Location Assignment Problem Research Articles

Improving order-picking operation through efficient storage location assignment: A new approach

Fast development of online retail industry requires customer orders to be fulfilled within tight windows, where order-picking, the most time-consuming and labor-intensive activity in warehouses, plays an important role. One of the basic ways to improve order-picking operation is assigning storage locations to appropriate items. The storage location assignment problem is in general NP-hard and is mainly solved by heuristics which usually suffer from limited solution quality or high computational effort, especially for large scale problems. In literature, most studies make the storage assignment decisions according to item properties, such as turnover or correlation, which are statistically extracted from item orders. These storage methods follow a data→concept→assign decision mechanism and may ignore useful data characteristics that are not conceptualized. This paper presents a new approach to improve the order-picking operation, which directly uses item orders to make the decisions without any statistical treatments, i.e., following a data→assign mechanism. The concept of good move pair is introduced to quickly find a better assignment through directly exploiting data characteristics of item orders, and an iterative algorithm is developed to minimize the total travel distance. We evaluate the algorithm on real data and numerical instances, and compare its performance with extant methods in the literature. The results show that the proposed method significantly outperforms other methods in most cases. We also extend the algorithm to the case of high-level warehouses and examine its effectiveness.

The Outbound Containers’ Storage Location Assignment Problem for a Maritime Terminal

Seaway is important for supply chain management in order to be carried for manufactured products and logistics activities. However, this logistics process does not only consist of transportation. Storing transported goods temporarily or permanently in ports according to an appropriate plan is vital in order to whole process is carried on properly. This paper describes the storage location assignment problem for outbound containers in a maritime terminal and hybrid sequence stacking algorithm (HSSA) which is used to solve this problem. In this problem; yard bay consists of 6 horizontally and 4 vertically units long (6x4) totally 24 units of squares. 18 containers which are come in random weight and sequence are wanted to be placed into these slots optimal. Considering the fact that ships are loaded first with the heaviest containers and then with lighter containers; the presence of lightweight containers in the upper spaces necessitates a rehandle operation for the lightweight containers above to reach heavy containers. The main purpose of our method is to minimize the number of rehandle operations. For this purpose, according to the basic logic of HSSA algorithm; the heaviest containers are tried to be placed on the top and leftmost slots, on the other hand the lightest containers are tried to be placed on the bottom and rightmost slots as much as possible. However, the fact that incoming containers come at different weights in a different number and in a previously unknown sequence is the biggest obstacle to this method always achieving the best solution. At this point, although the method is a heuristic algorithm, it is important that it gives satisfactory results. In this study, a sample was solved by using HSSA and the results were presented in the study. These results show that the proposed algorithm is effective and efficient in solving the storage location assignment problem.

A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment.

Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

Particle Swarm Optimization Using Neighborhood-Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Automated Storage and Retrieval Systems (AS/RS) offer several advantages such as gaining space, reducing human errors, and completing more storing and retrieving tasks in less time to large size warehouses. Machines included multi-shuttle systems are designed to carry multiple loads in one cycle on the purpose of develop the performance. This paper deals with a quadruple command cycle location assignment method in unit-load dual-shuttle AS/RS. Also, it is shown how to solve location assignment problem using Particle Swarm Optimization (PSO) with the objective of shortest travel distance therewithal shortest travel time. Binary Coded Genetic Algorithm (BGA) and Real Coded Genetic Algorithm (RGA) are developed to validate result obtained. At the end, ten numerical examples for two scenarios designed small size and large size are presented to prove the implementation of the proposed method. Results showed that PSO accomplished to find optimal solution for shortest travel distance of AS/RS machine.

A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem

Efficiency and effectiveness of logistic activities, in general, and of distribution networks, in particular, are largely influenced by the way warehouses operate as nodes of these networks. In recent years, warehouse management has undergone major changes due to the increase of e-commerce and competition in time-reduction. Despite that, logistic costs of warehouse processes (e.g. receiving, storage, order picking and shipping, etc.) are still often high. Referring to the order picking process, related activities may be optimized by a proper assignment of products to storage locations. In the literature, this problem is known as Storage Location Assignment Problem (SLAP). Due to the multi-criteria nature of SLAP, a combined ELECTRE TRI and TOPSIS based approach under an uncertain environment is designed in the present paper. Aiming at properly dealing with the uncertainty of the decision process, an interval extension of both ELECTRE TRI and TOPSIS is proposed. Therefore, ELECTRE TRI is firstly used to assign products to shelf levels whereas TOPSIS is afterwards applied to determine their storage locations within every level rather than using a random assignment policy. The whole methodology has been designed in cooperation with a Sicilian company that provides logistic services all over the Italian territory.

