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This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Project managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.

It employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.

The study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.

The research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.

The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.

Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.

Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Assembly line is a common production form and has been effectively used in many industries, but the imprecise processing time of each process makes production line balancing and capacity forecasting the most troublesome problems for production managers. In this paper, uncertain man-hours are represented as interval grey numbers, and the optimization problem of production line balance in the case of interval grey man-hours is studied to better evaluate the production line capacity.

First, this paper constructs the basic model of assembly line balance optimization for the single-product scenario, and on this basis constructs an assembly line balance optimization model under the multi-product scenario with the objective function of maximizing the weighted greyscale production line balance rate, second, this paper designs a simulated annealing algorithm to solve problem. A neighborhood search strategy is proposed, based on assembly line balance optimization, an assembly line capacity evaluation method with interval grey man-hour characteristics is designed.

This paper provides a production line balance optimization scheme with uncertain processing time for multi-product scenarios and designs a capacity evaluation method to provide managers with scientific management strategies so that decision-makers can scientifically solve the problems that the company's design production line is quite different from the actual production situation.

There are few literary studies on combining interval grey number with assembly line balance optimization. Therefore, this paper makes an important contribution in this regard.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.