness penalties of jobs. The proposed approach is based on a strategy of minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining a lower total costs, and minimizing the risk of obtaining higher total costs simultaneously. This approach is applicable to just-in-time systems, in which many firms face the need to complete jobs as close as possible to their due dates. The objective of the model is to minimize the total costs of earliness/tardiness penalties. In this paper, the proposed possibility linear programming approach is applied to a fuzzy Single machine scheduling problem with respect to the overall degree of decision maker satisfaction. Due to the proposed model’s complexity, conventional optimization methods cannot be utilized in reasonable time. Hence, the particle swarm optimization method is applied toward its solution.

This paper deals with minimization of tardiness in Single machine scheduling problem when each job has two different due-dates i.e. ordinary due-date and drop dead date. The drop dead date is the date in which jobs’ weights rise sharply or the customer cancels the order. A linear programming formulation is developed for the problem and since the problem is known to be NP-hard, three heuristic algorithms are designed for the problem based on Tabu search mechanism. Extensive numerical experiments were conducted to observe and compare the behavior of the algorithms in solving the problem.

Order acceptance and scheduling problem consist of simultaneously deciding which orders to be selected and how to schedule these selected orders. An extension of the sequence-dependent setup times and release dates was introduced in 2010 and a mathematical formulation was presented. Since then, a few mathematical formulations have appeared in the literature by addressing this problem on the basis of sequence-dependent setup times. However, some of the mathematical formulations are nonlinear or lack usability. Therefore, the model presented in 2010 is still being considered in recent studies. In this paper, we investigated the case in which there are sequence-dependent setup times with no release dates for all orders. We developed a new mathematical formulation with O(n2) binary variables and O(n2) constraints. In order to see the performance of our formulation, we conducted a computational analysis with CPLEX 12. 4 by solving benchmark instances available in the literature. To manage the comparison, we reduced the existing formulation to the without release dates for all orders. As a result, we observed that the existing formulation can solve the test problems with up to 10 orders in a given time limit. On the other hand, our proposed formulation can solve all the available instances with up to 100 orders within the same time limit. Our proposed formulation is extremely faster than the existing one and can solve small and moderate sized real-life problems in a reasonable time. Thus, the researchers do not need any special heuristics for solving such problems. Instead, they can directly use our formulation with an optimizer.