New model of the storage location assignment problem considering demand correlation pattern

Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP). In existing studies, most storage assignment methods only consider the properties of individual item rather than the item groups that are usually collectively required. This paper introduces the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the SLAP. The model is subsequently reduced using the S-shape routing strategy, and a method for determining DCPs from historical data is proposed. To solve the model, a heuristic and a simulated annealing method are developed. The proposed methods are examined and compared extant methods using both real data collected from an online retailer and numerical instances that are randomly generated. The computational results are discussed.

A Bilevel Multiobjective Optimisation Approach for Solving the Evacuation Location Assignment Problem

In this paper, a bilevel multiobjective optimisation model is proposed to solve the evacuation location assignment problem. The model incorporates the two decision‐makers’ spaces, namely, urban planners and evacuees. In order to solve the proposed problem, it is first reformulated into a single‐level problem using the Karush–Kuhn–Tucker conditions. Next, the problem is linearised into a mixed‐integer linear programming model and solved using an off‐the‐shelf solver. A case study is examined to showcase the applicability of the proposed model, which is solved using single‐objective and multiobjective lexicographic optimisation approaches. The model provides planners with an ability to determine the best locations for placement of shelters in such a way that the evacuees’ traffic assignment on the existing network is optimised.

The storage location assignment problem: A literature review

The storage location assignment problem: A literature review

A study of the sensitivity of sequence stacking strategies for the storage location assignment problem for out-bound containers in a maritime terminal

Recently, a new approach to solve the outbound container location problem motivated by the Shanghai maritime terminals was published. This approach addresses the problem by decomposing it into two stages. The first stage is a mathematical programming model and the second stage is a heuristic algorithm whose objective is to minimize the total reshuffle movements executed during the loading operation. That study reports that the diagonal stacking configuration provides the best performance. Conversely, this paper documents the results of a study to observe the performance of the various staking strategies under different conditions that can occur regularly in real ports. The parameters that are varied are the number of tiers in each stack, the number of container weight levels, and the permitted bay utilization. The results show that although the diagonal stacking strategy does provide the best performance under some conditions, it does not yield the best performance in other parameter combinations. The horizontal strategy is found to perform best for most of the parameter combinations considered in this study.

Bi-objective dependent location quadratic assignment problem: Formulation and solution using a modified artificial bee colony algorithm

Combinatorial optimization problems arise from various real life situations and the quadratic assignment problem (QAP) to model a facility layout problem or a plant location problem is such an example. While examining the facility layout of a semi-automated bus body manufacturing unit, a bi-objective facility layout optimization problem is identified in which the solution space of the second objective function depends and changes upon the feasible solutions of the first objective function. In this paper, the said problem is first defined in the form of a bi-objective quadratic dependent location assignment problem (bi-d-QAP), a heuristic solution approach is then provided, and finally, a modified artificial bee colony algorithm is proposed while combining both the genetic and neighborhood search algorithms to solve the considered bi-d-QAP. The data obtained from the above-mentioned manufacturing unit are utilized to show how the proposed algorithm performs better in comparison to some of the popular state-of-the-art optimization algorithms.

Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import

Maritime terminals need more efficiency in their handling operations due to the phenomenal evolution of world container traffic, and to the increase of the container ship capacity. In this work, we propose a new integrated modeling considering the optimization of maritime container terminals using straddle carriers. The problem is considered at import. We study a combination between two known problems, the first is the storage location assignment problem, and the second is the straddle carrier scheduling problem. This approach, which combines between two chronologically successive problems, leads to the use of multi‐objective optimization. In fact, we study the multi‐objective integrated problem of location assignment and Straddle carrier Scheduling (IPLASS) in maritime container terminal at import. We prove that the problem is NP‐Complete. The objective is to minimize the operating cost which we evaluate according to eight components: the date of last task called makespan, the total vehicle operating time, the total storage bay occupation time, the number of vehicles used, the number of storage bays used, the number of storage locations used, and two different costs of storage location assignment. The location assignment costs are evaluated in order to facilitate the containers transfer for deliveries. We assume that the operating cost is a function of these components and that the influence of each component is variable and dependent on different parameters. These parameters are essentially: the number of quays in the terminal, the straddle carrier traffic layout, the number of container ships to serve in the terminal, the influence of concurrent operations in the terminal, the storage space configuration, the number of free storage bays, the number of free straddle carriers, the number of free quay cranes, the mobility of quay cranes, etc. To solve IPLASS efficiently, we propose an adapted multi‐objective Tabu Search algorithm. Lower‐bound evaluations are introduced to perform approximation of Pareto Front. To explore efficiently the non‐convex Pareto Front Region, we evaluate also a maximized distance adapted to the set of objectives. Indicators of efficiency are developed to propose distinguished solutions to operator. 2D‐projections of approximated Pareto Frontier are given to more understand the efficiency of proposed solutions.

