Figure 2 - uploaded by Reza Moghdani
Content may be subject to copyright.
Source publication
One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers' service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an or...
Citations
... Experimentally, MOCS (Multi-objective Cuckoo Search) has shown the most efficient Pareto solution on the basis of computational time, mean ideal distance, and spacing thread with the comparison of Multi-Objective Imperialist Competitive Algorithm (MOICA) and even MOPSO (Multi-Objective Particle Swarm Optimization). Jamali et al. [70] used an inventory priority model for a supply chain network model with demand uncertainty and a cost-minimization approach. An improved hybrid cuckoo search algorithm along with genetic algorithm had implemented to solve that model. ...
Combinatorial optimization problems are often considered NP-hard problems in the fieldof decision science and the industrial revolution. As a successful transformation to tackle complexdimensional problems, metaheuristic algorithms have been implemented in a wide area of combi-natorial optimization problems. Metaheuristic algorithms have been evolved and modified withrespect to the problem nature since it was recommended for the first time. As there is a growinginterest in incorporating necessary methods to develop metaheuristics, there is a need to rediscoverthe recent advancement of metaheuristics in combinatorial optimization. From the authors’ pointof view, there is still a lack of comprehensive surveys on current research directions. Therefore, asubstantial part of this paper is devoted to analyzing and discussing the modern age metaheuristicalgorithms that gained popular use in mostly cited combinatorial optimization problems such asvehicle routing problems, traveling salesman problems, and supply chain network design problems.A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combina-torial optimization problems is carried out in this study, apart from conventional metaheuristicslike simulated annealing, particle swarm optimization, and tabu search. These metaheuristics havebeen filtered through some key factors like easy parameter handling, the scope of hybridization aswell as performance efficiency. In this study, a concise description of the framework of the selectedalgorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed,along with the impacts of algorithm modification on performance, constraint handling strategy, thehandling of multi-objective situations using hybridization, and future research opportunities.
... Gholami et al. (2018) "ABC analysis of clients using axiomatic design and incomplete estimated meaning". Jamali et al. (2018) "Hybrid Improved Cuckoo Search Algorithm and Genetic Algorithm to Solve Marko Modulated Demand". ...
As the emission of carbon dioxide has resulted in many issues in the global environment, controlling carbon emission has become a high priority for governments. One of the sectors engaged with carbon emission is inventory management. A lot of activities in inventory systems such as purchasing, warehousing, and transporting the items lead to emitting carbon. Therefore, governments have ruled policies to mitigate the emissions in inventory systems and develop sustainable supply chains. Despite the importance of this issue, no attempts have been made to study and address the vital role of different policies in controlling carbon emissions in review progress. This paper provides a systematic literature review to analyze the impact of carbon emission policies on inventory systems. 75 papers have been extracted from the most relevant academic and research databases and the results have been analyzed and synthesized. By classifying and introducing different carbon policies applicable in inventory systems, this paper introduces the policies that make effort to restrict the emissions. Finally, theoretical and managerial insights and extensive opportunities for future research are outlined.
... A case study is also presented to select the contractor in a power plant project. Jamali et al. (2018) proposed a stochastic inventory control problem using discrete Markov-modulated demand. A simulation-based optimization technique is used to approximate good quality solutions of this problem. ...
Artificial neural network (ANN) is modeled to predict and classify problems. However, in the training phase of ANNs discovering faultless values of the weights of a network is extremely troublesome. Traditional weight updating methods often get stuck into local optima and converge to optimal solutions very slowly. Therefore, to overcome these drawbacks a modified version of a nature-based algorithm which merges meta-heuristics with weight-updating technique of ANN has been used in this paper. Whale optimization algorithm (WOA) is a well-established, efficient and competitive algorithm inspired by the hunting mechanism of the whales including their behavior in finding and attacking their prey with their bubble-net feeding technique. In WOA, the next location of the search individuals or whales is modified depending on some probability. Due to the high exploration rate of WOA, there is a disproportion between exploration and exploitation in the WOA and it also converges to the solution slowly. Thus, to establish an equilibrium between exploration and exploitation a new variant of WOA called modified whale optimization algorithm (MWOA) is proposed to overcome the problem of delayed convergence. In MWOA, roulette wheel selection is combined with WOA to enhance the convergence speed of WOA. MWOA is tested on 11 benchmark functions, and the outcomes are compared with WOA. The results prove that MWOA has gained success in overcoming the problem of the slow convergence of WOA. Also, the results show that the proposed MWOA technique, when applied to ANN, can overcome the problems of traditional techniques and has improved the results.
