Multi-objective Inventory Control Research Articles

Multi-objective continuous review inventory policy using MOPSO and TOPSIS methods

In the dynamic business environment of today, decision-making involves considering various conflicting aspects. Inventory planning problems aim to determine how much and when to order products to satisfy customer demand at the lowest possible cost while maintaining a desirable service level. These problems can be formulated as a MOPSO algorithm, which is used to handle multiple objectives in a continuous review stochastic inventory control system (r, Q). Unfortunately, most multi-objective inventory models have been solved by aggregating objectives using specific weights or by optimizing only one objective and treating the others as constraints. Considering the complexity of real-world inventory control problems, which involves conflicting objectives such as minimizing cost and maximizing service level, the need arises to employ more precise optimizers that can generate better and more diverse non-dominated solutions of reorder point and order size system. In this paper multi-criteria decision-making framework that combines MOPSO algorithm and TOPSIS method to generate a Pareto front of non-dominated solutions and rank them based on decision makers' preferences. Initially, the original MOPSO is applied to the multi-objective inventory control problem, and then the mutation operator is integrated into the MOPSO to maintain diversity in the swarm and explore the entire search space. Next, the leader selection strategy called the geographically-based system (Grids) is replaced by the crowding distance factor to choose the global optimal particle as a leader. Additionally, the ε-dominance concept is employed to limit the archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. In conclusion, this work not only pioneers a cutting-edge approach to multi objective inventory control but also underscores its practical value. By facilitating the generation of superior solutions that cater to diverse decision-maker preferences, our methodology resonates deeply with real-world challenges and sets a new benchmark for effective inventory planning.

Application of nature inspired algorithms for multi-objective inventory control scenarios

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.

A review of multi-objective inventory control problem

This paper presents a comprehensive review, summary, and discussion on multi objective inventory control problems and the solution approaches. Since the past decade, no seminal review paper has been published on multi objective inventory problems and the solution approaches. Therefore there is a requirement to present a review on the relevant field. In this review paper, current research practices in the relevant field and further research directions are suggested. This paper will be helpful for researchers and practitioners as; it is a single source of the relevant articles and helps to keep up to date on previously published research papers on multi objective inventory problems and the various solution approaches.

A review of multi-objective inventory control problem

A review of multi-objective inventory control problem

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

This paper investigates the inventory control of perishable products with a limited storage lifetime in closed-loop supply chains. Uncertainties related to customers' demands, the return rate of goods, and qualities of the returned products are considered. An efficient interactive response surface methodology is adopted by using a statistical simulation approach. The desirability function is taken into account a minimum desirability level of multiple objectives, the potential correlation between the considered objectives, and minimisation of uncontrollable variables or noise factors effect. The experimental results indicate the efficiency of the proposed simulation-based optimisation approach in handling correlated multiple objectives for inventory control of perishable products in closed-loop supply chains under uncertainty. Considered risks in the supply chain echelons and fair costs allocation has been balanced efficiently via using the proposed interactive approach to solving the multi-objective problem. Finally, the robustness of the obtained solutions is assessed under variation in weights of the response variables.

INTERACTIVE FUZZY PROGRAMMING MODEL IN MULTI-OBJECTIVE INVENTORY CONTROL PROBLEM USING VARIOUS OPERATORS

This paper deals with the interactive fuzzy programming approach for Multi Objective Inventory Control Problem (MOICP). In multi-objective optimization problem, objectives are often non-commensurable and cannot be combined into a single objective. Moreover, the objectives usually conflict with each other in that any improvement of one objective can be achieved only at the expense of another. In real world, all objectives of MOICP are not rigid. Some are rigid and some are fuzzy or all are imprecise. Fuzzy goals are defined by different membership functions through interaction with decision maker. By making the aspiration levels more flexible and by assigning different values to the normal weights to corresponding objectives functions, different solutions are determined to interact with the decision maker.

A Lagrangian Relaxation for a Fuzzy Random EPQ Problem with Shortages and Redundancy Allocation: Two Tuned Meta-heuristics

This paper develops an economic production quantity model for a multi-product multi-objective inventory control problem with fuzzy-stochastic demand and backorders. In this model, the annual demand is represented by trapezoidal fuzzy random numbers. The centroid defuzzification and the expected value methods are applied to defuzzify and make decisions in a random environment. In the case where the warehouse space is limited, the Lagrangian relaxation procedure is first employed to determine the optimal order and the maximum backorder quantities of the products such that the total inventory cost is minimized. The optimal solution obtained by the proposed approach is compared with that obtained by the traditional deterministic method. Moreover, a sensitivity analysis presents the rationality of the solution. Then, the model is extended to a multi-objective integer programming problem in which the optimal numbers of redundant production machines are determined to maximize the production system reliability. In the second proposed model, several constraints are considered to fit real-world situations. As the second model is developed for an NP-hard problem and hence cannot be solved using exact methods in a reasonable computational time, a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm-II (NSGA-II) is employed to provide Pareto front solutions. Due to non-availability of benchmark in the literature, another multi-objective evolutionary algorithm called non-dominated ranking genetic algorithm is implemented as well to validate the obtained results and evaluate the performance of NSGA-II. In addition, the Taguchi method is used to calibrate the parameters of both algorithms for better performance.

Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system

This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.

Evolutionary Pareto optimizers for continuous review stochastic inventory systems

Multi-objective inventory control has been studied for a long time. The trade-off analysis of cycle stock investment and workload, so called the exchange curve concept, possibly dates back to several decades ago. A classical way to such trade-off analysis is to utilize the Lagrangian relaxation technique or interactive method to search for the optimum in a sequence of single objective optimization problems. However, the field of optimization has been changed over the last few decades since the concept of evolutionary computation was introduced. In this paper, a continuous review stochastic inventory system with three objectives about cost and shortage is resolved by evolutionary computation in order to plan for the control policies under backordering and lost sales. Two evolutionary optimizers, multi-objective electromagnetism-like optimization (MOEMO) and multi-objective particle swarm optimization (MOPSO), are employed to well and fast approximate the non-dominated policies in term of lot size and safety stock. Trade-offs are observed in a non-dominated set that no one excels the others in all objectives. Computational results show that the evolutionary Pareto optimizers could generate trade-off solutions potentially ignored by the well-known simultaneous method. Comparisons between the results of backordering and lost sales indicate that decision makers will make more deliberate choices about lot sizing and safety stocking when unsatisfied demand is completely lost.

Multi-objective inventory control using electromagnetism-like meta-heuristic

Most of the works on multi-objective inventory control unify the various objectives into a single objective such that lead to a compromise solution whose non-dominance is not guaranteed. This paper presents an algorithm based on Electromagnetism-like Mechanism (EM) to solve a multi-objective inventory control problem with cost and shortage minimization objectives. EM is a new meta-heuristic originated from the electromagnetism theory in physics; it simulates attraction and repulsion of charged particles in order to move towards the optimum. A framework, so called Multi-Objective EM (MOEM), is proposed to approximate the well-known efficient solutions of order size and safety stock without using any surrogate measure (e.g. service level or shortage cost) and prior preference information from decision-makers. To give a specific compromise solution, any outranking method can be implemented to prioritize the non-dominated solutions for decision-makers. Finally, this could be the first attempt to apply EM to multi-objective inventory control, even the inventory control problems.