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This paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.

A mixed integer linear programming model is devised, which utilizes the splitting of noncritical activities as a mean toward leveling the renewable resources. The developed model minimizes renewable resources leveling costs along with consumable resources related costs, and it is solved using IBM ILOG CPLEX optimization package. A hybrid metaheuristic procedure is also proposed to efficiently solve the model for larger projects with complex networks structure.

The results confirmed the significance of the integrated approach as both the project schedule and the material ordering policy turned out to be different once compared to the sequential approach under same parameter settings. Furthermore, the integrated approach resulted in substantial total costs reduction for low values of the acquiring and releasing costs of the renewable resources. Computational experiments conducted over 240 test instances of various sizes, and complexities illustrate the efficiency of the proposed metaheuristic approach as it yields solutions that are on average 1.14% away from the optimal ones.

This work highlights the necessity of having project managers address project scheduling and materials lot sizing decisions concurrently, rather than sequentially, to better level resources and minimize materials related costs. Significant cost savings were generated through the developed model despite the use of a small-scale example which illustrates the great potential that the integrated approach has in real life projects. For real life projects with complex network topology, practitioners are advised to make use of the developed metaheuristic procedure due to its superior time efficiency as compared to exact solution methods.

The sequential approach, wherein a project schedule is established first followed by allocating the needed resources, is proven to yield a nonoptimized project schedule and materials ordering policy, leading to an increase in the project's total cost. The integrated approach proposed hereafter optimizes both decisions at once ensuring the timely completion of the project at the least possible cost. The proposed metaheuristic approach provides a viable alternative to exact solution methods especially for larger projects.

Addresses a deterministic maintenance‐scheduling problem for a group of non‐identical machines. The problem is formulated as a mixed integer non‐linear program which is approached heuristically. In the model formulation, it is assumed that a major overhaul brings the machine back to its new condition, whereas a minor overhaul restores it to a specified operating condition. An iterative solution procedure is developed to obtain a near‐optimal cyclic overhaul schedule. Presents a worked example to illustrate the proposed solution method.

Models inspection co‐ordination of a group of machines using a non‐linear integer program. The objective of the model is to minimize the expected total cost per unit time, which is a function of a major inspection cost, minor inspection cost and the cost of failure, subject to the restriction that the inspection interval of each machine is an integer multiple of a basic time interval. Proposes a solution procedure to solve the inspection co‐ordination problem. Uses a numerical example to illustrate the model and its proposed solution method.

In this paper, the joint replenishment problem is modeled for a two-level supply chain consisting of a single supplier and multiple retailers that use the vendor-managed inventory (VMI) policy for several products. This paper aims to find the optimal number of products to order in both policies, the optimal times at which each retailer orders the products in the traditional policy and the optimal times at which the supplier orders the product in the VMI policy.

The problem is first formulated into the framework of a constrained integer nonlinear programming model; then, the problem is solved using a teacher-learner based optimization algorithm. As there are no benchmarks available in the literature, a genetic algorithm is used as well to validate the results obtained.

The solutions obtained using both the algorithms for several numerical examples are compared to the ones of a random search procedure for further validation. A real case is solved at the end to demonstrate the applicability of the proposed methodology and to compare both the policies.

The paper does not have any special limitations.

The study has significant practical implications for the sellers and for the suppliers who have to get the most profit. Also, satisfying the constraints make decision more complicated.

This paper has two main originalities. The authors have developed the model of the joint replenishment problem and have contributed in the problem-solving process. They have used a new meta-heuristic and then compared it to a classic one.