This paper presents a literature review for inventory control of deteriorating items since 2018. A classification including 18 classes and 33 subclasses is offered to categorize inventory control models, constraints, and solution methods used in previous studies. Providing standard classes in this field, such as demand, deterioration, shortages, number of warehouses, and time value of money alongside new classes, for example, the type of model costs and supply chain, inventory constraints, number of supply chain levels, time horizon, lead time, considering multi-item models, preservation technology, financial conditions, non-instantaneous deteriorating items, environmental issues, and solution methods made this classification more comprehensive. A brief history and explanation are given to understand each class better, and related articles are grouped in these classes. The research gaps and a crucial aspect that paves the way for future research are presented in each category. A broad view of the future of this topic is provided, and exciting opportunities are highlighted for researchers to contribute to this field and inspire them to explore these potential areas of research. The potential for future research in this subject is vast and promising; this article offers numerous opportunities for researchers to make significant contributions. The results show that the best ways to extend this topic are using variable deterioration rates, costs, and demand functions, considering realistic assumptions, including allowable shortages with partial backlogging, two warehouses, inflation and discounts, preservation technology, uncertain lead time, and environmental issues. Developing cyclic (if possible), multi-item, and production models with financial conditions and various inventory constraints is an excellent way to develop existing models. Finally, solving the proposed models using exact methods to find the global answer is a great effort to contribute to this field.

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

The increasing variety of types of food circulating in Bekasi, has both positive and negative impacts at the same time. The positive impact is the increasing variety of food choices owned by Bekasi residents. In addition, the negative impact is that there is often no guarantee of halalness for the foods offered, both in terms of raw materials, processes and presentations. This study aims to determine the effect of internal control of raw materials and production planning on the smooth process of SMEs, especially based on halal food in Bekasi. The study involved 49 SME entrepreneurs in Bekasi and processed data using the multivariate statistical method and path analysis. The results show that in very specific cases, namely halal food products, internal control and production process planning had a positive influence on the smoothness of the production process and business competitiveness.

Activity-Based-Model (ABC) is used for the purpose of significant improvement for overhead accounting systems by providing the best information required for managerial decision. This pa-per discusses implacability of ABC technique on inventory valuation as a management account-ing innovation. In order to prove the applicability of ABC for inventory control a material driven medium-sized and privately owned company from engineering (iron and steel) industry is select-ed and by analysis of its production process and its material dependency and use of indirect in-ventory, an ABC model is explored for better inventory control. The case revealed that the ne-cessity of ABC in the area of inventory control is significant. The company is not only able to increase its quality of decision but also it can significantly analyze its cost of direct material cost, valuation of direct material and use its implications for better decision making.

Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to create partitions so that all members of each set are similar according to a specified set of metrics. Both the association rule mining and clustering helps in more effective individual and group decision making for optimal inventory control. Owing to the above facts, association rules are mined from each cluster to find frequent items and then loss profit is calculated for each frequent item. Initially, the clustering algorithm is used to partition the transactional database into different clusters. Apriori, a classic data mining algorithm is utilized for mining association rules from each cluster to find frequent items. Later the loss profit is calculated for each frequent item. The obtained loss profit is used to rank frequent items in each cluster. Thus, the ranking of frequent items in each cluster using the proposed approach greatly facilitate optimal inventory control. An example is illustrated to validate the results.

The present study reviews different studies on inventory control of deteriorating items in chain supply published over the period 1963- 2013. The study investigates supply chain of the items in terms of various perspectives. Finally, the summary of the studies is shown in two tables for one-echelon and multi-echelon supply chain including the main information and assumptions of each paper. In the mentioned tables, the papers were classified in terms of the type of demand rate, deterioration rate, solution procedure and findings. It can be said that no analysis on the results was done in the present study and it can be only used as a good reference in the study field for other researchers.

Determining the optimal inventory control and selling price for deteriorating items is of great significance. In this paper, a joint pricing and inventory control model for deteriorating items with price- and time-dependent demand rate and time-dependent deteriorating rate with partial backlogging is considered. The objective is to determine the optimal price, the replenishment time, and economic order quantity such that the total profit per unit time is maximized. After modeling the problem, an algorithm is proposed to solve the resulted problem. We also prove that the problem statement is concave function and the optimal solution is indeed global.

Weevils are a type of insect with elongated snouts coming from superfamily of Curculionoidea with approximately 97,000 species. Most of them consider pest and cause environmental damages but some kinds like wheat weevil, maize weevil, and boll weevils are famous to cause huge damage on crops, especially cereal grains. This research proposes a novel swarm-based metaheuristics algorithm called Weevil Damage Optimization Algorithm (WDOA) which mimics weevils’ fly power, snout power, and damage power on crops or agricultural products. The proposed algorithm is tested with 12 benchmark unimodal and multimodal artificial landscapes or optimization test functions. Additionally, the proposed WDOA is employed in five engineering problems to check its robustness for problem solving. Problems are Travelling Salesman Problem (TSP), n-Queens problem, portfolio problem, Optimal Inventory Control (OIC) problem, and Bin Packing Problem (BPP). All tests’ functions are compared with widely used benchmark algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS) algorithm, Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA), and Differential Evolution (DE) algorithm. Also, all problems are tested with DE, FA, and HS algorithms. The Proposed algorithm showed robustness and speed on all functions and problems by providing precision alongside with reasonable speed.

In today & apos; s global marketplace, individual firms do not compete as independent entities rather as an integral part of a supply chain. Uncertainty is the main attribute in managing the supply chains. Accordingly, we develop a (Q, R) inventory model with service level constraint and variable lead-time in fuzzy-stochastic environment. In addition, the triangular fuzzy numbers counts upon lead-time are used to construct fuzzy-stochastic lead-time demand. Using credibility criterion, the expected shortages are calculated. Without loss of generality, we assume that all the observed values of the fuzzy random variable, representing the demand are triangular fuzzy numbers. Consequently, the value of total expected cost in the fuzzy sense is derived using the expected value criterion or credibility criterion. To determine an optimal policy, a numerical technique is presented and the results are analyzed using scan and zoom for constraint optimization. Finally, in order to demonstrate the accuracy and effectiveness of the proposed model, numerical example and sensitivity analysis are also included.