Fixed charge solid transportation problem (FCSTP) is one of the main and most important problems in transportation and network research areas. To tackle such an NP-hard problem, An ELECTROMAGNETISM-like ALGORITHM (EM) is employed. To the best of our knowledge, EM has been considered for any kind of transportation problems. Due to the significant role of parameters on the ALGORITHM’s performance, a calibration in EM is carried out with the aid of a set of experimental design. The efficiency of employed parameters is measured by the experimental design method. To evaluate the performance of the proposed EM, a computational study has been conducted and the associated results obtained by the EM are compared with simulated annealing ALGORITHM (SA).

An ELECTROMAGNETISM ALGORITHM is a meta-heuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper presents an application of the ELECTROMAGNETISM ALGORITHM to supplier selection in a production planning process where there are multiple products and multiple customers and also capacity constraints. We consider a situation where the demand quantity of multiple discrete products is known over a planning horizon. The required raw material for each of these products can be purchased from a set of approved suppliers. Also, a demand-dependent delivery time (due date) and maximum delivery time (deadline) apply for each demand. Problems containing all these assumptions have not been addressed previously in the literature. A decision needs to be made regarding what raw material to order and in what quantities, which suppliers and, finally, at which periods. Numerical results indicate that the ELECTROMAGNETISM ALGORITHM exhibits impressive performances with small error ratios. The results support the success of the ELECTROMAGNETISM ALGORITHM application to the supplier selection problem of interest.

In this paper, two different sub-problems are considered to solve a resource constrained project scheduling problem (RCPSP), namely i) assignment of modes to tasks and ii) scheduling of these tasks in order to minimize the make span of the project. The modified ELECTROMAGNETISM-like ALGORITHM deals with the first problem to create an assignment of modes to activities. This list is used to generate a project schedule. When a new assignment is made, it is necessary to fix all mode dependent requirements of the project activities and to generate a random schedule with the serial SGS method. A local search will optimize the sequence of the activities. Also in this paper, a new penalty function has been proposed for solutions which are infeasible with respect to non-renewable resources. Performance of the proposed ALGORITHM has been compared with the best ALGORITHMs published so far on the basis of CPU time and number of generated schedules stopping criteria. Reported results indicate excellent performance of the ALGORITHM.

This paper studies the multi-stage supply chain system (MSSCM) controlled by the kanban mechanism. In the kanban system, decision making is based on the number of kanbans as well as batch sizes. A kanban mechanism is employed to assist in linking different production processes in a supply chain system in order to implement the scope of just-in-time (JIT) philosophy. For a MSSCM, a mixed-integer nonlinear programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter. Since the adopted model is of MINLP type and solving it by branch and bound (B& B) takes time, a metaheuristic is presented. This metaheuristic is an electromagnetic ALGORITHM (EA). The EA is compared against an existing ALGORITHM and also B& B results to evaluate the proposed metaheuristic. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than B& B with regard to the objective functions considered in this paper.

In this paper, we study a flow shop batch processing machines scheduling problem. The fuzzy due dates are considered to make the problem more close to the reality. The objective function is taken as the weighted sum of fuzzy earliness and fuzzy tardiness. In order to tackle the given problem, we propose a hybrid ELECTROMAGNETISM-like (EM) ALGORITHM, in which the EM is hybridized with a diversification mechanism and effective local search to enhance the efficiency of the ALGORITHM.The proposed ALGORITHMs are evaluated by comparison against two existing well-known EMs in the literature. Additionally, we propose some heuristics based on the earliest due date (EDD) to solve the given problem. The proposed hybrid EM ALGORITHM is tested on sets of various randomly generated instances. For this purpose, we investigate the impacts of the rise in problem sizes on the performance of the developed ALGORITHM. Through the analysis of the experimental results, the highly effective performance of the proposed ALGORITHM is shown against the two existing well-known EMs from the literature and proposed EDDs.

There are several approaches to the concept of "Filed" in the classical theory of ELECTROMAGNETISM; Operationalism and Realism are two important strategies in this regard. I this paper the Realistic view of Marc Lang is introduced as well as the empiricist views of Carnap and Bridgman. Lang in his famous book "An Introduction to the Philosophy of Physics" suggests two essential questions about "action at distance" and "reality of fields" and after a proper discussion conclude that the Classical Physics is not capable of dissolving the problems relating the locality and the ontology of energy and field, and it is the mission of the Special Theory of Relativity.

This paper considers open-shop scheduling with no intermediate buffer to minimize total tardiness. This problem occurs in many production settings, in the plastic molding, chemical, and food processing industries. The paper mathematically formulates the problem by a mixed integer linear program. The problem can be optimally solved by the model. The paper also develops a novel metaheuristic based on an ELECTROMAGNETISM ALGORITHM to solve the large-sized problems. The paper conducts two computational experiments. The first includes small-sized instances by which the mathematical model and general performance of the proposed metaheuristic are evaluated. The second evaluates the metaheuristic for its performance to solve some large-sized instances. The results show that the model and ALGORITHM are effective to deal with the problem.

Scheduling problems with batch processing machines (BPM) assume that machines are continuously available, and no time is needed for their preventive maintenance (PM). In this paper, we study a realistic variant of flowshop scheduling which integrates flow shop batch processing machines (FBPM) and preventive maintenance for minimizing the makespan. In order to tackle the given problem, we employ reliability concept, and develop an ELECTROMAGNETISM-like (EM) ALGORITHM, as an evolutionary technique, and propose an enhanced EM ALGORITHM, in which the EM is hybridized with a diversification mechanism, and an effective local search to enhance the efficiency of the ALGORITHM. The proposed ALGORITHMs are evaluated by comparison against two existing well-known EMs in the literature. For this purpose, we study the behavior and investigate the impacts of the rise in problem sizes on the performance of the developed ALGORITHM. The superiority of our EM is inferred from computational results obtained in various circumstances.