... Then, each risk/crisis (shortage of medicines) can affect the whole medicine supply chain instantaneously. This problem can not only waste the resources but can also jeopardize patients' lives by making trouble for access to medical products [22,39]. ...
The role of medicines in health systems is increasing day by day. The medicine supply chain is a part of the health system that if not properly addressed, the concept of health in that community is unlikely to experience significant growth. To fill gaps and available challenging in the medicine supply chain network (MSCN), in the present paper, efforts have been made to propose a location-production-distribution-transportation-inventory holding problem for a multi-echelon multi-product multi-period bi-objective MSCN network under production technology policy. To design the network, a mixed-integer linear programming (MILP) model capable of minimizing the total costs of the network and the total time the transportation is developed. As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems. Then, some experiments were designed and solved by an optimization solver called GAMS (CPLEX) and the presented algorithms to validate the model and effectiveness of the presented algorithms. Comparison of the provided results by the presented algorithms and the exact solution is indicative of the high-quality efficiency and performance of the proposed algorithm to find a near-optimal solution within reasonable computational time. Hence, the results are compared with commercial solvers (GAMS) with the suggested algorithms in the small-sized problems and then the results of the proposed meta-heuristic algorithms with the heuristic methods are compared with each other in the large-sized problems. To tune and control the parameters of the proposed algorithms, the Taguchi method is utilized. To validate the proposed algorithms and the MSCN model, assessment metrics are used and a few sensitivity analyses are stated, respectively. The results demonstrate the high quality of the proposed IACO algorithm.
... Takami et al. [40] focused on two issues for a project porfolio selection model by considering hesitant fuzzy weighted averaging operator. Jamali et al. [21] formulated a supply chain management network under discrete Markov-modulated demand. Considering the demand rate as triangular fuzzy number Moghdani et al. [27] extended the EPQ model with multiple deliveries for the space constraints. ...
Brand substitution is common observed phenomenon in daily life. It is the decision
makers’ economic understanding and potential scheme for business-industries. Also,
it provides the flexibility in management and increases the ability to control the production.
This works proposes an integrated supplier-retailer inventory model for substitutable
products. Two suppliers with two different brand products with their corresponding
demand are involved and one retailer sells each of the products. To nullify the
complexities of the joint optimization problem, we first develop a deterministic model
for three cases: no substitution, partial substitution and full substitution, then we go
for its fuzzification. Keeping the financial constraint of each producer, we have studied
over the elasticity of the cost parameters by means of triangular dense fuzzy lock set
approach with its locking and unlocking property for final decision making. Finally,
sensitivity analysis and graphical illustrations are made to justify the model.
... Takami et al. (2018) focused on two issues for a project porfolio selection model by considering hesitant fuzzy weighted averaging operator. Jamali et al. (2018) formulated a supply chain management network under discrete Markov-modulated demand. Considering the demand rate as triangular fuzzy number Moghdani et al. (2019) extended the EPQ model with multiple deliveries for the space constraints. ...
... De and Sana (2018a) analyzed an economic production-inventory quantity model for stochastic-uncertain demand with an order size, reorder point, and leadtime as decision variables. Jamali, Sana, and Moghdani (2018) developed an efficient inventory control policy for stochastic demands to reduce unnecessary under and over-stock expenses and maintain a high customer service level. They employed the simulation-based optimization methods to achieve optimal solutions and proposed a genetic algorithm and hybrid improved cuckoo search algorithm to solve these types of problems. ...
For the production of complex products, this study investigates a flexible production system with a variable production rate as an alternative method to overcome the stock-out risk because of the uncertainty of fuzzy-stochastic demand in an integrated model. The variable production rate enables vendors to fulfill demand uncertainties and reduce the lead time. To establish the relationship between the process quality and production rate three functions, linear, quadratic, and cubic, have been introduced in the mode. The development of such an advanced flexible production system requires a considerably higher setup cost and increases the supply chain cost. To overcome this, the authors introduce a discrete investment function to control setup costs. Authors utilize a crashing cost to reduce the duration of the lead time within the supply chain (SC) structure. In real-life, vendors and buyers face different constraints and set some targets for themselves. Here, the authors consider the storage space and budget constraint for the vendor and customers’ service level constraint for the buyer. An SC model is proposed to find the optimal order quantity, reorder point, lead time, investment for setup cost reduction, and production rate with the minimized total expected cost of the chain. To get the optimal solutions to decision variables, the authors employed a classical optimization technique in the proposed model. An improved algorithm for the global minimum expected cost of SC is designed under the flexible production system. Three numerical examples with comparative study to the previous model and the sensitivity analysis are included to test and validate the proposed model. The numerical analysis and comparative study prove that the proposed model attains the minimum SC cost at the decision variables' optimal values.