MIP models and a hybrid method for the capacitated air-cargo network planning and scheduling problems

This paper examines hub location and plane assignment problems for the air-cargo delivery service. Two mixed integer programming models are constructed. Their difference lies in the way they control the number of visiting hubs for serving each O-D pair. Due to the NP-hardness of the problem, a two-stage hybrid algorithm is developed to solve large-scale instances. The first stage settles partial critical variables by heuristics and the second stage settles the rest of variables by a commercial solver. Based on the real-life data, numerical experiments are conducted to test the performance of the models and algorithm.

A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems.

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.

A decomposition approach to dual shuttle automated storage and retrieval systems

Automated Storage and Retrieval Systems (AS/RS) have become vital in today’s distribution and production environments, however it remains necessary to equip them with more efficient operational control policies. Motivated by real situations encountered by companies employing AS/RS, the present paper studies a miniload AS/RS system, with a dual shuttle crane in which a set of storage and retrieval requests must be scheduled such that the prioritized waiting time is minimized. Dual shuttle cranes have received minimal academic attention and thus continue to pose new problems that must be solved. The miniload AS/RS problem is addressed by decomposing it into a location assignment and sequencing problem. Different heuristic strategies are introduced for making the assignments, while a general mathematical model and efficient branch and bound procedure are proposed for optimizing the sequence. Additionally, a fast metaheuristic capable of solving larger instances is also developed. A set of real-world based benchmarks with varying characteristics is generated to evaluate the proposed methods. Very small instances prove the only for which optimal sequences are found in reasonable calculation time. Experimental results demonstrate the effectiveness of the heuristic decomposition method.

Multi-criteria approach for products classification and their storage location assignment

Even small improvements in storage operations can bring significant benefits in customer response time, increasing the level of service offered. However, a company cannot offer high level of service for all products. In this sense, one of the ways used to optimize the activities for order picking is the study of storage location assignment problem (SLAP). Thus, this paper proposed a multi-criteria decision model to perform the products classification and to solve the SLAP in a multi-layer warehouse. The ELECTRE TRI method is used to define the shelf level for each product, and the ELECTRE III method is used to establish a fixed location for each product on the shelf it belongs to. The great advantage of this model is that there is not trade-off among the criteria, and it is able to consider several criteria simultaneously. However, the main goal is not optimization, but the search for a storage operations performance balance, considering multiple criteria, allowing for a better inventory management when considering profitability criteria, for example, and improvement in the order picking operations, through the demand and space required criteria. In addition, it considers the preferences of the decision maker.

Constrained clustering method for class-based storage location assignment in warehouse

Purpose – Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints. Design/methodology/approach – In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI. Findings – The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent. Practical implications – While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is. Originality/value – The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

A cloud-based location assignment system for packaged food allocation in e-fulfillment warehouse

E-retailing is the delivery of products and services over the Internet, and it has been growing rapidly in recent years. Logistics services in the related warehouses have to be enhanced accordingly in order to handle the huge amount of e-orders in a shorter timeframe. Moreover, since the e-retailing of packaged food has been getting popular, these warehouses inevitably have to handle food products requiring special care, including temperature, humidity and insolation control, and expiry date management. Consequently, suitable storage guidelines have to be formulated for these warehouses to cope with their more demanding daily operations. A cloud-based location assignment system (CLAS) is thus proposed in this article, which provides timely and comprehensive solutions to the storage location assignment problem for warehouse operators. CLAS applies (i) cloud-based infrastructure for enhancing the agility of the proposed system, (ii) fuzzy association rule mining for predicting the potential storage time of various types of packaged food, and (iii) fuzzy logic and association rule mining for offering the most preferred storage locations of various products. A prototype of the CLAS is tested in a warehouse which involves packaged food handling. The test results show that the operational efficiency measured in terms of storage assignment decision-making time and order-picking time and the quality of the storage assignment solutions measured in terms of product disposal rate and product return/exchange rate have been improved in the case company.

Metaheuristics for Warehouse Storage Location Assignment Problems

Metaheuristics for Warehouse Storage Location Assignment Problems

Heuristic algorithms for a storage location assignment problem in a chaotic warehouse

The extensive application of emerging technologies is revolutionizing warehouse management. These technologies facilitate working with complex and powerful warehouse management models in which products do not have assigned fixed locations (random storage). Random storage allows the utilization of the available space to be optimized. In this context, and motivated by a real problem, this article presents a model that looks for the optimal allocation of goods in order to maximize the storage space availability within the restrictions of the warehouse. For the proposed model a construction method, a local search algorithm and different metaheuristics have been developed. The introduced algorithms can also be used for other purposes such as to assess when and how it is convenient to perform relocation of stored items to improve the current level of storage space availability. Computational tests performed on a set of randomly generated and real warehouse instances show the effectiveness of the proposed methods.