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In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services.

The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution.

Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study.

The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.

This paper aims to optimize the interactions of businesses located within industrial clusters (ICs) by using a supply-demand hub in ICs (SDHIC) as a conjoint provider of logistics and depository facilities for small- and medium-sized enterprises (SMEs) as producers, where all of these interactions are under supervision of a third-party logistics provider (3PL).

To evaluate the values of SDHIC, three mathematical models are proposed, optimally solved via GAMS and then compared. Also, a “linear relaxation-based heuristic” procedure is proposed to yield a feasible initial solution within a significant shorter computational time. To illustrate the values of SDHIC, comprehensive calculations over a case study and generated sets of instances are conducted, including several sensitivity analysis.

The experimental results demonstrate the efficiency of SDHIC for SMEs via combining batches and integrating the holding space of inventories, while the outcomes of the case study are aligned with those obtained from random sample examples, which confirms the trueness of used parameters and reveals the applicability of using SDHIC in real world. Finally, several interesting managerial implications for practitioners are extracted and presented.

Some of the managerial and practical implications are optimizing interactions of businesses involved in a supply chain of an IC containing some customers, suppliers and manufacturers and rectifying the present noteworthy gaps pertaining to the previously published research via using real assumptions and merging upstream and downstream of the supply chain through centralizing on storage of raw materials (supply echelon) and finished products (demand echelon) at the same place simultaneously to challenge a classic concept in which supply and demand echelons were being separately planned regarding their inventory management and logistics activities and showing the positive consequences of such challenge, showing the performance improvement of the proposed model compared to the classic model, by increasing the storing cost of raw materials and finished products, considering some disadvantages of using SDHIC and showing the usefulness of SDHIC in total, presenting some applied findings according to the obtained results of sensitivity analysis.

The key contributions of this paper to the literature are suggesting a new applied mathematical methodology to the supply chain (SC) of ICs by means of a conjoint provider of warehousing activities called SDHIC, comparing the new proposed model with the two classic ones and showing the proposed model’s dominancy, showing the helpful outcomes of collaborating 3PL with the SMEs in a cluster, proposing a “linear relaxation-based heuristic” procedure to yield a feasible initial solution within a significant shorter computational time and minimizing total supply chain costs of such IC by optimum application of facilities, lands and labor.