... Immediately consideration of dynamic factors in production is known as reactive or real-time schedules [2]. Seeking the optimization of manufacturing processes, inventories and manufacturing costs has a significant impact on the organizational management of a company, and these have impacts on the decision-making process [3][4][5][59][60][61].The plant floor will always require optimized tasks, to better meet all customers' requirements [6][7][8]. The processes are aimed to be sustainable, efficient, and profitable [9][10][11]. ...
This research addresses a novel Flexible Manufacturing System focused on rescheduling production which combines the techniques of Preti Nets(PetN), discrete simulation and memetic algorithms such as genetic algorithms(AG), simulated annealing (SA) and local search (LS). Reactive scheduling is caused by a failure of the machines. This hybrid algorithm is called as "PetNMA" and consider the performance measures such as makespan and Total Weighted Tardiness (TWT). The reactive program considers the operations which have not yet been processed at the time of the failure and the operation is processed by the machine at that moment. The PetNMA algorithm consists of three sub-algorithms: the first for the initial scheduling; the second to simulate the execution of the initial program set until the machine failure occurs; and the third to build reactive scheduling through the application of a Memetic Algorithm. We consider the problems that represent an FMS those are contrasted with bottleneck heuristics and dispatch rules obtaining good results in the proposed objectives. In the initial schedule instances, the proposed genetic algorithm improves in 50%, 70% and 70%, in the makespan, total weighted tardiness and combination respectively. The 58% of the results in rescheduling have a variation between 0% and 2% relative to the initial scheduling. The 95%, 85% and 85% of the rescheduling results show a variation between 0% and 2% with respect to the dispatch rules for the Makespan, the total weighted tardiness and its combination respectively.
... After that, Craven proposed the next monograph in 1988. Jamali et al [3] proposed an approach, hybrid Improved Cuckoo Search algorithm (ICS) and Genetic Algorithm (GA) to solve the Markov-Modulated Demand problem. Veeramani et al [15] proposed a solution procedure to solve fuzzy linear fractional programming problems. ...
Multi-objective linear plus linear fractional programming problem is an emerging tool for solving problems in different environments such as production planning, financial and corporate planning and healthcare and hospital planning which has attracted many researchers in recent years. This paper presents a method to find a Pareto optimal solution for the multi-objective linear plus linear fractional programming problem through nonlinear membership function. The proposed approach defines a fuzzy goal for each objective through a nonlinear membership function. By means of nonlinear membership function, the multi-objective linear plus linear fractional programming problem transformed into a multi-objective nonlinear programming problem. Applying the linear approximation method, the nonlinear objectives are converted into linear. In order to solve the multi-objective linear programming problem, the fuzzy goal programming model is formulated by minimizing the negative deviational variables. The proposed procedure is illustrated through numerical examples and a real-life application problem. Further, it is compared with the existing methods. Finally, the Euclidean distance function has been used to prove the efficiency of the proposed method.
... After that, Craven proposed the next monograph in 1988. Jamali et al [3] proposed an approach, hybrid Improved Cuckoo Search algorithm (ICS) and Genetic Algorithm (GA) to solve the Markov-Modulated Demand problem. Veeramani et al [15] proposed a solution procedure to solve fuzzy linear fractional programming problems. ...
This paper presents an efficient metaheuristic approach for optimizing the generalized ratio problems such as the sum and multiplicative of linear or nonlinear ratio objective function with affine constraints. This paper focuses on the significance of hybrid techniques, which are implemented by using GA and ER-WCA to increase efficiency and robustness for solving linear and nonlinear generalized ratio problems. Initially, GA starts with an initial random population and it is processed by genetic operators. ER-WCA will observe and preserve the GAs fittest chromosome in each cycle and every generation. This Genetic ER-WCA algorithm is provided with better optimal solutions while solving constrained ratio optimization problems. Also, the effectiveness of the proposed genetic ER-WCA algorithm is analyzed while solving the large scale ratio problems. The results and performance of the proposed algorithm ensures a strong optimization and improves the exploitative process when compared to the other existing metaheuristic techniques. Numerical problems and applications are used to test the performance of the convergence and the accuracy of the approached method. The behavior of this Genetic ER-WCA algorithm is compared with those of evolutionary algorithms namely Neural Network Algorithm, Grey Wolf Optimization, ER-WCA, Water Cycle Algorithm, Firefly algorithm, Cuckoo search algorithm. The evaluated results show that the proposed algorithm increases the convergence and accuracy more than other existing algorithms.
