International Journal of Industrial Engineering & Production Research

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Project scheduling   Net present value

We consider a project scheduling problem with permitted tardiness and discrete time/resource trade-offs under maximum net present value objective. In this problem, a project consists of a set of sequential phases such that each phase contains one or more sub-projects including activities interrelated by finish-start-type precedence relations with a time lag of zero, which require one or more renewable resources. There is also a set of unconstrained renewable resources. For each activity, instead of a fixed duration and known resource requirements, a total work content respect to each renewable resource is given which essentially indicates how much work has to be performed on it. This work content can be performed in different modes, i.e. with different durations and resource requirements as long as the required work content is met. Based on the cost of resources units and resource requirements of each activity, there is a corresponding cash flow for the activity. Each phase is ended with a milestone that corresponds to the phase income. We prove that the mode corresponding to the minimum possible duration of each activity is the optimal mode in this problem. We also present a simple optima scheduling procedure to determine the finish time of each activity .

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In this paper, we consider scheduling of project networks under minimization of total weighted resource tardiness penalty costs. In this problem, we assume constrained resources are renewable and limited to very costly machines and tools which are also used in other projects and are not accessible in all periods of time of a project. In other words, there is a dictated ready date as well as a due date for each resource such that no resource can be available before its ready date but the resources are allowed to be used after their due dates by paying penalty cost depending on the resource type. We also assume, there is only one unit of each resource type available and no activity needs more than it for execution. The goal is to find a schedule with minimal total weighted resource tardiness penalty costs. For this purpose, we present a hybrid metaheuristic procedure based on the greedy randomized adaptive search algorithm and path-relinking algorithm. We develop reactive and non-reactive versions of the algorithm. Also, we use different bias probability functions to make our solution procedure more efficient. The computational experiments show the reactive version of the algorithm outperforms the non-reactive version. Moreover, the bias probability functions defined based on the duration and precedence relation characteristics give better results than other bias probability functions.

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Public transportation has been one of the most important research fields in the two last decades. The purpose of this paper is to create a schedule for public transport fleets such as buses and metro trains with the goal of minimizing the total transfer waiting time. We extend previous research works in the field of transit schedule with considering headways of each route as decision variables. In this paper, we formulate the problem as a mixed integer linear programming model and solve it using ILOG CPLEX solver. For large-scale test instances, we develop a metaheuristic based on the scatter search algorithm to obtain good solutions in a reasonable CPU run times. Finally, in the computational section, the efficiency of the proposed model and developed algorithm are compared with the existing results in the literature on a real railway network.

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In last decades, mobile factories have been used due to their high production capability, carrying their equipment and covering rough and uneven routes. Nowadays, more companies use mobile factories with the aim of reducing the transportation and manufacturing costs. The mobile factory must travel between the suppliers, visit all of them in each time period and return to the initial location of the mobile factory. In this paper, we present an integer nonlinear programming model for production scheduling and routing of mobile factory with the aim of maximization of profit. This problem is similar to the well-known Traveling Salesman Problem (TSP) which is an NP-hard problem. Also at each supplier, the scheduling problem for production is NP-hard. After linearization, we proposed a heuristic greedy algorithm. The efficiency of this heuristic algorithm is analyzed using the computational studies on 540 randomly generated test instances. Finally, the sensitivity analysis of the production cost, transportation cost and relocation cost was conducted.

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In this paper, a closed-loop supply chain is modeled based on hyperchaotic dynamics. Then, synchronization of the two hyperchaotic closed loop supply chains is performed with a proportional integral (PI) sliding mode controller design method. Using Lyapunov stability theory, it has been proved that the PI sliding mode controller can converge the synchronization error to zero in a limited time. The most important issue in the design of control strategies is the behavior of the control signal. In other words, it affects the cost of design and implementation. Numerical simulation results show that the control signal has low amplitude and fluctuations. so, the PI sliding mode control method can be implemented in the real world. Based on the numerical simulation results, the use of two controllers is proposed to reduce design costs.